Exploratory analysis of principal sensory trigeminal nucleus and ventrobasal thalamus

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Last updated: 2023-03-04

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Knit directory: Hevesi_2023/

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File	Version	Author	Date	Message
html	8bf2341	Evgenii O. Tretiakov	2023-03-04	Build site.
Rmd	bfa7d1f	Evgenii O. Tretiakov	2023-03-04	workflowr::wflow_publish("analysis/eda.Rmd", all = TRUE, update = TRUE,
html	3b19c24	Evgenii O. Tretiakov	2023-02-02	Build site.
Rmd	ddf7471	Evgenii O. Tretiakov	2023-02-02	workflowr::wflow_publish("analysis/eda.Rmd", all = TRUE, update = TRUE,
Rmd	b124415	Evgenii O. Tretiakov	2023-01-26	improve qc
Rmd	321af0c	Evgenii O. Tretiakov	2023-01-25	fix original labels and adjust visualisations
html	c82f5c0	Evgenii O. Tretiakov	2023-01-25	Build site.
Rmd	2d7093f	Evgenii O. Tretiakov	2023-01-24	workflowr::wflow_publish("analysis/eda.Rmd", all = TRUE, verbose = TRUE)
Rmd	45efe1a	Evgenii O. Tretiakov	2023-01-24	workflowr::wflow_publish("analysis/eda.Rmd", all = TRUE, verbose = TRUE)
Rmd	52ce403	Evgenii O. Tretiakov	2023-01-24	workflowr::wflow_publish("analysis/eda.Rmd", all = TRUE, verbose = TRUE)

---

Combined analysis of scRNA-seq datasets derived from the same experiment

Since it was derived from the same two pooled mice we might try to merge them together.

souporcell_THP7 <-
  read_tsv(here("souporcell/THP7", "clusters.tsv")) %>%
  mutate(origin = "THP7",
         cell_name = str_c("THP7_", barcode))
souporcell_Pr5P7 <-
  read_tsv(here("souporcell/Pr5P7", "clusters.tsv")) %>%
  mutate(origin = "Pr5P7",
         cell_name = str_c("Pr5P7_", barcode))
souporcell <-
  bind_rows(
    souporcell_THP7,
    souporcell_Pr5P7)

souporcell %>%
  janitor::tabyl(status, assignment, origin)

$Pr5P7
     status   0 0/1   1 1/0
    singlet 294   0 321   0
 unassigned  37   9  65   5

$THP7
     status   0 0/1   1 1/0
    singlet 492   0 318   0
 unassigned  70   5  29   9

cell_bender_merged <-
  Read_CellBender_h5_Multi_Directory(
    base_path = here("cellbender"),
    custom_name = "_output_filtered.h5",
    sample_names = sort(c("THP7", "Pr5P7")), # must be sorted as the function internally doesn't index output of list.dir so can't reorder or subset
    merge = TRUE)

cell_ranger_merged <-
  Read10X_h5_Multi_Directory(
    base_path = here("cellranger"),
    default_10X_path = TRUE,
    h5_filename = "filtered_feature_bc_matrix.h5",
    merge = TRUE,
    sample_names = sort(c("THP7", "Pr5P7")), # must be sorted as the function internally doesn't index output of list.dir so can't reorder or subset
    parallel = TRUE, 
    num_cores = 16)

combined.srt <-
  Create_CellBender_Merged_Seurat(
    raw_cell_bender_matrix = cell_bender_merged,
    raw_counts_matrix = cell_ranger_merged,
    raw_assay_name = "RAW")

After running troublet (genotyping-based detection of doublets), it seems that we don’t have much of doublets.

Elimination of ambient RNA

	orig.ident	nCount_RNA	nFeature_RNA	nCount_RAW	nFeature_RAW	nFeature_Diff	nCount_Diff
Pr5P7_AAACCCAAGCTGACAG-1	Pr5P7	5526	2614	5542	2615	1	16
Pr5P7_AAACCCAGTCTTGTCC-1	Pr5P7	6876	2954	6898	2954	0	22
Pr5P7_AAACGAAGTGTTCCTC-1	Pr5P7	12076	4603	12110	4604	1	34
Pr5P7_AAACGCTTCCCTCGAT-1	Pr5P7	10009	3885	10046	3890	5	37
Pr5P7_AAAGGATGTTGCATGT-1	Pr5P7	3277	1840	3283	1840	0	6

orig.ident	Median_nCount_RNA	Median_nFeature_RNA	Median_nCount_Diff	Median_nFeature_Diff
Pr5P7	5142	2481	27	3
THP7	8434	3476	89	14
Totals (All Cells)	6489	2902	50	9

	Raw_Counts	CellBender_Counts	Count_Diff	Pct_Diff
1700054A03Rik	357	134	223	62.46499
Mt2	148	73	75	50.67568
Gm50306	28	14	14	50.00000
Hist2h2bb	9	5	4	44.44444
Ly6a	12	7	5	41.66667

Plot feature differences

In addition to returning the data.frame it can be useful to visually examine the changes/trends after running CellBender.

Past versions of unnamed-chunk-4-1.png

Version	Author	Date
c82f5c0	Evgenii O. Tretiakov	2023-01-25

sex_genes <-
  str_to_title(c('EHD2', 'ESPL1', 'JARID1D', 'PNPLA4',
                 'RPS4Y1', 'XIST','tsix', 'Eif2s3y',
                 'Ddx3y', 'Uty', 'Kdm5d')) %>% .[. %in% rownames(combined.srt)]
stress_genes <-
  str_to_title(c('Rpl26','Gstp1','Rpl35a','Erh',
                 'Slc25a5','Pgk1','Eno1',
                 'Tubb2a','Emc4','Scg5')) %>% .[. %in% rownames(combined.srt)]

combined.srt <-
  Store_Palette_Seurat(
    seurat_object = combined.srt,
    palette = rev(brewer.pal(n = 11, name = "RdYlGn")),
    palette_name = "mdat_Colour_Pal")
combined.srt <-
  Store_Palette_Seurat(
    seurat_object = combined.srt,
    palette = rev(brewer.pal(n = 11, name = "Spectral")),
    palette_name = "expr_Colour_Pal")

low.cutoff.gene          <- 500
high.cutoff.gene         <- NULL
high.cutoff.gene         <- Inf
low.cutoff.umis          <- NULL
low.cutoff.umis          <- -Inf
high.cutoff.umis         <- 45000
high.cutoff.pc.mt        <- 1
high.cutoff.pc.ribo      <- 1
high.cutoff.pc.hb        <- 0.5
high.cutoff.logprob.dupl <- NULL
high.cutoff.complexity   <- 0.8

combined.srt <-
  Add_Mito_Ribo_Seurat(combined.srt, species = "mouse")
combined.srt[["percent_hb"]] <- PercentageFeatureSet(combined.srt, pattern = "^Hb[^(p)]")
combined.srt <-
  Add_Cell_Complexity_Seurat(combined.srt)

# Visualize QC metrics as a violin plot
p1 <-
  QC_Plots_Complexity(
    combined.srt,
    high_cutoff = high.cutoff.complexity,
    color_seed = reseed)
p2 <-
  QC_Plots_Genes(
    combined.srt,
    low_cutoff = low.cutoff.gene,
    high_cutoff = high.cutoff.gene,
    plot_title = "Genes per Nucleus",
    color_seed = reseed,
    ggplot_default_colors = T
  )
p3 <-
  QC_Plots_UMIs(
    combined.srt,
    low_cutoff = low.cutoff.umis,
    high_cutoff = high.cutoff.umis,
    plot_title = "UMIs per Nucleus",
    color_seed = reseed,
    ggplot_default_colors = T
  )
p4 <-
  QC_Plots_Mito(
    combined.srt,
    high_cutoff = high.cutoff.pc.mt,
    plot_title = "Mito genes % per Nucleus",
    color_seed = reseed,
    ggplot_default_colors = T
  )
p5 <-
  QC_Plots_Feature(
    combined.srt,
    feature = "percent_ribo",
    high_cutoff = high.cutoff.pc.ribo,
    y_axis_label = "% Ribosomal Genes Counts",
    plot_title = "Ribo genes % per Nucleus",
    color_seed = reseed,
    ggplot_default_colors = T
  )
p6 <-
  QC_Plots_Feature(
    combined.srt,
    feature = "percent_hb",
    high_cutoff = high.cutoff.pc.hb,
    y_axis_label = "% Hemoglobin Genes Counts",
    plot_title = "Hemoglobin genes % per Nucleus",
    color_seed = reseed,
    ggplot_default_colors = T
  )

wrap_plots(p1, p2, p3, p4, p5, p6, ncol = 3)

Past versions of pl-vln-qc-1.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02
c82f5c0	Evgenii O. Tretiakov	2023-01-25

plot1 <- QC_Plot_GenevsFeature(seurat_object = combined.srt, feature1 = "percent_mito", low_cutoff_gene = low.cutoff.gene, high_cutoff_gene = high.cutoff.gene, high_cutoff_feature = high.cutoff.pc.mt, color_seed = reseed, ggplot_default_colors = T, pt.size = 4, shuffle_seed = reseed) & scale_y_log10()
plot2 <- QC_Plot_UMIvsGene(seurat_object = combined.srt, low_cutoff_gene = low.cutoff.gene, high_cutoff_gene = high.cutoff.gene, low_cutoff_UMI = low.cutoff.umis, high_cutoff_UMI = high.cutoff.umis, color_seed = reseed, ggplot_default_colors = T, pt.size = 4, shuffle_seed = reseed) & scale_x_log10() & scale_y_log10()
plot3 <- QC_Plot_GenevsFeature(seurat_object = combined.srt, feature1 = "percent_ribo", low_cutoff_gene = low.cutoff.gene, high_cutoff_gene = high.cutoff.gene, high_cutoff_feature = high.cutoff.pc.ribo, color_seed = reseed, ggplot_default_colors = T, pt.size = 4, shuffle_seed = reseed) & scale_y_log10()
plot4 <- FeatureScatter(combined.srt, feature1 = "percent_ribo", feature2 = "percent_mito", shuffle = T, pt.size = 4, seed = reseed)
(plot1 + plot2) / (plot3 + plot4)

Past versions of pl-scatter-qc-1.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02
c82f5c0	Evgenii O. Tretiakov	2023-01-25

QC_Plot_UMIvsGene(seurat_object = combined.srt,
                  meta_gradient_name = "percent_mito",
                  low_cutoff_gene = low.cutoff.gene,
                  high_cutoff_gene = high.cutoff.gene,
                  high_cutoff_UMI = high.cutoff.umis,
                  meta_gradient_low_cutoff = high.cutoff.pc.mt,
                  meta_gradient_color = combined.srt@misc$mdat_Colour_Pal,
                  combination = TRUE,
                  color_seed = reseed,
                  ggplot_default_colors = TRUE,
                  pt.size = 4,
                  shuffle_seed = reseed) & 
  scale_x_log10() & scale_y_log10()

Past versions of pl-scatter-qc-comb-1.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02

Past versions of pl-scatter-doublets-log-prob-1.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02

combined.srt$QC <-
  ifelse(
    combined.srt@meta.data$log10GenesPerUMI < high.cutoff.complexity &
      combined.srt@meta.data$QC == 'Pass',
    'Low_Complexity',
    combined.srt@meta.data$QC
  )
combined.srt$QC <-
  ifelse(
    combined.srt@meta.data$log10GenesPerUMI < high.cutoff.complexity &
      combined.srt@meta.data$QC != 'Pass' &
      combined.srt@meta.data$QC != 'Low_Complexity',
    paste('Low_Complexity', combined.srt@meta.data$QC, sep = ','),
    combined.srt@meta.data$QC
  )
combined.srt$QC <-
  ifelse(
    combined.srt@meta.data$nFeature_RNA < low.cutoff.gene &
      combined.srt@meta.data$QC == 'Pass',
    'Low_nFeature',
    combined.srt@meta.data$QC
  )
combined.srt$QC <-
  ifelse(
    combined.srt@meta.data$nFeature_RNA < low.cutoff.gene &
      combined.srt@meta.data$QC != 'Pass' &
      combined.srt@meta.data$QC != 'Low_nFeature',
    paste('Low_nFeature', combined.srt@meta.data$QC, sep = ','),
    combined.srt@meta.data$QC
  )
combined.srt$QC <-
  ifelse(
    combined.srt@meta.data$percent_mito > high.cutoff.pc.mt &
      combined.srt@meta.data$QC == 'Pass',
    'High_MT',
    combined.srt@meta.data$QC
  )
combined.srt$QC <-
  ifelse(
    combined.srt@meta.data$percent_mito > high.cutoff.pc.mt &
      combined.srt@meta.data$QC != 'Pass' &
      combined.srt@meta.data$QC != 'High_MT',
    paste('High_MT', combined.srt@meta.data$QC, sep = ','),
    combined.srt@meta.data$QC
  )
combined.srt$QC <-
  ifelse(
    combined.srt@meta.data$nCount_RNA > high.cutoff.umis &
      combined.srt@meta.data$QC == 'Pass',
    'High_UMIs',
    combined.srt@meta.data$QC
  )
combined.srt$QC <-
  ifelse(
    combined.srt@meta.data$nCount_RNA > high.cutoff.umis &
      combined.srt@meta.data$QC != 'Pass' &
      combined.srt@meta.data$QC != 'High_UMIs',
    paste('High_UMIs', combined.srt@meta.data$QC, sep = ','),
    combined.srt@meta.data$QC
  )
combined.srt$QC <-
  ifelse(
    combined.srt@meta.data$percent_ribo > high.cutoff.pc.ribo &
      combined.srt@meta.data$QC == 'Pass',
    'High_Ribo',
    combined.srt@meta.data$QC
  )
combined.srt$QC <-
  ifelse(
    combined.srt@meta.data$percent_ribo > high.cutoff.pc.ribo &
      combined.srt@meta.data$QC != 'Pass' &
      combined.srt@meta.data$QC != 'High_Ribo',
    paste('High_Ribo', combined.srt@meta.data$QC, sep = ','),
    combined.srt@meta.data$QC
  )
combined.srt$QC <-
  ifelse(
    combined.srt@meta.data$percent_hb > high.cutoff.pc.hb &
      combined.srt@meta.data$QC == 'Pass',
    'High_Hgb',
    combined.srt@meta.data$QC
  )
combined.srt$QC <-
  ifelse(
    combined.srt@meta.data$percent_hb > high.cutoff.pc.hb &
      combined.srt@meta.data$QC != 'Pass' &
      combined.srt@meta.data$QC != 'High_Hgb',
    paste('High_Hgb', combined.srt@meta.data$QC, sep = ','),
    combined.srt@meta.data$QC
  )
table(combined.srt$QC)

                                     High_Hgb 
                                            4 
                           High_Hgb,High_Ribo 
                                            1 
                                      High_MT 
                                           15 
                         High_MT,Low_nFeature 
                                            4 
          High_MT,Low_nFeature,Low_Complexity 
                                            1 
                                    High_Ribo 
                                           61 
                            High_Ribo,High_MT 
                                           33 
               High_Ribo,High_MT,Low_nFeature 
                                           13 
High_Ribo,High_MT,Low_nFeature,Low_Complexity 
                                            1 
                                    High_UMIs 
                                           35 
                                 Low_nFeature 
                                            3 
                                         Pass 
                                         1316 

# Visualize QC metrics as a violin plot again after subset
combined.subset.srt <- combined.srt
combined.subset.srt <- subset(combined.subset.srt, subset = QC == "Pass")
p1 <-
  QC_Plots_Complexity(
    seurat_object = combined.subset.srt,
    color_seed = reseed,
    ggplot_default_colors = T)
p2 <-
  QC_Plots_Genes(
    seurat_object = combined.subset.srt,
    low_cutoff = low.cutoff.gene,
    high_cutoff = high.cutoff.gene,
    plot_title = "Genes per Nucleus",
    color_seed = reseed,
    ggplot_default_colors = T
  )
p3 <-
  QC_Plots_UMIs(
    seurat_object = combined.subset.srt,
    low_cutoff = low.cutoff.umis,
    high_cutoff = high.cutoff.umis,
    plot_title = "UMIs per Nucleus",
    color_seed = reseed,
    ggplot_default_colors = T
  )
p4 <-
  QC_Plots_Mito(
    seurat_object = combined.subset.srt,
    high_cutoff = high.cutoff.pc.mt,
    plot_title = "Mito genes % per Nucleus",
    color_seed = reseed,
    ggplot_default_colors = T
  )
p5 <-
  QC_Plots_Feature(
    seurat_object = combined.subset.srt,
    feature = "percent_ribo",
    high_cutoff = high.cutoff.pc.ribo,
    y_axis_label = "% Ribosomal Genes Counts",
    plot_title = "Ribo genes % per Nucleus",
    color_seed = reseed,
    ggplot_default_colors = T
  )
p6 <-
  QC_Plots_Feature(
    seurat_object = combined.subset.srt,
    feature = "percent_hb",
    high_cutoff = high.cutoff.pc.hb,
    y_axis_label = "% Hemoglobin Genes Counts",
    plot_title = "Hemoglobin genes % per Nucleus",
    color_seed = reseed,
    ggplot_default_colors = T
  )

wrap_plots(p1, p2, p3, p4, p5, p6, ncol = 3)

Past versions of pl-vln-qc-subset-1.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02

combined.srt <- NormalizeData(combined.srt)
combined.srt <- 
  FindVariableFeatures(
    combined.srt, 
    selection.method = "vst",
    nfeatures = 3000)
top100 <- head(VariableFeatures(combined.srt), 100)
plot5 <- VariableFeaturePlot(combined.srt)
LabelPoints(plot = plot5, points = top100, repel = TRUE, xnudge = 0, ynudge = 0)

Past versions of pl-init-dr-1.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02

all.genes <- rownames(combined.srt)
hvg <- VariableFeatures(combined.srt)
var_regex <- '^Hla-|^Ig[hjkl]|^Rna|^mt-|^Rp[sl]|^Hb[^(p)]|^Gm'
hvg <- hvg[str_detect(pattern = var_regex, string = hvg, negate = T)]
combined.srt[["var_regex"]] <-
  PercentageFeatureSet(combined.srt, pattern = var_regex)
combined.srt <- ScaleData(combined.srt,
                          features = all.genes,
                          vars.to.regress = c("log10GenesPerUMI"))
npcs <-  30
combined.srt <- RunPCA(combined.srt, 
               features = hvg,
               npcs = npcs,
               seed.use = reseed,
               verbose = TRUE)
VizDimLoadings(combined.srt, dims = 1:9, reduction = "pca") & 
  theme(axis.text = element_text(size = 5),
        axis.title = element_text(size = 8, face = "bold"))

Past versions of pl-init-dr-2.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02

DimHeatmap(combined.srt, dims = 1:9, nfeatures = 20, cells = 500, balanced = T)

Past versions of pl-init-dr-3.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02

DimPlot_scCustom(combined.srt, reduction = "pca", color_seed = reseed, ggplot_default_colors = T, pt.size = 3)

Past versions of pl-init-dr-4.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02

ElbowPlot(combined.srt)

Past versions of pl-init-dr-5.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02

combined.srt <-
  JackStraw(
    object = combined.srt,
    assay = "RNA",
    reduction = "pca",
    dims = npcs,
    num.replicate = 100,
    prop.freq = 0.02,
    maxit = 1000)
combined.srt <-
  ScoreJackStraw(combined.srt,
                 dims = seq_along(combined.srt[["pca"]]@stdev))
JackStrawPlot(combined.srt, dims = seq_along(combined.srt[["pca"]]@stdev))

Past versions of pl-init-dr-6.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02

test_pc <- 
  PCScore(object = combined.srt,
           PCs = seq_along(combined.srt[["pca"]]@stdev),
           score.thresh = 1e-05)
selected_pcs <-
  seq_along(
    combined.srt[["pca"]]@stdev
    )[test_pc$Score <= 1e-03 & 
        combined.srt[["pca"]]@stdev > quantile(combined.srt[["pca"]]@stdev, .25)]
selected_pcs

 [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22

combined.srt <-
  combined.srt |>
  FindNeighbors(
    dims = selected_pcs,
    k.param = 15,
    annoy.metric = "euclidean",
    n.trees = 100,
    verbose = FALSE) |>
  RunUMAP(
    dims = selected_pcs,
    reduction.name = "umap",
    reduction.key = "UMAP_",
    return.model = FALSE,
    umap.method = "umap-learn",
    densmap = TRUE,
    dens.lambda = 1L,
    dens.frac = 0.3,
    n.epochs = 1000L,
    n.neighbors = 15L,
    min.dist = 0.01,
    spread = 2L,
    metric = "correlation",
    init = "pca",
    seed.use = reseed,
    verbose = FALSE)

metadata <- combined.srt@meta.data
rownames(metadata) <- colnames(combined.srt)
ref.labels <- metadata$orig.ident

resolutions <-
  modularity_event_sampling(
    A = combined.srt@graphs$RNA_snn,
    n.res = 20,
    gamma.min = 0.1,
    gamma.max = 3.000001
  ) # sample based on the similarity matrix

# clustering using Suerat
combined.srt <- combined.srt |> 
  FindClusters(algorithm = 4, method = "igraph",
               resolution = resolutions, random.seed = reseed,
               verbose = FALSE)

# initial cluster tree from Seurat flat clustering
plot_clustree(
  labelmat = combined.srt@meta.data,
  prefix = 'RNA_snn_res.',
  ref.labels = ref.labels,
  plot.ref = FALSE
)

Past versions of pl-init-dr-7.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02

out <-  mrtree(
  combined.srt,
  prefix = 'RNA_snn_res.',
  n.cores = n_cores,
  consensus = FALSE,
  sample.weighted = TRUE,
  augment.path = FALSE,
  verbose = FALSE
)

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# if there are few partitions per k, within resolution consensus step can speed up the algorithm
# weight per sample is encoraged if the classes are imbalanced

plot_tree(
  labelmat = out$labelmat.mrtree,
  ref.labels = ref.labels,
  plot.piechart = TRUE,
  node.size = 0.2,
  tip.label.dist = 10,
  bottom.margin = 30
)

Past versions of pl-mrtree-init-1.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02

# Adjusted Multiresolution Rand Index (AMRI)
ks.flat <-  apply(
  out$labelmat.flat,
  2,
  FUN = function(x)
    length(unique(x))
)
ks.mrtree <-  apply(
  out$labelmat.mrtree,
  2,
  FUN = function(x)
    length(unique(x))
)
amri.flat <-  sapply(1:ncol(out$labelmat.flat), function(i)
  AMRI(out$labelmat.flat[, i], ref.labels)$amri)
amri.flat <-  aggregate(amri.flat, by = list(k = ks.flat), FUN = mean)
amri.recon <-  sapply(1:ncol(out$labelmat.mrtree), function(i)
  AMRI(out$labelmat.mrtree[, i], ref.labels)$amri)

df <-  rbind(
  data.frame(
    k = amri.flat$k,
    amri = amri.flat$x,
    method = 'Seurat flat'
  ),
  data.frame(k = ks.mrtree, amri = amri.recon, method = 'MRtree')
)
ggplot2::ggplot(data = df, aes(x = k, y = amri, color = method)) + geom_line() + theme_bw()

Past versions of pl-mrtree-init-2.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02

stab.out <- stability_plot(out)
stab.out$plot

Past versions of pl-mrtree-init-3.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02

kable_material(
  kable(
    stab.out$df,
    "html"),
  bootstrap_options = c("bordered",
                          "condensed",
                          "responsive",
                          "striped"),
  position = "left",
  font_size = 14
)

resolution	ari
7	0.9462109
8	0.6936598
9	0.7479854
10	0.6381063
11	0.7536902
12	0.9974854
13	0.9835726
14	0.9910735
15	0.9792653
16	0.9539976
17	0.9031287
20	0.8603113
23	0.8735061

resK <- SelectResolution(stab.out$df)
resK

[1] 14

kable_material(
  kable(
    table(
      out$labelmat.mrtree[, which.min(
        abs(as.integer(
          str_remove(dimnames(
            out$labelmat.mrtree)[[2]], "K"
          )
        ) - resK)
      )]
    ),
    "html"),
  bootstrap_options = c("bordered",
                          "condensed",
                          "responsive",
                          "striped"),
  position = "left",
  font_size = 14
)

Var1	Freq
1	217
2	194
3	157
4	144
5	136
6	120
7	109
8	90
9	82
10	78
11	74
12	34
13	30
14	22

combined.srt$k_tree <- out$labelmat.mrtree[, which.min(
  abs(as.integer(
    str_remove(dimnames(
      out$labelmat.mrtree)[[2]], "K"
    )
  ) - resK)
)] %>% as.numeric() %>% as.factor()

QC_Plots_Mito(
  combined.srt,
  high_cutoff = high.cutoff.pc.mt,
  plot_title = "Mito genes % per Nucleus (overclustered)",
  color_seed = reseed,
  ggplot_default_colors = T
)

Past versions of unnamed-chunk-6-1.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02
c82f5c0	Evgenii O. Tretiakov	2023-01-25

QC_Plots_Feature(
  combined.srt,
  feature = "percent_ribo",
  high_cutoff = high.cutoff.pc.ribo,
  y_axis_label = "% Ribosomal Genes Counts",
  plot_title = "Ribo genes % per Nucleus (overclustered)",
  color_seed = reseed,
  ggplot_default_colors = T
)

Past versions of unnamed-chunk-7-1.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02
c82f5c0	Evgenii O. Tretiakov	2023-01-25

p1 <- DimPlot_scCustom(combined.srt, label = T, repel = T, pt.size = 2) + ggtitle("Unsupervised overclustering") + NoLegend()
p2 <- DimPlot_scCustom(combined.srt, label = T, repel = T, group.by = "k_tree", pt.size = 2) + ggtitle("MRTree") + NoLegend()

p1 | p2

Past versions of pl-umap-clusters-1.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02

Idents(combined.srt) <- "k_tree"

FeaturePlot_scCustom(combined.srt, features = "percent_mito", colors_use = combined.srt@misc$mdat_Colour_Pal, na_cutoff = NA, pt.size = 4, order = TRUE, alpha_na_exp = 0.3, alpha_exp = 0.75) & 
  theme(plot.title = element_text(size = 16))

Past versions of unnamed-chunk-8-1.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02
c82f5c0	Evgenii O. Tretiakov	2023-01-25

FeaturePlot_scCustom(combined.srt, features = "percent_mito", colors_use = combined.srt@misc$mdat_Colour_Pal, na_cutoff = 5, pt.size = 4, order = TRUE, alpha_na_exp = 0.3, alpha_exp = 0.75)

Past versions of unnamed-chunk-8-2.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02
c82f5c0	Evgenii O. Tretiakov	2023-01-25

FeaturePlot_scCustom(combined.srt, features = "nFeature_RNA", colors_use = combined.srt@misc$mdat_Colour_Pal, pt.size = 4, order = TRUE, alpha_na_exp = 0.3, alpha_exp = 0.75) & 
  theme(plot.title = element_text(size = 16))

Past versions of unnamed-chunk-8-3.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02

Dual Assay Plotting

For Cell Bender especially, but also potentially for other assays as well, it can be helpful during analysis to plot the corrected and uncorrected counts for given feature.

Past versions of unnamed-chunk-10-1.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02
c82f5c0	Evgenii O. Tretiakov	2023-01-25

Past versions of unnamed-chunk-11-1.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02
c82f5c0	Evgenii O. Tretiakov	2023-01-25

Both targets look fine.

DimPlot_scCustom(combined.srt, pt.size = 3, group.by = "QC", repel = T, label = T, label.size = 5)

Past versions of unnamed-chunk-12-1.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02
c82f5c0	Evgenii O. Tretiakov	2023-01-25

DimPlot_scCustom(combined.srt, label.size = 5, repel = T, pt.size = 3, label = T)

Past versions of unnamed-chunk-13-1.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02
c82f5c0	Evgenii O. Tretiakov	2023-01-25

combined.srt <- subset(combined.srt, subset = QC == "Pass")
DimPlot_scCustom(combined.srt, label.size = 4, repel = T, pt.size = 3, label = T)

Past versions of unnamed-chunk-14-1.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02

Reevaluate after subsetting low-quality cells

combined.srt$comb_clstr1 <- Idents(combined.srt)
s.genes = gorth(cc.genes.updated.2019$s.genes, source_organism = "hsapiens", target_organism = "mmusculus")$ortholog_name
g2m.genes = gorth(cc.genes.updated.2019$g2m.genes, source_organism = "hsapiens", target_organism = "mmusculus")$ortholog_name

combined.srt <-
  CellCycleScoring(combined.srt,
                   s.features = s.genes,
                   g2m.features = g2m.genes)
table(combined.srt[[]]$Phase)

 G1 G2M   S 
588 213 515 

FeaturePlot_scCustom(combined.srt,features = "percent_mito", label.size = 4, repel = T, pt.size = 3, label = T, colors_use = combined.srt@misc$mdat_Colour_Pal, order = TRUE, alpha_na_exp = 0.3, alpha_exp = 0.75)  & 
  theme(plot.title = element_text(size = 16))

Past versions of unnamed-chunk-16-1.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02
c82f5c0	Evgenii O. Tretiakov	2023-01-25

QC_Plots_Mito(
  combined.srt,
  high_cutoff = high.cutoff.pc.mt,
  plot_title = "Mito genes % per Nucleus",
  color_seed = reseed,
  ggplot_default_colors = T
)

Past versions of unnamed-chunk-17-1.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02
c82f5c0	Evgenii O. Tretiakov	2023-01-25

FeaturePlot_scCustom(combined.srt, features = "percent_ribo",
            label.size = 4,repel = T,pt.size = 3,label = T, colors_use = combined.srt@misc$mdat_Colour_Pal, order = TRUE, alpha_na_exp = 0.3, alpha_exp = 0.75)  & 
  theme(plot.title = element_text(size = 16))

Past versions of unnamed-chunk-18-1.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02
c82f5c0	Evgenii O. Tretiakov	2023-01-25

QC_Plots_Feature(
  combined.srt,
  feature = "percent_ribo",
  high_cutoff = high.cutoff.pc.ribo,
  y_axis_label = "% Ribosomal Genes Counts",
  plot_title = "Ribo genes % per Nucleus",
  color_seed = reseed,
  ggplot_default_colors = T
)

Past versions of unnamed-chunk-19-1.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02
c82f5c0	Evgenii O. Tretiakov	2023-01-25

p1 <-
  QC_Plots_Genes(
    combined.srt,
    low_cutoff = low.cutoff.gene,
    high_cutoff = high.cutoff.gene,
    plot_title = "Genes per Nucleus",
    color_seed = reseed,
    ggplot_default_colors = T
  )
p2 <-
  QC_Plots_UMIs(
    combined.srt,
    low_cutoff = low.cutoff.umis,
    high_cutoff = high.cutoff.umis,
    plot_title = "UMIs per Nucleus",
    color_seed = reseed,
    ggplot_default_colors = T
  )
p1 | p2

Past versions of unnamed-chunk-20-1.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02
c82f5c0	Evgenii O. Tretiakov	2023-01-25

FeaturePlot_scCustom(
  combined.srt,
  features = c("S.Score", "G2M.Score"),
  label.size = 4,
  repel = T,
  pt.size = 3,
  label = T,
  colors_use = combined.srt@misc$mdat_Colour_Pal,
  na_cutoff = NA,
  order = TRUE,
  alpha_na_exp = 0.3,
  alpha_exp = 0.75) & 
  theme(plot.title = element_text(size = 16))

Past versions of unnamed-chunk-21-1.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02
c82f5c0	Evgenii O. Tretiakov	2023-01-25

VlnPlot(combined.srt,
        features = c("S.Score", "G2M.Score")) &
  theme(plot.title = element_text(size=16))

Past versions of unnamed-chunk-22-1.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02
c82f5c0	Evgenii O. Tretiakov	2023-01-25

Apply SCTransform pipeline

# normalize and run dimensionality reduction on control dataset
npcs <-  30
metadata = combined.srt@meta.data
rownames(metadata) = colnames(combined.srt)
combined.srt <-
  SCTransform(
    combined.srt,
    vst.flavor = "v2",
    ncells = ncol(combined.srt),
    variable.features.n = 3500,
    vars.to.regress = c("log10GenesPerUMI",
                        "S.Score", "G2M.Score"),
    return.only.var.genes = FALSE,
    seed.use = reseed,
    verbose = FALSE
  ) 
hvg <- VariableFeatures(combined.srt)
var_regex <- '^Hla-|^Ig[hjkl]|^Rna|^mt-|^Rp[sl]|^Hb[^(p)]|^Gm'
hvg <- hvg[str_detect(pattern = var_regex, string = hvg, negate = T)]
combined.srt <- combined.srt %>%
  RunPCA(features = hvg, npcs = npcs, seed.use = reseed, verbose = FALSE)

print(combined.srt[["pca"]], dims = 1:5, nfeatures = 5)

PC_ 1 
Positive:  Ntng1, Tenm2, Cntnap2, Cntn5, Sgcz 
Negative:  Dnah6, Dnah12, Cfap299, Cfap54, Ak9 
PC_ 2 
Positive:  Atp1a2, Lama4, Arhgap31, Arhgap29, Flt1 
Negative:  Cntnap2, Tenm2, Ntng1, Meg3, Snhg11 
PC_ 3 
Positive:  Ptprz1, Slc4a4, Npas3, Luzp2, Wdr17 
Negative:  Ebf1, Myo1b, Hmcn1, Slc38a2, Rapgef5 
PC_ 4 
Positive:  Trpm3, Adam12, Ranbp3l, Slc6a13, Bmp6 
Negative:  Myo10, Ptprb, Ptprm, Egfl7, Slc7a5 
PC_ 5 
Positive:  Lrrc4c, Dpp10, Galnt13, Galntl6, Hs3st4 
Negative:  Ntng1, 6330411D24Rik, Lef1, Rorb, Arpp21 

VizDimLoadings(combined.srt, dims = 1:4, reduction = "pca")

Past versions of pl-pca-loadings-1.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02
c82f5c0	Evgenii O. Tretiakov	2023-01-25

DimHeatmap(combined.srt, dims = 1:15, cells = 500, balanced = TRUE)

Past versions of pl-pca-heatmap-1.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02
c82f5c0	Evgenii O. Tretiakov	2023-01-25

ElbowPlot(combined.srt, ndims = npcs)

Past versions of pl-elbow-pca-1.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02
c82f5c0	Evgenii O. Tretiakov	2023-01-25

combined.srt <-
  combined.srt |>
  FindNeighbors(
    dims = seq_along(combined.srt[["pca"]]@stdev),
    k.param = 20,
    annoy.metric = "euclidean",
    n.trees = 100,
    verbose = FALSE) |>
  RunUMAP(
    dims = seq_along(combined.srt[["pca"]]@stdev),
    reduction.name = "umap",
    reduction.key = "UMAP_",
    return.model = TRUE,
    umap.method = "umap-learn",
    densmap = TRUE,
    dens.lambda = 1L,
    dens.frac = 0.1,
    n.epochs = 1000L,
    n.neighbors = 20L,
    min.dist = 0.01,
    spread = 4L,
    metric = "correlation",
    init = "pca",
    seed.use = reseed,
    verbose = FALSE) |>
  FindNeighbors(
    reduction = "umap",
    dims = 1:2,
    force.recalc = TRUE,
    k.param = 20,
    annoy.metric = "euclidean",
    n.trees = 100,
    verbose = FALSE)

Plot by source after clean up

plEmbCombBatch <- DimPlot_scCustom(combined.srt, reduction = "umap",
                          group.by = "orig.ident", pt.size = 3,
                          label = TRUE, repel = TRUE, seed = reseed,
                          ggplot_default_colors = TRUE, color_seed = reseed,
                          shuffle = TRUE) + NoLegend()
plEmbCombBatch

Past versions of pl-all-RNA-1.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02
c82f5c0	Evgenii O. Tretiakov	2023-01-25

metadata <- combined.srt@meta.data
rownames(metadata) <- colnames(combined.srt)
ref.labels <- metadata$k_tree

resolutions <-
  modularity_event_sampling(
    A = combined.srt@graphs$SCT_snn,
    n.res = 70,
    gamma.min = 0.05,
    gamma.max = 4.000001
  ) # sample based on the similarity matrix

# clustering using Suerat
combined.srt <- combined.srt |> 
  FindClusters(algorithm = 4, method = "igraph",
               resolution = resolutions, random.seed = reseed,
               verbose = FALSE)

# initial cluster tree from Seurat flat clustering
plot_clustree(
  labelmat = combined.srt@meta.data,
  prefix = 'SCT_snn_res.',
  ref.labels = ref.labels,
  plot.ref = FALSE
)

Past versions of pl-clustree-1.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02
c82f5c0	Evgenii O. Tretiakov	2023-01-25

# Adjusted Multiresolution Rand Index (AMRI)
ks.flat <-  apply(
  out$labelmat.flat,
  2,
  FUN = function(x)
    length(unique(x))
)
ks.mrtree <-  apply(
  out$labelmat.mrtree,
  2,
  FUN = function(x)
    length(unique(x))
)
amri.flat <-  sapply(1:ncol(out$labelmat.flat), function(i)
  AMRI(out$labelmat.flat[, i], ref.labels)$amri)
amri.flat <-  aggregate(amri.flat, by = list(k = ks.flat), FUN = mean)
amri.recon <-  sapply(1:ncol(out$labelmat.mrtree), function(i)
  AMRI(out$labelmat.mrtree[, i], ref.labels)$amri)

df <-  rbind(
  data.frame(
    k = amri.flat$k,
    amri = amri.flat$x,
    method = 'Seurat flat'
  ),
  data.frame(k = ks.mrtree, amri = amri.recon, method = 'MRtree')
)
ggplot2::ggplot(data = df, aes(x = k, y = amri, color = method)) + geom_line() + theme_bw()

Past versions of pl-clustering-amri-1.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02
c82f5c0	Evgenii O. Tretiakov	2023-01-25

stab.out <- stability_plot(out)
stab.out$plot

Past versions of pl-clustering-resolution-1.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02
c82f5c0	Evgenii O. Tretiakov	2023-01-25

kable_material(
  kable(
    stab.out$df,
    "html"),
  bootstrap_options = c("bordered",
                          "condensed",
                          "responsive",
                          "striped"),
  position = "left",
  font_size = 14
)

resolution	ari
12	1.0000000
13	1.0000000
14	0.9950979
15	1.0000000
16	1.0000000
17	1.0000000
18	1.0000000
19	1.0000000
20	1.0000000
21	1.0000000
22	1.0000000
24	0.9772836
25	0.9982121
26	0.9967004

resK <- SelectResolution(stab.out$df)
resK

 [1] 22 21 20 19 18 17 16 15 13 12

kable_material(
  kable(
    table(
      out$labelmat.mrtree[, which.min(
        abs(as.integer(
          str_remove(dimnames(
            out$labelmat.mrtree)[[2]], "K"
          )
        ) - resK)
      )]
    ),
    "html"),
  bootstrap_options = c("bordered",
                          "condensed",
                          "responsive",
                          "striped"),
  position = "left",
  font_size = 14
)

Var1	Freq
1	166
2	163
3	142
4	138
5	110
6	90
7	72
8	70
9	62
10	61
11	58
12	41
13	34
14	30
15	30
16	28
17	21

combined.srt$k_tree <- out$labelmat.mrtree[, which.min(
  abs(as.integer(
    str_remove(dimnames(
      out$labelmat.mrtree)[[2]], "K"
    )
  ) - resK)
)] %>% as.numeric() %>% as.factor()
p1 <- DimPlot_scCustom(combined.srt, label = T, repel = T, pt.size = 2) + ggtitle("Unsupervised overclustering") + NoLegend()
p2 <- DimPlot_scCustom(combined.srt, label = T, repel = T, group.by = "k_tree", pt.size = 2) + ggtitle("MRTree") + NoLegend()

p1 | p2

Past versions of pl-clustering-1.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02
c82f5c0	Evgenii O. Tretiakov	2023-01-25

FeaturePlot_scCustom(combined.srt, "Galr1", pt.size = 2, order = T, colors_use = combined.srt@misc$expr_Colour_Pal, alpha_na_exp = 0.3, alpha_exp = 0.75) + 
  ggtitle("Galr1: ") + theme(plot.title = element_text(size = 24))

Past versions of unnamed-chunk-23-1.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02
c82f5c0	Evgenii O. Tretiakov	2023-01-25

FeaturePlot_scCustom(combined.srt, "Gal", pt.size = 2, order = T, colors_use = combined.srt@misc$expr_Colour_Pal, alpha_na_exp = 0.3, alpha_exp = 0.75) + 
  ggtitle("Gal: ") + theme(plot.title = element_text(size = 24))

Past versions of unnamed-chunk-24-1.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02
c82f5c0	Evgenii O. Tretiakov	2023-01-25

DotPlot_scCustom(
  seurat_object = combined.srt,
  assay = "SCT",
  features = genes.zh,
  flip_axes = TRUE,
  x_lab_rotate = TRUE,
  colors_use = viridis(n = 30, alpha = .75, direction = -1, option = "G"))

Past versions of pl-gal-galr1-dotplot-sct-1.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02

DotPlot_scCustom(
  seurat_object = combined.srt,
  assay = "RNA",
  features = genes.zh,
  flip_axes = TRUE,
  x_lab_rotate = TRUE,
  colors_use = viridis(n = 30, alpha = .75, direction = -1, option = "E"))

Past versions of pl-gal-galr1-dotplot-rna-1.png

Version	Author	Date
3b19c24	Evgenii O. Tretiakov	2023-02-02

We see the spread of our targets across derived clusters, which isn’t optimal. Lets see if we will see some significant hits with proper statistical testing.

Idents(combined.srt) <- "k_tree"
combined.srt <- 
  PrepSCTFindMarkers(combined.srt, assay = "SCT")

markers.logreg <- 
  FindAllMarkers(
    combined.srt,
    assay = "SCT",
    verbose = FALSE,
    random.seed = reseed,
    only.pos = TRUE,
    min.pct = 0.2,
    base = 10,
    logfc.threshold = 0.2,
    densify = TRUE,
    test.use = "LR")
write_csv(markers.logreg,
          here(tables_dir,
               'hevesi2023-all-mrk_logreg-sct_combined.csv'))

markers.logreg %>%
  group_by(cluster) %>%
  slice_max(n = 20, order_by = avg_log10FC) %>%
  kable("html") %>%
  kable_material(
    bootstrap_options = c("bordered",
                          "condensed",
                          "responsive",
                          "striped"),
    position = "left",
    font_size = 14
  )

p_val	avg_log10FC	pct.1	pct.2	p_val_adj	cluster	gene
0.0000000	0.6571165	0.988	0.593	0.0000000	1	Tafa1
0.0000000	0.6502581	1.000	0.704	0.0000000	1	Tenm1
0.0000000	0.5972545	1.000	0.678	0.0000000	1	Ntng1
0.0000000	0.5949014	1.000	0.695	0.0000000	1	Rnf220
0.0000000	0.5871276	0.976	0.670	0.0000000	1	Shisa9
0.0000000	0.5609202	0.904	0.420	0.0000000	1	Thsd7b
0.0000000	0.5595962	0.994	0.502	0.0000000	1	Samd5
0.0000000	0.5470635	0.904	0.299	0.0000000	1	Gm48749
0.0000000	0.5171596	0.988	0.621	0.0000000	1	Cntnap5a
0.0000000	0.5032538	0.873	0.258	0.0000000	1	Gm32647
0.0000000	0.4864269	0.994	0.610	0.0000000	1	Tox
0.0000000	0.4645483	0.855	0.285	0.0000000	1	Trhde
0.0000000	0.4464085	0.988	0.695	0.0000000	1	Egfem1
0.0000000	0.4429732	1.000	0.756	0.0000000	1	Cntnap2
0.0000000	0.4409920	1.000	0.878	0.0000000	1	Ptprd
0.0000000	0.4356482	1.000	0.773	0.0000000	1	Grm7
0.0000000	0.4331818	1.000	0.654	0.0000000	1	Arpp21
0.0000000	0.4261508	0.940	0.397	0.0000000	1	Foxp2
0.0000000	0.4191931	0.994	0.868	0.0000000	1	Grik2
0.0000000	0.4147323	0.880	0.437	0.0000000	1	Cdh6
0.0000000	1.2753468	0.785	0.040	0.0000000	2	Cfap299
0.0000000	1.2557930	0.761	0.023	0.0000000	2	Dnah12
0.0000000	1.1597560	0.773	0.104	0.0000000	2	Adamts20
0.0000000	1.0796429	0.755	0.039	0.0000000	2	Dnah6
0.0000000	1.0502595	0.822	0.095	0.0000000	2	Cfap54
0.0000000	1.0298384	0.810	0.118	0.0000000	2	Spag16
0.0000000	1.0196588	0.804	0.072	0.0000000	2	Ccdc162
0.0000000	1.0130810	0.791	0.032	0.0000000	2	Hydin
0.0000000	1.0021477	0.767	0.054	0.0000000	2	Cfap61
0.0000000	1.0017248	0.804	0.056	0.0000000	2	Rgs22
0.0000000	0.9974433	0.791	0.069	0.0000000	2	Cfap44
0.0000000	0.9863835	0.847	0.103	0.0000000	2	Gm973
0.0000000	0.9656747	0.791	0.049	0.0000000	2	Spef2
0.0000000	0.9587074	0.761	0.051	0.0000000	2	Ak7
0.0000000	0.9536831	0.791	0.024	0.0000000	2	Ak9
0.0000000	0.9531191	0.969	0.884	0.0000000	2	Syne1
0.0000000	0.9518822	0.810	0.059	0.0000000	2	Kif6
0.0000000	0.9506150	0.798	0.132	0.0000000	2	Dnah9
0.0000000	0.9440670	0.779	0.037	0.0000000	2	Dnah11
0.0000000	0.9041702	0.761	0.026	0.0000000	2	Cfap65
0.0000000	0.5608464	0.810	0.333	0.0000000	3	Unc5d
0.0000000	0.5580631	0.866	0.520	0.0000000	3	Cdh18
0.0000000	0.4651351	0.866	0.335	0.0000000	3	Tafa2
0.0000000	0.4597091	0.789	0.312	0.0000000	3	Gm15398
0.0000000	0.4452701	0.915	0.682	0.0000000	3	Galntl6
0.0000000	0.4450124	0.873	0.232	0.0000000	3	Dlgap2
0.0000000	0.4358118	0.979	0.741	0.0000000	3	Lrrtm4
0.0000000	0.4260853	0.873	0.363	0.0000000	3	Gm26871
0.0000001	0.4250104	0.535	0.419	0.0010774	3	Adarb2
0.0000000	0.4097539	0.873	0.488	0.0000000	3	Gria1
0.0000000	0.4029539	0.958	0.718	0.0000000	3	Lingo2
0.0000000	0.4002465	0.965	0.801	0.0000000	3	Ralyl
0.0000000	0.3943962	1.000	0.832	0.0000000	3	Ahi1
0.0000000	0.3943376	1.000	0.766	0.0000000	3	Dlg2
0.0000000	0.3921796	0.831	0.318	0.0000000	3	Grin2a
0.0000000	0.3850609	0.810	0.430	0.0000000	3	Grm8
0.0000000	0.3732597	0.979	0.648	0.0000000	3	A230006K03Rik
0.0000000	0.3696774	0.979	0.784	0.0000000	3	Dab1
0.0000000	0.3531083	0.683	0.121	0.0000000	3	B230217J21Rik
0.0000000	0.3444115	0.908	0.638	0.0000000	3	Cntnap5a
0.0000000	0.9047788	0.935	0.311	0.0000000	4	Gm3764
0.0000000	0.8179890	0.971	0.628	0.0000000	4	Ptprz1
0.0000000	0.7988612	0.949	0.429	0.0000000	4	Slc4a4
0.0000000	0.7099902	0.971	0.734	0.0000000	4	Npas3
0.0000000	0.6853820	0.928	0.665	0.0000000	4	Luzp2
0.0000000	0.6719878	0.928	0.408	0.0000000	4	Gm48747
0.0000000	0.6436592	0.790	0.180	0.0000000	4	Slc6a11
0.0000000	0.6110417	0.899	0.374	0.0000000	4	Nhsl1
0.0000000	0.6106302	0.935	0.639	0.0000000	4	Gpc5
0.0000000	0.6049856	0.935	0.553	0.0000000	4	Trim9
0.0000000	0.5907997	1.000	0.733	0.0000000	4	Qk
0.0000000	0.5708835	0.841	0.193	0.0000000	4	Lrig1
0.0000000	0.5577725	0.928	0.508	0.0000000	4	Wdr17
0.0000000	0.5505430	0.957	0.648	0.0000000	4	Grm3
0.0000000	0.5466775	0.971	0.575	0.0000000	4	Ptn
0.0000000	0.5409534	0.906	0.492	0.0000000	4	Slc1a2
0.0000000	0.5363683	0.790	0.168	0.0000000	4	Bmpr1b
0.0000000	0.5356458	0.848	0.261	0.0000000	4	Plpp3
0.0000000	0.5292591	0.877	0.237	0.0000000	4	Apoe
0.0000000	0.5166935	0.717	0.141	0.0000000	4	Pla2g7
0.0000000	0.4387974	0.991	0.896	0.0000000	5	Gm42418
0.0000000	0.3672638	0.909	0.844	0.0000001	5	Gm26917
0.0000000	0.3635542	0.682	0.455	0.0000001	5	Cdh4
0.0000000	0.3596761	0.909	0.556	0.0000000	5	Cmss1
0.0000000	0.3509651	0.836	0.481	0.0000000	5	Rbfox3
0.0000000	0.3420507	0.727	0.390	0.0000000	5	Gm26871
0.0000000	0.3377407	0.845	0.534	0.0000000	5	Nxph1
0.0000000	0.3137945	0.791	0.549	0.0000000	5	Gpi1
0.0000000	0.3093742	0.627	0.271	0.0000000	5	Dlgap2
0.0000000	0.2992671	0.982	0.862	0.0000000	5	Camta1
0.0000000	0.2979735	1.000	0.907	0.0000000	5	Meg3
0.0000000	0.2919796	0.991	0.801	0.0000000	5	Nkain2
0.0000000	0.2909660	0.955	0.695	0.0000000	5	Celf4
0.0000000	0.2830124	0.836	0.580	0.0000000	5	Klhl29
0.0000000	0.2805466	0.955	0.792	0.0000000	5	Dab1
0.0000000	0.2784120	0.873	0.528	0.0000000	5	Cdh18
0.0000000	0.2761471	0.909	0.637	0.0000000	5	Usp29
0.0000000	0.2734255	0.955	0.737	0.0000001	5	Schip1
0.0000048	0.2646295	0.527	0.318	0.0983789	5	Gm32647
0.0000000	0.2646166	0.991	0.825	0.0000000	5	Ptprn2
0.0000000	1.3287492	0.667	0.033	0.0000000	6	Ptgds
0.0000000	1.1001808	0.933	0.048	0.0000000	6	Ranbp3l
0.0000000	1.0231247	0.956	0.883	0.0000000	6	Trpm3
0.0000000	0.9147659	0.933	0.225	0.0000000	6	Adam12
0.0000000	0.8879254	0.867	0.056	0.0000000	6	Slc6a20a
0.0000000	0.8669011	0.844	0.109	0.0000000	6	Adamts12
0.0000000	0.8499882	0.878	0.165	0.0000000	6	Sidt1
0.0000000	0.7909713	0.767	0.144	0.0000000	6	Bmp6
0.0000000	0.7883417	0.956	0.310	0.0000000	6	Atp1a2
0.0000000	0.7644435	0.900	0.016	0.0000000	6	Slc6a13
0.0000000	0.7474819	0.922	0.292	0.0000000	6	Bicc1
0.0000000	0.7271937	0.744	0.183	0.0000000	6	Slc7a11
0.0000000	0.7090860	0.867	0.701	0.0000000	6	Nnat
0.0000000	0.6924188	0.767	0.145	0.0000000	6	Lrmda
0.0000000	0.6884408	0.844	0.109	0.0000000	6	Cped1
0.0000000	0.6758639	0.878	0.146	0.0000000	6	Tmtc4
0.0000000	0.6605440	0.844	0.268	0.0000000	6	Sned1
0.0000000	0.6390464	0.911	0.105	0.0000000	6	Arhgap29
0.0000000	0.6233387	0.878	0.231	0.0000000	6	Pdzrn3
0.0000000	0.5929585	0.822	0.138	0.0000000	6	Colec12
0.0000000	0.9487361	0.958	0.304	0.0000000	7	6330411D24Rik
0.0000000	0.8401545	0.972	0.336	0.0000000	7	Pex5l
0.0000000	0.6446468	0.986	0.511	0.0000000	7	Ryr3
0.0000000	0.6350898	0.986	0.525	0.0000000	7	Spock1
0.0000000	0.6163441	0.917	0.404	0.0000000	7	Adarb1
0.0000000	0.5945646	1.000	0.783	0.0000000	7	Hs6st3
0.0000000	0.5938236	0.861	0.163	0.0000000	7	4930419G24Rik
0.0000000	0.5647983	0.986	0.521	0.0000000	7	Edil3
0.0000000	0.5421220	0.944	0.233	0.0000000	7	Ptpn3
0.0000000	0.5377003	1.000	0.518	0.0000000	7	Zmat4
0.0000000	0.5373895	0.944	0.416	0.0000000	7	Cntn3
0.0000000	0.5326070	1.000	0.696	0.0000000	7	Frmpd4
0.0000000	0.5131547	1.000	0.617	0.0000000	7	Zfp804b
0.0000000	0.5076611	1.000	0.655	0.0000000	7	Unc13c
0.0000000	0.5075011	0.972	0.489	0.0000000	7	Camk4
0.0000000	0.4950522	0.986	0.671	0.0000000	7	Cacnb4
0.0000000	0.4940987	0.972	0.421	0.0000000	7	Epb41l4b
0.0000000	0.4919531	1.000	0.879	0.0000000	7	Prickle2
0.0000000	0.4915512	0.944	0.265	0.0000000	7	Fras1
0.0000000	0.4912050	0.958	0.587	0.0000000	7	Pak7
0.0000000	0.9315875	0.957	0.429	0.0000000	8	Hs3st4
0.0000000	0.8830266	1.000	0.797	0.0000000	8	Lrrc4c
0.0000000	0.8702088	0.814	0.196	0.0000000	8	4930445B16Rik
0.0000000	0.8293413	1.000	0.698	0.0000000	8	Dpp10
0.0000000	0.8184406	1.000	0.691	0.0000000	8	Galntl6
0.0000000	0.7285249	0.914	0.279	0.0000000	8	Kcnmb2
0.0000000	0.6817534	0.929	0.150	0.0000000	8	Gm15155
0.0000000	0.6707176	0.886	0.192	0.0000000	8	Meis2
0.0000000	0.6624833	0.971	0.813	0.0000000	8	Asic2
0.0000000	0.6501771	0.943	0.288	0.0000000	8	Ubash3b
0.0000000	0.6447586	0.971	0.781	0.0000000	8	Nrg1
0.0000000	0.6310500	0.871	0.071	0.0000000	8	Gad1
0.0000000	0.6099125	0.957	0.096	0.0000000	8	Gad2
0.0000000	0.5988190	0.986	0.663	0.0000000	8	Grm3
0.0000000	0.5938922	0.729	0.148	0.0000000	8	Sema3e
0.0000000	0.5905362	0.886	0.132	0.0000000	8	Ptchd1
0.0000000	0.5854086	1.000	0.880	0.0000000	8	Nrxn3
0.0000000	0.5853860	0.971	0.432	0.0000000	8	Fign
0.0000000	0.5767985	0.871	0.336	0.0000000	8	Ak5
0.0000000	0.5755538	0.986	0.610	0.0000000	8	Zfp804a
0.0000000	1.3227119	0.984	0.053	0.0000000	9	Flt1
0.0000000	1.1177770	0.984	0.104	0.0000000	9	Mecom
0.0000000	0.9762405	0.984	0.210	0.0000000	9	Dach1
0.0000000	0.9535095	0.952	0.149	0.0000000	9	Slc7a1
0.0000000	0.9290368	0.935	0.053	0.0000000	9	Ets1
0.0000000	0.9213625	0.887	0.116	0.0000000	9	Slco1c1
0.0000000	0.9055905	0.935	0.069	0.0000000	9	Slc7a5
0.0000000	0.8841938	0.919	0.015	0.0000000	9	Egfl7
0.0000000	0.8678912	0.935	0.030	0.0000000	9	Ptprb
0.0000000	0.8581400	0.919	0.058	0.0000000	9	Fli1
0.0000000	0.8457729	0.968	0.230	0.0000000	9	Ccdc141
0.0000000	0.8305455	0.871	0.191	0.0000000	9	Hmcn1
0.0000000	0.8284534	0.903	0.030	0.0000000	9	Adgrl4
0.0000000	0.8179876	0.968	0.368	0.0000000	9	Slc38a2
0.0000000	0.7916615	1.000	0.311	0.0000000	9	Myo10
0.0000000	0.7810337	0.823	0.055	0.0000000	9	Apcdd1
0.0000000	0.7741358	0.952	0.286	0.0000000	9	Rapgef5
0.0000000	0.7670584	1.000	0.632	0.0000000	9	Ptprm
0.0000000	0.7670278	0.887	0.226	0.0000000	9	Tfrc
0.0000000	0.7660856	0.887	0.109	0.0000000	9	Lama4
0.0000000	0.7966165	0.869	0.163	0.0000000	10	4930588A03Rik
0.0000000	0.7900272	0.918	0.123	0.0000000	10	Gm38505
0.0000000	0.7892775	0.984	0.469	0.0000000	10	Pcdh15
0.0000000	0.7020805	1.000	0.441	0.0000000	10	Sox6
0.0000000	0.6957390	0.984	0.527	0.0000000	10	6030443J06Rik
0.0000000	0.6718450	0.836	0.129	0.0000000	10	Pdgfra
0.0000000	0.6512600	0.984	0.551	0.0000000	10	Sox2ot
0.0000000	0.6372609	0.967	0.471	0.0000000	10	Lhfpl3
0.0000000	0.6270686	0.918	0.247	0.0000000	10	Dscaml1
0.0000000	0.5654791	0.967	0.500	0.0000000	10	Xylt1
0.0000000	0.5652314	0.951	0.514	0.0000000	10	Epn2
0.0000000	0.5536460	0.820	0.083	0.0000000	10	Sox10
0.0000000	0.5288474	0.918	0.309	0.0000000	10	Mir9-3hg
0.0000000	0.5160463	0.984	0.554	0.0000000	10	Pcdh11x
0.0000000	0.4990924	1.000	0.708	0.0000000	10	Sgcd
0.0000000	0.4937250	0.836	0.244	0.0000000	10	Megf11
0.0000000	0.4869009	1.000	0.668	0.0000000	10	Dcc
0.0000000	0.4563497	0.918	0.431	0.0000000	10	Tnr
0.0000000	0.4527685	1.000	0.539	0.0000000	10	Nxph1
0.0000000	0.4519295	0.820	0.224	0.0000000	10	Arhgef3
0.0000000	1.0241256	0.500	0.174	0.0000000	11	Mbp
0.0000000	0.8482169	0.379	0.153	0.0000000	11	9630013A20Rik
0.0000000	0.8172537	0.328	0.092	0.0000000	11	Plp1
0.0000000	0.7469378	0.310	0.055	0.0000000	11	St18
0.0000000	0.7263954	0.517	0.087	0.0000000	11	Npsr1
0.0000000	0.7020544	0.983	0.839	0.0000000	11	Kcnip4
0.0000000	0.6602253	0.707	0.219	0.0000000	11	Pcsk5
0.0000000	0.6549541	0.293	0.123	0.0000000	11	Cnksr3
0.0000000	0.5952941	0.862	0.627	0.0000000	11	Zfp804b
0.0000000	0.5662574	0.259	0.006	0.0000000	11	Mobp
0.0000000	0.5583427	0.690	0.193	0.0000000	11	Prox1
0.0000000	0.5540357	0.741	0.355	0.0000000	11	Cntnap5c
0.0000000	0.5495114	0.828	0.631	0.0000000	11	Nfasc
0.0000000	0.5457659	0.828	0.692	0.0000000	11	Brinp3
0.0000000	0.5030720	0.293	0.020	0.0000000	11	Mag
0.0000005	0.4724393	0.759	0.648	0.0106283	11	Tmeff2
0.0000000	0.4713310	0.948	0.626	0.0000000	11	Gm20754
0.0000000	0.4706809	0.431	0.196	0.0000006	11	Cemip2
0.0000000	0.4487399	0.862	0.672	0.0000000	11	Rgs6
0.0000000	0.4460125	0.741	0.349	0.0000000	11	Prr16
0.0006958	0.4808576	0.463	0.297	1.0000000	12	Nwd2
0.0000000	0.4743175	0.610	0.355	0.0007256	12	Gm15398
0.0000000	0.4619596	0.610	0.304	0.0000169	12	Slit3
0.0000010	0.4598898	0.488	0.267	0.0208247	12	Gm45321
0.0000000	0.4584631	0.683	0.308	0.0000001	12	Stxbp5l
0.0000004	0.4039485	0.610	0.315	0.0092817	12	4930555F03Rik
0.0000000	0.4024779	0.976	0.760	0.0000001	12	Lrrtm4
0.0000224	0.3880570	0.878	0.791	0.4636247	12	Unc5c
0.0000001	0.3812647	0.390	0.180	0.0010581	12	Gfra1
0.0000067	0.3810508	0.561	0.325	0.1392465	12	Kctd8
0.0000000	0.3777165	0.854	0.560	0.0000002	12	Gpi1
0.0033288	0.3699645	0.756	0.580	1.0000000	12	Cmss1
0.0000000	0.3641411	1.000	0.784	0.0000016	12	Nrg1
0.0000114	0.3494685	1.000	0.892	0.2357880	12	Kcnma1
0.0003471	0.3432635	0.268	0.101	1.0000000	12	Npsr1
0.0000280	0.3364966	0.683	0.409	0.5785582	12	C130073E24Rik
0.0050611	0.3364041	0.976	0.901	1.0000000	12	Gm42418
0.0031680	0.3301619	0.439	0.307	1.0000000	12	Cpne4
0.0000000	0.3288227	0.732	0.411	0.0000709	12	Unc13a
0.0006945	0.3233748	0.902	0.848	1.0000000	12	Gm26917
0.0000000	0.9449937	0.647	0.144	0.0000000	13	Bcas1
0.0000000	0.9409910	0.706	0.447	0.0000000	13	Tnr
0.0000000	0.7218463	0.559	0.059	0.0000000	13	Bcas1os2
0.0000000	0.6971670	0.647	0.215	0.0000000	13	Tns3
0.0000000	0.6352373	0.882	0.746	0.0000000	13	Fyn
0.0000000	0.6340517	0.824	0.172	0.0000000	13	Mbp
0.0000000	0.6121338	0.676	0.300	0.0000000	13	Itpr2
0.0000000	0.5947798	0.647	0.457	0.0000000	13	Epb41l2
0.0000000	0.5885908	0.618	0.034	0.0000000	13	Enpp6
0.0000000	0.5688640	0.882	0.821	0.0008781	13	Opcml
0.0000000	0.5635389	0.824	0.372	0.0000000	13	Ust
0.0000000	0.5545936	0.529	0.054	0.0000001	13	St18
0.0000006	0.5448275	0.559	0.330	0.0127979	13	Gm32647
0.0000000	0.5375124	0.824	0.590	0.0000004	13	9530059O14Rik
0.0000000	0.5357745	1.000	0.651	0.0000000	13	Tmem108
0.0000000	0.5283072	0.882	0.633	0.0000502	13	Nfasc
0.0000000	0.5262521	0.735	0.275	0.0000000	13	Sirt2
0.0000000	0.5146112	0.588	0.360	0.0003282	13	Prr16
0.0000000	0.5041546	0.588	0.244	0.0000000	13	Abtb2
0.0000000	0.4999108	1.000	0.927	0.0001631	13	Frmd4a
0.0000000	1.3744411	1.000	0.080	0.0000000	14	Bnc2
0.0000000	0.9355881	1.000	0.463	0.0000000	14	Fbxl7
0.0000000	0.9272111	0.867	0.059	0.0000000	14	Adamtsl3
0.0000000	0.9157545	0.933	0.158	0.0000000	14	Thsd4
0.0000000	0.9076270	1.000	0.713	0.0000000	14	Foxp1
0.0000000	0.8969509	1.000	0.383	0.0000000	14	Slc38a2
0.0000000	0.8967671	0.933	0.320	0.0000000	14	Nr3c2
0.0000000	0.8908707	0.900	0.546	0.0000000	14	Slc4a10
0.0000000	0.8503342	0.933	0.065	0.0000000	14	Trabd2b
0.0000000	0.8333417	0.833	0.312	0.0000000	14	Col25a1
0.0000000	0.8118453	0.833	0.183	0.0000000	14	Itgbl1
0.0000000	0.7796978	0.933	0.357	0.0000000	14	Gulp1
0.0000000	0.7562549	0.933	0.115	0.0000000	14	Eya2
0.0000000	0.7426407	0.967	0.452	0.0000000	14	Tmtc1
0.0000000	0.7338606	0.867	0.259	0.0000000	14	Sh3pxd2a
0.0000000	0.7283485	1.000	0.645	0.0000000	14	Tmeff2
0.0000000	0.7180718	0.800	0.096	0.0000000	14	Dock5
0.0000000	0.7033469	0.967	0.205	0.0000000	14	Hmcn1
0.0000000	0.7017159	0.967	0.312	0.0000000	14	Nxn
0.0000000	0.6722369	0.567	0.067	0.0000000	14	Crispld1
0.0000000	1.6692281	0.900	0.174	0.0000000	15	Htr2c
0.0000000	1.4475659	1.000	0.079	0.0000000	15	Ttr
0.0000000	0.9520627	1.000	0.542	0.0000000	15	Wdr17
0.0000000	0.9413800	0.800	0.026	0.0000000	15	Gmnc
0.0000000	0.9072979	0.900	0.190	0.0000000	15	Enpp2
0.0000000	0.8979889	0.967	0.065	0.0000000	15	Rbm47
0.0000000	0.8974342	0.900	0.234	0.0000000	15	Vat1l
0.0000000	0.8787920	0.933	0.163	0.0000000	15	Sulf1
0.0000000	0.8685294	0.933	0.192	0.0000000	15	Otx2os1
0.0000000	0.8414672	0.967	0.673	0.0000000	15	Stk39
0.0000000	0.8107865	1.000	0.885	0.0000000	15	Trpm3
0.0000000	0.7751631	0.900	0.330	0.0000000	15	Nhsl2
0.0000000	0.7668973	0.967	0.242	0.0000000	15	C330002G04Rik
0.0000000	0.7504541	0.967	0.383	0.0000000	15	Itpr1
0.0000000	0.7147429	1.000	0.706	0.0000000	15	Frmpd4
0.0000000	0.7066112	0.867	0.175	0.0000000	15	Prdm16
0.0000000	0.7000165	1.000	0.589	0.0000000	15	Rfx3
0.0000000	0.6970875	0.800	0.254	0.0000000	15	Cab39l
0.0000000	0.6945247	0.800	0.225	0.0000000	15	Atp2b3
0.0000000	0.6784167	0.933	0.169	0.0000000	15	Slc16a2
0.0000000	0.6093348	0.964	0.428	0.0000000	16	Pcsk2
0.0000000	0.5823304	0.857	0.248	0.0000000	16	Stxbp6
0.0000000	0.5686065	1.000	0.380	0.0000000	16	Lef1
0.0000000	0.5294625	1.000	0.713	0.0000000	16	Ntng1
0.0000000	0.5060755	0.964	0.434	0.0000000	16	Cntn3
0.0000000	0.5014865	1.000	0.302	0.0000000	16	Cpne7
0.0000000	0.4788813	0.964	0.199	0.0000000	16	Prox1
0.0000000	0.4644870	0.893	0.422	0.0000716	16	Adarb2
0.0000000	0.4626738	1.000	0.527	0.0000000	16	Ryr3
0.0000010	0.4577550	0.643	0.318	0.0213566	16	4930555F03Rik
0.0000000	0.4526475	1.000	0.782	0.0000000	16	Cntnap2
0.0000000	0.4508588	0.964	0.739	0.0000000	16	Lingo2
0.0000000	0.4413095	0.964	0.766	0.0000062	16	Mgat4c
0.0000000	0.4407631	0.964	0.723	0.0000002	16	Sox5
0.0000000	0.4366980	0.893	0.328	0.0000169	16	6330411D24Rik
0.0000000	0.4363868	0.679	0.189	0.0000005	16	Sox5os4
0.0000000	0.4347882	0.750	0.125	0.0000000	16	Rxfp1
0.0000000	0.4340566	1.000	0.534	0.0000002	16	Zmat4
0.0000000	0.4294471	1.000	0.753	0.0000000	16	Syt1
0.0000005	0.4211794	0.750	0.609	0.0097965	16	Rorb
0.0000000	0.8169323	1.000	0.632	0.0000000	17	Zfp804b
0.0000000	0.7913766	1.000	0.608	0.0000000	17	Kcnq5
0.0000000	0.7854327	1.000	0.738	0.0000000	17	Cntn5
0.0000000	0.7255555	1.000	0.842	0.0000000	17	Kcnip4
0.0000000	0.7024320	1.000	0.785	0.0000000	17	Tenm2
0.0000000	0.6813306	0.952	0.767	0.0000006	17	Mgat4c
0.0000000	0.6570441	1.000	0.751	0.0000000	17	Schip1
0.0000000	0.5996349	0.905	0.377	0.0000000	17	Epha3
0.0000000	0.5933017	1.000	0.819	0.0000000	17	Opcml
0.0000000	0.5851687	1.000	0.635	0.0000000	17	Gm20754
0.0000000	0.5699112	0.857	0.304	0.0000003	17	Slit3
0.0000000	0.5638579	0.952	0.764	0.0000009	17	Lrrtm4
0.0000000	0.5532614	0.952	0.868	0.0000000	17	Slc24a3
0.0000000	0.5432472	1.000	0.598	0.0000000	17	Gabra2
0.0000000	0.5378526	0.952	0.434	0.0000017	17	4930509J09Rik
0.0000000	0.5333226	1.000	0.724	0.0000010	17	Sox5
0.0000004	0.5284057	0.524	0.100	0.0075898	17	Npsr1
0.0000000	0.5197057	1.000	0.887	0.0000000	17	Rims2
0.0000000	0.5170743	0.905	0.669	0.0000000	17	Kcnc2
0.0000000	0.5093695	1.000	0.520	0.0000001	17	Cdh8

markers.wilcox <-
  FindAllMarkers(
    combined.srt,
    assay = "SCT",
    verbose = FALSE,
    random.seed = reseed,
    only.pos = TRUE,
    min.pct = 0.2,
    base = 10,
    logfc.threshold = 0.2,
    densify = TRUE,
    test.use = "wilcox")
write_csv(markers.wilcox,
          here(tables_dir,
               'hevesi2023-all-mrk_wilcox-sct_combined.csv'))
markers.wilcox %>%
  group_by(cluster) %>%
  slice_max(n = 20, order_by = avg_log10FC) %>%
  kable("html") %>%
  kable_material(
    bootstrap_options = c("bordered",
                          "condensed",
                          "responsive",
                          "striped"),
    position = "left",
    font_size = 14
  )

p_val	avg_log10FC	pct.1	pct.2	p_val_adj	cluster	gene
0.0000000	0.6571165	0.988	0.593	0.0000000	1	Tafa1
0.0000000	0.6502581	1.000	0.704	0.0000000	1	Tenm1
0.0000000	0.5972545	1.000	0.678	0.0000000	1	Ntng1
0.0000000	0.5949014	1.000	0.695	0.0000000	1	Rnf220
0.0000000	0.5871276	0.976	0.670	0.0000000	1	Shisa9
0.0000000	0.5609202	0.904	0.420	0.0000000	1	Thsd7b
0.0000000	0.5595962	0.994	0.502	0.0000000	1	Samd5
0.0000000	0.5470635	0.904	0.299	0.0000000	1	Gm48749
0.0000000	0.5171596	0.988	0.621	0.0000000	1	Cntnap5a
0.0000000	0.5032538	0.873	0.258	0.0000000	1	Gm32647
0.0000000	0.4864269	0.994	0.610	0.0000000	1	Tox
0.0000000	0.4645483	0.855	0.285	0.0000000	1	Trhde
0.0000000	0.4464085	0.988	0.695	0.0000000	1	Egfem1
0.0000000	0.4429732	1.000	0.756	0.0000000	1	Cntnap2
0.0000000	0.4409920	1.000	0.878	0.0000000	1	Ptprd
0.0000000	0.4356482	1.000	0.773	0.0000000	1	Grm7
0.0000000	0.4331818	1.000	0.654	0.0000000	1	Arpp21
0.0000000	0.4261508	0.940	0.397	0.0000000	1	Foxp2
0.0000000	0.4191931	0.994	0.868	0.0000000	1	Grik2
0.0000000	0.4147323	0.880	0.437	0.0000000	1	Cdh6
0.0000000	1.2753468	0.785	0.040	0.0000000	2	Cfap299
0.0000000	1.2557930	0.761	0.023	0.0000000	2	Dnah12
0.0000000	1.1597560	0.773	0.104	0.0000000	2	Adamts20
0.0000000	1.0796429	0.755	0.039	0.0000000	2	Dnah6
0.0000000	1.0502595	0.822	0.095	0.0000000	2	Cfap54
0.0000000	1.0298384	0.810	0.118	0.0000000	2	Spag16
0.0000000	1.0196588	0.804	0.072	0.0000000	2	Ccdc162
0.0000000	1.0130810	0.791	0.032	0.0000000	2	Hydin
0.0000000	1.0021477	0.767	0.054	0.0000000	2	Cfap61
0.0000000	1.0017248	0.804	0.056	0.0000000	2	Rgs22
0.0000000	0.9974433	0.791	0.069	0.0000000	2	Cfap44
0.0000000	0.9863835	0.847	0.103	0.0000000	2	Gm973
0.0000000	0.9656747	0.791	0.049	0.0000000	2	Spef2
0.0000000	0.9587074	0.761	0.051	0.0000000	2	Ak7
0.0000000	0.9536831	0.791	0.024	0.0000000	2	Ak9
0.0000000	0.9531191	0.969	0.884	0.0000000	2	Syne1
0.0000000	0.9518822	0.810	0.059	0.0000000	2	Kif6
0.0000000	0.9506150	0.798	0.132	0.0000000	2	Dnah9
0.0000000	0.9440670	0.779	0.037	0.0000000	2	Dnah11
0.0000000	0.9041702	0.761	0.026	0.0000000	2	Cfap65
0.0000000	0.5608464	0.810	0.333	0.0000000	3	Unc5d
0.0000000	0.5580631	0.866	0.520	0.0000000	3	Cdh18
0.0000000	0.4651351	0.866	0.335	0.0000000	3	Tafa2
0.0000000	0.4597091	0.789	0.312	0.0000000	3	Gm15398
0.0000000	0.4452701	0.915	0.682	0.0000000	3	Galntl6
0.0000000	0.4450124	0.873	0.232	0.0000000	3	Dlgap2
0.0000000	0.4358118	0.979	0.741	0.0000000	3	Lrrtm4
0.0000000	0.4260853	0.873	0.363	0.0000000	3	Gm26871
0.0000480	0.4250104	0.535	0.419	0.9930575	3	Adarb2
0.0000000	0.4097539	0.873	0.488	0.0000000	3	Gria1
0.0000000	0.4029539	0.958	0.718	0.0000000	3	Lingo2
0.0000000	0.4002465	0.965	0.801	0.0000000	3	Ralyl
0.0000000	0.3943962	1.000	0.832	0.0000000	3	Ahi1
0.0000000	0.3943376	1.000	0.766	0.0000000	3	Dlg2
0.0000000	0.3921796	0.831	0.318	0.0000000	3	Grin2a
0.0000000	0.3850609	0.810	0.430	0.0000000	3	Grm8
0.0000000	0.3732597	0.979	0.648	0.0000000	3	A230006K03Rik
0.0000000	0.3696774	0.979	0.784	0.0000000	3	Dab1
0.0000000	0.3531083	0.683	0.121	0.0000000	3	B230217J21Rik
0.0000000	0.3444115	0.908	0.638	0.0000000	3	Cntnap5a
0.0000000	0.9047788	0.935	0.311	0.0000000	4	Gm3764
0.0000000	0.8179890	0.971	0.628	0.0000000	4	Ptprz1
0.0000000	0.7988612	0.949	0.429	0.0000000	4	Slc4a4
0.0000000	0.7099902	0.971	0.734	0.0000000	4	Npas3
0.0000000	0.6853820	0.928	0.665	0.0000000	4	Luzp2
0.0000000	0.6719878	0.928	0.408	0.0000000	4	Gm48747
0.0000000	0.6436592	0.790	0.180	0.0000000	4	Slc6a11
0.0000000	0.6110417	0.899	0.374	0.0000000	4	Nhsl1
0.0000000	0.6106302	0.935	0.639	0.0000000	4	Gpc5
0.0000000	0.6049856	0.935	0.553	0.0000000	4	Trim9
0.0000000	0.5907997	1.000	0.733	0.0000000	4	Qk
0.0000000	0.5708835	0.841	0.193	0.0000000	4	Lrig1
0.0000000	0.5577725	0.928	0.508	0.0000000	4	Wdr17
0.0000000	0.5505430	0.957	0.648	0.0000000	4	Grm3
0.0000000	0.5466775	0.971	0.575	0.0000000	4	Ptn
0.0000000	0.5409534	0.906	0.492	0.0000000	4	Slc1a2
0.0000000	0.5363683	0.790	0.168	0.0000000	4	Bmpr1b
0.0000000	0.5356458	0.848	0.261	0.0000000	4	Plpp3
0.0000000	0.5292591	0.877	0.237	0.0000000	4	Apoe
0.0000000	0.5166935	0.717	0.141	0.0000000	4	Pla2g7
0.0000000	0.4387974	0.991	0.896	0.0000000	5	Gm42418
0.0000012	0.3672638	0.909	0.844	0.0239738	5	Gm26917
0.0000000	0.3635542	0.682	0.455	0.0000481	5	Cdh4
0.0000000	0.3596761	0.909	0.556	0.0000000	5	Cmss1
0.0000000	0.3509651	0.836	0.481	0.0000000	5	Rbfox3
0.0000000	0.3420507	0.727	0.390	0.0000000	5	Gm26871
0.0000000	0.3377407	0.845	0.534	0.0000000	5	Nxph1
0.0000000	0.3137945	0.791	0.549	0.0000000	5	Gpi1
0.0000000	0.3093742	0.627	0.271	0.0000000	5	Dlgap2
0.0000000	0.2992671	0.982	0.862	0.0000000	5	Camta1
0.0000000	0.2979735	1.000	0.907	0.0000000	5	Meg3
0.0000000	0.2919796	0.991	0.801	0.0000000	5	Nkain2
0.0000000	0.2909660	0.955	0.695	0.0000000	5	Celf4
0.0000000	0.2830124	0.836	0.580	0.0000001	5	Klhl29
0.0000000	0.2805466	0.955	0.792	0.0000000	5	Dab1
0.0000000	0.2784120	0.873	0.528	0.0000000	5	Cdh18
0.0000000	0.2761471	0.909	0.637	0.0000000	5	Usp29
0.0000000	0.2734255	0.955	0.737	0.0000002	5	Schip1
0.0000019	0.2646295	0.527	0.318	0.0401717	5	Gm32647
0.0000000	0.2646166	0.991	0.825	0.0000000	5	Ptprn2
0.0000000	1.3287492	0.667	0.033	0.0000000	6	Ptgds
0.0000000	1.1001808	0.933	0.048	0.0000000	6	Ranbp3l
0.0000000	1.0231247	0.956	0.883	0.0000000	6	Trpm3
0.0000000	0.9147659	0.933	0.225	0.0000000	6	Adam12
0.0000000	0.8879254	0.867	0.056	0.0000000	6	Slc6a20a
0.0000000	0.8669011	0.844	0.109	0.0000000	6	Adamts12
0.0000000	0.8499882	0.878	0.165	0.0000000	6	Sidt1
0.0000000	0.7909713	0.767	0.144	0.0000000	6	Bmp6
0.0000000	0.7883417	0.956	0.310	0.0000000	6	Atp1a2
0.0000000	0.7644435	0.900	0.016	0.0000000	6	Slc6a13
0.0000000	0.7474819	0.922	0.292	0.0000000	6	Bicc1
0.0000000	0.7271937	0.744	0.183	0.0000000	6	Slc7a11
0.0000000	0.7090860	0.867	0.701	0.0000000	6	Nnat
0.0000000	0.6924188	0.767	0.145	0.0000000	6	Lrmda
0.0000000	0.6884408	0.844	0.109	0.0000000	6	Cped1
0.0000000	0.6758639	0.878	0.146	0.0000000	6	Tmtc4
0.0000000	0.6605440	0.844	0.268	0.0000000	6	Sned1
0.0000000	0.6390464	0.911	0.105	0.0000000	6	Arhgap29
0.0000000	0.6233387	0.878	0.231	0.0000000	6	Pdzrn3
0.0000000	0.5929585	0.822	0.138	0.0000000	6	Colec12
0.0000000	0.9487361	0.958	0.304	0.0000000	7	6330411D24Rik
0.0000000	0.8401545	0.972	0.336	0.0000000	7	Pex5l
0.0000000	0.6446468	0.986	0.511	0.0000000	7	Ryr3
0.0000000	0.6350898	0.986	0.525	0.0000000	7	Spock1
0.0000000	0.6163441	0.917	0.404	0.0000000	7	Adarb1
0.0000000	0.5945646	1.000	0.783	0.0000000	7	Hs6st3
0.0000000	0.5938236	0.861	0.163	0.0000000	7	4930419G24Rik
0.0000000	0.5647983	0.986	0.521	0.0000000	7	Edil3
0.0000000	0.5421220	0.944	0.233	0.0000000	7	Ptpn3
0.0000000	0.5377003	1.000	0.518	0.0000000	7	Zmat4
0.0000000	0.5373895	0.944	0.416	0.0000000	7	Cntn3
0.0000000	0.5326070	1.000	0.696	0.0000000	7	Frmpd4
0.0000000	0.5131547	1.000	0.617	0.0000000	7	Zfp804b
0.0000000	0.5076611	1.000	0.655	0.0000000	7	Unc13c
0.0000000	0.5075011	0.972	0.489	0.0000000	7	Camk4
0.0000000	0.4950522	0.986	0.671	0.0000000	7	Cacnb4
0.0000000	0.4940987	0.972	0.421	0.0000000	7	Epb41l4b
0.0000000	0.4919531	1.000	0.879	0.0000000	7	Prickle2
0.0000000	0.4915512	0.944	0.265	0.0000000	7	Fras1
0.0000000	0.4912050	0.958	0.587	0.0000000	7	Pak7
0.0000000	0.9315875	0.957	0.429	0.0000000	8	Hs3st4
0.0000000	0.8830266	1.000	0.797	0.0000000	8	Lrrc4c
0.0000000	0.8702088	0.814	0.196	0.0000000	8	4930445B16Rik
0.0000000	0.8293413	1.000	0.698	0.0000000	8	Dpp10
0.0000000	0.8184406	1.000	0.691	0.0000000	8	Galntl6
0.0000000	0.7285249	0.914	0.279	0.0000000	8	Kcnmb2
0.0000000	0.6817534	0.929	0.150	0.0000000	8	Gm15155
0.0000000	0.6707176	0.886	0.192	0.0000000	8	Meis2
0.0000000	0.6624833	0.971	0.813	0.0000000	8	Asic2
0.0000000	0.6501771	0.943	0.288	0.0000000	8	Ubash3b
0.0000000	0.6447586	0.971	0.781	0.0000000	8	Nrg1
0.0000000	0.6310500	0.871	0.071	0.0000000	8	Gad1
0.0000000	0.6099125	0.957	0.096	0.0000000	8	Gad2
0.0000000	0.5988190	0.986	0.663	0.0000000	8	Grm3
0.0000000	0.5938922	0.729	0.148	0.0000000	8	Sema3e
0.0000000	0.5905362	0.886	0.132	0.0000000	8	Ptchd1
0.0000000	0.5854086	1.000	0.880	0.0000000	8	Nrxn3
0.0000000	0.5853860	0.971	0.432	0.0000000	8	Fign
0.0000000	0.5767985	0.871	0.336	0.0000000	8	Ak5
0.0000000	0.5755538	0.986	0.610	0.0000000	8	Zfp804a
0.0000000	1.3227119	0.984	0.053	0.0000000	9	Flt1
0.0000000	1.1177770	0.984	0.104	0.0000000	9	Mecom
0.0000000	0.9762405	0.984	0.210	0.0000000	9	Dach1
0.0000000	0.9535095	0.952	0.149	0.0000000	9	Slc7a1
0.0000000	0.9290368	0.935	0.053	0.0000000	9	Ets1
0.0000000	0.9213625	0.887	0.116	0.0000000	9	Slco1c1
0.0000000	0.9055905	0.935	0.069	0.0000000	9	Slc7a5
0.0000000	0.8841938	0.919	0.015	0.0000000	9	Egfl7
0.0000000	0.8678912	0.935	0.030	0.0000000	9	Ptprb
0.0000000	0.8581400	0.919	0.058	0.0000000	9	Fli1
0.0000000	0.8457729	0.968	0.230	0.0000000	9	Ccdc141
0.0000000	0.8305455	0.871	0.191	0.0000000	9	Hmcn1
0.0000000	0.8284534	0.903	0.030	0.0000000	9	Adgrl4
0.0000000	0.8179876	0.968	0.368	0.0000000	9	Slc38a2
0.0000000	0.7916615	1.000	0.311	0.0000000	9	Myo10
0.0000000	0.7810337	0.823	0.055	0.0000000	9	Apcdd1
0.0000000	0.7741358	0.952	0.286	0.0000000	9	Rapgef5
0.0000000	0.7670584	1.000	0.632	0.0000000	9	Ptprm
0.0000000	0.7670278	0.887	0.226	0.0000000	9	Tfrc
0.0000000	0.7660856	0.887	0.109	0.0000000	9	Lama4
0.0000000	0.7966165	0.869	0.163	0.0000000	10	4930588A03Rik
0.0000000	0.7900272	0.918	0.123	0.0000000	10	Gm38505
0.0000000	0.7892775	0.984	0.469	0.0000000	10	Pcdh15
0.0000000	0.7020805	1.000	0.441	0.0000000	10	Sox6
0.0000000	0.6957390	0.984	0.527	0.0000000	10	6030443J06Rik
0.0000000	0.6718450	0.836	0.129	0.0000000	10	Pdgfra
0.0000000	0.6512600	0.984	0.551	0.0000000	10	Sox2ot
0.0000000	0.6372609	0.967	0.471	0.0000000	10	Lhfpl3
0.0000000	0.6270686	0.918	0.247	0.0000000	10	Dscaml1
0.0000000	0.5654791	0.967	0.500	0.0000000	10	Xylt1
0.0000000	0.5652314	0.951	0.514	0.0000000	10	Epn2
0.0000000	0.5536460	0.820	0.083	0.0000000	10	Sox10
0.0000000	0.5288474	0.918	0.309	0.0000000	10	Mir9-3hg
0.0000000	0.5160463	0.984	0.554	0.0000000	10	Pcdh11x
0.0000000	0.4990924	1.000	0.708	0.0000000	10	Sgcd
0.0000000	0.4937250	0.836	0.244	0.0000000	10	Megf11
0.0000000	0.4869009	1.000	0.668	0.0000000	10	Dcc
0.0000000	0.4563497	0.918	0.431	0.0000000	10	Tnr
0.0000000	0.4527685	1.000	0.539	0.0000000	10	Nxph1
0.0000000	0.4519295	0.820	0.224	0.0000000	10	Arhgef3
0.0000000	1.0241256	0.500	0.174	0.0000000	11	Mbp
0.0000002	0.8482169	0.379	0.153	0.0048912	11	9630013A20Rik
0.0000000	0.8172537	0.328	0.092	0.0000033	11	Plp1
0.0000000	0.7469378	0.310	0.055	0.0000000	11	St18
0.0000000	0.7263954	0.517	0.087	0.0000000	11	Npsr1
0.0000000	0.7020544	0.983	0.839	0.0000000	11	Kcnip4
0.0000000	0.6602253	0.707	0.219	0.0000000	11	Pcsk5
0.0000126	0.6549541	0.293	0.123	0.2607246	11	Cnksr3
0.0000000	0.5952941	0.862	0.627	0.0000002	11	Zfp804b
0.0000000	0.5662574	0.259	0.006	0.0000000	11	Mobp
0.0000000	0.5583427	0.690	0.193	0.0000000	11	Prox1
0.0000000	0.5540357	0.741	0.355	0.0000000	11	Cntnap5c
0.0000004	0.5495114	0.828	0.631	0.0088573	11	Nfasc
0.0000000	0.5457659	0.828	0.692	0.0001353	11	Brinp3
0.0000000	0.5030720	0.293	0.020	0.0000000	11	Mag
0.0003653	0.4724393	0.759	0.648	1.0000000	11	Tmeff2
0.0000000	0.4713310	0.948	0.626	0.0000000	11	Gm20754
0.0000010	0.4706809	0.431	0.196	0.0199178	11	Cemip2
0.0000000	0.4487399	0.862	0.672	0.0000024	11	Rgs6
0.0000000	0.4460125	0.741	0.349	0.0000000	11	Prr16
0.0000041	0.4743175	0.610	0.355	0.0839666	12	Gm15398
0.0000000	0.4619596	0.610	0.304	0.0010143	12	Slit3
0.0000979	0.4598898	0.488	0.267	1.0000000	12	Gm45321
0.0000000	0.4584631	0.683	0.308	0.0000188	12	Stxbp5l
0.0000015	0.4039485	0.610	0.315	0.0300932	12	4930555F03Rik
0.0000000	0.4024779	0.976	0.760	0.0000028	12	Lrrtm4
0.0021607	0.3880570	0.878	0.791	1.0000000	12	Unc5c
0.0000293	0.3812647	0.390	0.180	0.6059771	12	Gfra1
0.0001393	0.3810508	0.561	0.325	1.0000000	12	Kctd8
0.0000000	0.3777165	0.854	0.560	0.0005099	12	Gpi1
0.0000000	0.3641411	1.000	0.784	0.0000130	12	Nrg1
0.0009473	0.3494685	1.000	0.892	1.0000000	12	Kcnma1
0.0002867	0.3432635	0.268	0.101	1.0000000	12	Npsr1
0.0000532	0.3364966	0.683	0.409	1.0000000	12	C130073E24Rik
0.0000004	0.3288227	0.732	0.411	0.0073581	12	Unc13a
0.0000026	0.3222958	0.488	0.224	0.0548183	12	Gm2516
0.0000006	0.3211624	0.829	0.518	0.0128026	12	Cdh8
0.0000000	0.3122612	0.732	0.362	0.0009475	12	Grin2a
0.0000000	0.3033823	0.659	0.289	0.0002781	12	Cntnap4
0.0000000	0.3031174	0.415	0.128	0.0002314	12	Nefm
0.0000000	0.9449937	0.647	0.144	0.0000000	13	Bcas1
0.0000000	0.9409910	0.706	0.447	0.0002074	13	Tnr
0.0000000	0.7218463	0.559	0.059	0.0000000	13	Bcas1os2
0.0000000	0.6971670	0.647	0.215	0.0000000	13	Tns3
0.0000000	0.6352373	0.882	0.746	0.0010193	13	Fyn
0.0000000	0.6340517	0.824	0.172	0.0000000	13	Mbp
0.0000000	0.6121338	0.676	0.300	0.0000082	13	Itpr2
0.0000045	0.5947798	0.647	0.457	0.0934544	13	Epb41l2
0.0000000	0.5885908	0.618	0.034	0.0000000	13	Enpp6
0.0000087	0.5688640	0.882	0.821	0.1789699	13	Opcml
0.0000000	0.5635389	0.824	0.372	0.0000001	13	Ust
0.0000000	0.5545936	0.529	0.054	0.0000000	13	St18
0.0000618	0.5448275	0.559	0.330	1.0000000	13	Gm32647
0.0000002	0.5375124	0.824	0.590	0.0040599	13	9530059O14Rik
0.0000000	0.5357745	1.000	0.651	0.0000001	13	Tmem108
0.0000063	0.5283072	0.882	0.633	0.1306024	13	Nfasc
0.0000000	0.5262521	0.735	0.275	0.0000000	13	Sirt2
0.0000371	0.5146112	0.588	0.360	0.7666722	13	Prr16
0.0000000	0.5041546	0.588	0.244	0.0002005	13	Abtb2
0.0000030	0.4999108	1.000	0.927	0.0617428	13	Frmd4a
0.0000000	1.3744411	1.000	0.080	0.0000000	14	Bnc2
0.0000000	0.9355881	1.000	0.463	0.0000000	14	Fbxl7
0.0000000	0.9272111	0.867	0.059	0.0000000	14	Adamtsl3
0.0000000	0.9157545	0.933	0.158	0.0000000	14	Thsd4
0.0000000	0.9076270	1.000	0.713	0.0000000	14	Foxp1
0.0000000	0.8969509	1.000	0.383	0.0000000	14	Slc38a2
0.0000000	0.8967671	0.933	0.320	0.0000000	14	Nr3c2
0.0000000	0.8908707	0.900	0.546	0.0000000	14	Slc4a10
0.0000000	0.8503342	0.933	0.065	0.0000000	14	Trabd2b
0.0000000	0.8333417	0.833	0.312	0.0000000	14	Col25a1
0.0000000	0.8118453	0.833	0.183	0.0000000	14	Itgbl1
0.0000000	0.7796978	0.933	0.357	0.0000000	14	Gulp1
0.0000000	0.7562549	0.933	0.115	0.0000000	14	Eya2
0.0000000	0.7426407	0.967	0.452	0.0000000	14	Tmtc1
0.0000000	0.7338606	0.867	0.259	0.0000000	14	Sh3pxd2a
0.0000000	0.7283485	1.000	0.645	0.0000000	14	Tmeff2
0.0000000	0.7180718	0.800	0.096	0.0000000	14	Dock5
0.0000000	0.7033469	0.967	0.205	0.0000000	14	Hmcn1
0.0000000	0.7017159	0.967	0.312	0.0000000	14	Nxn
0.0000000	0.6722369	0.567	0.067	0.0000000	14	Crispld1
0.0000000	1.6692281	0.900	0.174	0.0000000	15	Htr2c
0.0000000	1.4475659	1.000	0.079	0.0000000	15	Ttr
0.0000000	0.9520627	1.000	0.542	0.0000000	15	Wdr17
0.0000000	0.9413800	0.800	0.026	0.0000000	15	Gmnc
0.0000000	0.9072979	0.900	0.190	0.0000000	15	Enpp2
0.0000000	0.8979889	0.967	0.065	0.0000000	15	Rbm47
0.0000000	0.8974342	0.900	0.234	0.0000000	15	Vat1l
0.0000000	0.8787920	0.933	0.163	0.0000000	15	Sulf1
0.0000000	0.8685294	0.933	0.192	0.0000000	15	Otx2os1
0.0000000	0.8414672	0.967	0.673	0.0000000	15	Stk39
0.0000000	0.8107865	1.000	0.885	0.0000000	15	Trpm3
0.0000000	0.7751631	0.900	0.330	0.0000000	15	Nhsl2
0.0000000	0.7668973	0.967	0.242	0.0000000	15	C330002G04Rik
0.0000000	0.7504541	0.967	0.383	0.0000000	15	Itpr1
0.0000000	0.7147429	1.000	0.706	0.0000000	15	Frmpd4
0.0000000	0.7066112	0.867	0.175	0.0000000	15	Prdm16
0.0000000	0.7000165	1.000	0.589	0.0000000	15	Rfx3
0.0000000	0.6970875	0.800	0.254	0.0000000	15	Cab39l
0.0000000	0.6945247	0.800	0.225	0.0000000	15	Atp2b3
0.0000000	0.6784167	0.933	0.169	0.0000000	15	Slc16a2
0.0000000	0.6093348	0.964	0.428	0.0000000	16	Pcsk2
0.0000000	0.5823304	0.857	0.248	0.0000000	16	Stxbp6
0.0000000	0.5686065	1.000	0.380	0.0000000	16	Lef1
0.0000000	0.5294625	1.000	0.713	0.0000000	16	Ntng1
0.0000000	0.5060755	0.964	0.434	0.0000000	16	Cntn3
0.0000000	0.5014865	1.000	0.302	0.0000000	16	Cpne7
0.0000000	0.4788813	0.964	0.199	0.0000000	16	Prox1
0.0000000	0.4644870	0.893	0.422	0.0000100	16	Adarb2
0.0000000	0.4626738	1.000	0.527	0.0000000	16	Ryr3
0.0000037	0.4577550	0.643	0.318	0.0757812	16	4930555F03Rik
0.0000000	0.4526475	1.000	0.782	0.0000000	16	Cntnap2
0.0000000	0.4508588	0.964	0.739	0.0000034	16	Lingo2
0.0000000	0.4413095	0.964	0.766	0.0000730	16	Mgat4c
0.0000000	0.4407631	0.964	0.723	0.0000254	16	Sox5
0.0000000	0.4366980	0.893	0.328	0.0000000	16	6330411D24Rik
0.0000000	0.4363868	0.679	0.189	0.0000000	16	Sox5os4
0.0000000	0.4347882	0.750	0.125	0.0000000	16	Rxfp1
0.0000000	0.4340566	1.000	0.534	0.0000009	16	Zmat4
0.0000000	0.4294471	1.000	0.753	0.0000001	16	Syt1
0.0000208	0.4211794	0.750	0.609	0.4296014	16	Rorb
0.0000000	0.8169323	1.000	0.632	0.0000000	17	Zfp804b
0.0000000	0.7913766	1.000	0.608	0.0000000	17	Kcnq5
0.0000000	0.7854327	1.000	0.738	0.0000002	17	Cntn5
0.0000000	0.7255555	1.000	0.842	0.0000001	17	Kcnip4
0.0000000	0.7024320	1.000	0.785	0.0000000	17	Tenm2
0.0000001	0.6813306	0.952	0.767	0.0017939	17	Mgat4c
0.0000000	0.6570441	1.000	0.751	0.0000010	17	Schip1
0.0000000	0.5996349	0.905	0.377	0.0000001	17	Epha3
0.0000000	0.5933017	1.000	0.819	0.0000023	17	Opcml
0.0000000	0.5851687	1.000	0.635	0.0000007	17	Gm20754
0.0000000	0.5699112	0.857	0.304	0.0000004	17	Slit3
0.0000000	0.5638579	0.952	0.764	0.0001885	17	Lrrtm4
0.0000000	0.5532614	0.952	0.868	0.0000636	17	Slc24a3
0.0000000	0.5432472	1.000	0.598	0.0000004	17	Gabra2
0.0000000	0.5378526	0.952	0.434	0.0000181	17	4930509J09Rik
0.0000001	0.5333226	1.000	0.724	0.0014001	17	Sox5
0.0000000	0.5284057	0.524	0.100	0.0000015	17	Npsr1
0.0000000	0.5197057	1.000	0.887	0.0000022	17	Rims2
0.0000000	0.5170743	0.905	0.669	0.0000508	17	Kcnc2
0.0000000	0.5093695	1.000	0.520	0.0000015	17	Cdh8

Past versions of pl_clst_dotplot-1.png

Version	Author	Date
3b155ad	Evgenii O. Tretiakov	2023-03-05
8bf2341	Evgenii O. Tretiakov	2023-03-04
3b19c24	Evgenii O. Tretiakov	2023-02-02

markers.logreg %>%
    group_by(cluster) %>%
    top_n(n = 10, wt = avg_log10FC) -> top10
DoHeatmap(combined.srt, features = top10$gene) + NoLegend()

Past versions of pl-heatmap-1.png

Version	Author	Date
3b155ad	Evgenii O. Tretiakov	2023-03-05
3b19c24	Evgenii O. Tretiakov	2023-02-02
c82f5c0	Evgenii O. Tretiakov	2023-01-25

It seems that we should split datasets despite the fact that they’re derived from the same experiment. It should help us to get better embeddings and clusters.

Separate analysis

DefaultAssay(combined.srt) <- "RNA"
combined.srt$comb_clstr2 <- Idents(combined.srt)
combined.srt@meta.data <-
  combined.srt@meta.data %>%
  select(nCount_RNA,
         nFeature_RNA,
         log10GenesPerUMI,
         percent_mito,
         percent_ribo,
         percent_hb,
         var_regex,
         S.Score,
         G2M.Score,
         log_prob_doublet,
         orig.ident,
         comb_clstr1,
         comb_clstr2,
         QC,
         cell_name)
srt.list <- SplitObject(combined.srt,
                        split.by = "orig.ident")
THP7  <- srt.list[["THP7"]]
Pr5P7 <- srt.list[["Pr5P7"]]

Ventrobasal thalamus

plan("sequential")
invisible(gc())
plan("multisession", workers = 4)
n_cores <- 4
options(future.globals.maxSize = 8000 * 1024^2)

n_pcs <- 100
c(THP7, THP7.markers.logreg, THP7.markers.mast)  %<-% DeriveKTree(THP7)

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Idents(THP7) <- "k_tree"
THP7$sep_clstr <- Idents(THP7)

THP7.markers.logreg %>%
  group_by(cluster) %>%
  slice_max(n = 20, order_by = avg_log10FC) %>%
  kable("html") %>%
  kable_material(
    bootstrap_options = c("bordered",
                          "condensed",
                          "responsive",
                          "striped"),
    position = "left",
    font_size = 14
  )

p_val	avg_log10FC	pct.1	pct.2	p_val_adj	cluster	gene
0	0.7640263	0.950	0.664	0	1	Kcnip4
0	0.7242854	0.940	0.440	0	1	Tenm2
0	0.6964606	0.855	0.418	0	1	Brinp3
0	0.6258743	0.840	0.433	0	1	Cntn5
0	0.6240539	0.840	0.312	0	1	Gm20754
0	0.6158324	0.810	0.401	0	1	Kcnq5
0	0.6118205	0.890	0.444	0	1	Lrrtm4
0	0.5953479	0.730	0.291	0	1	Pcdh15
0	0.5813549	0.365	0.069	0	1	Npsr1
0	0.5753793	0.730	0.222	0	1	Tafa2
0	0.5498454	0.805	0.446	0	1	Dcc
0	0.5455645	0.945	0.556	0	1	Opcml
0	0.5315668	0.805	0.532	0	1	Zfp804b
0	0.5139852	0.865	0.347	0	1	Khdrbs2
0	0.5019838	0.795	0.388	0	1	Gria1
0	0.5018060	0.915	0.461	0	1	Sntg1
0	0.4986099	0.625	0.356	0	1	Nxph1
0	0.4919916	0.670	0.244	0	1	4930509J09Rik
0	0.4905154	0.795	0.263	0	1	Pcsk2
0	0.4835379	0.700	0.321	0	1	Prr16
0	1.4353259	0.959	0.107	0	2	Cfap299
0	1.4343721	0.959	0.055	0	2	Dnah12
0	1.3442395	0.951	0.253	0	2	Adamts20
0	1.2221474	0.951	0.124	0	2	Dnah6
0	1.2009083	0.951	0.137	0	2	Rgs22
0	1.1988035	0.951	0.103	0	2	Hydin
0	1.1947244	0.959	0.164	0	2	Cfap61
0	1.1822099	0.959	0.098	0	2	Ak7
0	1.1679765	0.967	0.234	0	2	Cfap54
0	1.1573976	0.934	0.138	0	2	Cfap44
0	1.1542253	0.959	0.214	0	2	Ccdc162
0	1.1378544	0.959	0.085	0	2	Ak9
0	1.1361903	0.967	0.162	0	2	Kif6
0	1.1337913	0.959	0.148	0	2	Spef2
0	1.1293963	0.959	0.232	0	2	Gm973
0	1.1273505	0.959	0.234	0	2	Dnah9
0	1.1142377	0.951	0.292	0	2	Spag16
0	1.1087591	0.967	0.077	0	2	Cfap65
0	1.1018309	0.918	0.054	0	2	Spag17
0	1.1011970	0.951	0.111	0	2	Dnah11
0	1.1422599	0.916	0.163	0	3	6330411D24Rik
0	0.7427098	0.935	0.309	0	3	Pex5l
0	0.7227560	0.963	0.438	0	3	Ntng1
0	0.6957097	0.963	0.418	0	3	Arpp21
0	0.6897156	0.944	0.397	0	3	Ryr3
0	0.6812756	0.888	0.336	0	3	Spock1
0	0.6269982	0.907	0.381	0	3	Edil3
0	0.6125840	0.935	0.382	0	3	Camk4
0	0.5941741	0.953	0.659	0	3	Hs6st3
0	0.5767449	0.832	0.275	0	3	Adarb2
0	0.5651408	0.953	0.539	0	3	Lrrc7
0	0.5642189	0.944	0.400	0	3	Zmat4
0	0.5489119	0.935	0.408	0	3	Hs3st5
0	0.5474077	0.963	0.515	0	3	Syt1
0	0.5442789	0.841	0.160	0	3	4930419G24Rik
0	0.5364514	0.888	0.348	0	3	Cntn3
0	0.5343979	0.907	0.481	0	3	Adarb1
0	0.5270442	0.935	0.332	0	3	Nell1
0	0.5266853	0.963	0.508	0	3	Cntnap2
0	0.5239474	0.832	0.233	0	3	Gm48749
0	1.0601642	0.974	0.323	0	4	Gm3764
0	1.0035949	0.974	0.491	0	4	Slc4a4
0	0.8680456	0.987	0.653	0	4	Ptprz1
0	0.8458438	0.921	0.464	0	4	Gm48747
0	0.8097657	0.921	0.170	0	4	Bmpr1b
0	0.7941314	0.803	0.197	0	4	Slc6a11
0	0.7930614	0.934	0.313	0	4	Apoe
0	0.7570380	0.868	0.558	0	4	Luzp2
0	0.7566663	0.987	0.315	0	4	Plpp3
0	0.7471816	0.803	0.473	0	4	Gpc5
0	0.7341970	0.908	0.264	0	4	Lrig1
0	0.7309734	0.842	0.216	0	4	Pla2g7
0	0.7302177	0.921	0.614	0	4	Trim9
0	0.7299330	0.711	0.162	0	4	Tnc
0	0.7265035	0.974	0.594	0	4	Ptn
0	0.7001316	0.829	0.158	0	4	Egfr
0	0.6932982	0.987	0.650	0	4	Gpm6b
0	0.6907836	1.000	0.726	0	4	Npas3
0	0.6835288	0.855	0.228	0	4	Cdh20
0	0.6759784	1.000	0.774	0	4	Qk
0	1.2233431	0.585	0.070	0	5	Flt1
0	1.1530867	0.938	0.437	0	5	Dlc1
0	1.1040712	0.831	0.132	0	5	Ebf1
0	0.9695445	0.385	0.080	0	5	Atp13a5
0	0.9447831	0.646	0.050	0	5	Itga1
0	0.9233525	0.723	0.115	0	5	Cped1
0	0.9164770	0.692	0.052	0	5	Ets1
0	0.9156116	0.538	0.139	0	5	Adamts12
0	0.9017114	0.492	0.187	0	5	Mecom
0	0.8743776	0.708	0.262	0	5	Dach1
0	0.8571195	0.615	0.262	0	5	Adam12
0	0.8482186	0.738	0.077	0	5	Lama4
0	0.8392114	0.600	0.242	0	5	Slc7a1
0	0.8326942	0.646	0.384	0	5	Rapgef5
0	0.8221493	0.523	0.025	0	5	Rgs5
0	0.8115852	0.615	0.062	0	5	Fli1
0	0.7913175	0.462	0.027	0	5	Egfl7
0	0.7888761	0.569	0.247	0	5	Hmcn1
0	0.7753925	0.677	0.526	0	5	Slc38a2
0	0.7750763	0.538	0.033	0	5	Morrbid
0	1.0385184	0.940	0.312	0	6	Hs3st4
0	0.9788616	0.970	0.531	0	6	Dpp10
0	0.9569103	1.000	0.668	0	6	Lrrc4c
0	0.9222581	0.985	0.503	0	6	Galntl6
0	0.9116642	0.821	0.198	0	6	4930445B16Rik
0	0.8126178	0.970	0.606	0	6	Nrg1
0	0.7723695	0.910	0.183	0	6	Gm15155
0	0.7621331	0.970	0.737	0	6	Asic2
0	0.7432652	0.881	0.315	0	6	Kcnmb2
0	0.7145113	1.000	0.467	0	6	Sgcz
0	0.7131163	0.925	0.338	0	6	Ubash3b
0	0.7086752	0.940	0.377	0	6	Fign
0	0.6962755	0.866	0.243	0	6	Meis2
0	0.6839052	0.940	0.420	0	6	Galnt13
0	0.6736126	0.806	0.164	0	6	D030068K23Rik
0	0.6671779	1.000	0.757	0	6	Nrxn3
0	0.6667063	0.955	0.451	0	6	Zfp804a
0	0.6661510	0.940	0.104	0	6	Gad2
0	0.6614709	0.881	0.104	0	6	Gad1
0	0.6524320	0.881	0.367	0	6	Ak5
0	1.9640315	1.000	0.140	0	7	Htr2c
0	1.4597121	1.000	0.177	0	7	Ttr
0	1.2423272	0.926	0.052	0	7	Gmnc
0	1.1559174	1.000	0.253	0	7	Vat1l
0	1.1478074	1.000	0.769	0	7	Trpm3
0	1.1352757	1.000	0.143	0	7	Sulf1
0	1.1263682	1.000	0.089	0	7	Rbm47
0	1.0851914	0.963	0.253	0	7	Enpp2
0	1.0289727	1.000	0.322	0	7	Otx2os1
0	1.0243521	0.963	0.656	0	7	Stk39
0	1.0196880	1.000	0.336	0	7	C330002G04Rik
0	1.0076501	0.963	0.378	0	7	Nhsl2
0	0.9995940	1.000	0.630	0	7	Wdr17
0	0.9747735	0.926	0.317	0	7	Atp2b3
0	0.9328334	0.815	0.046	0	7	Col8a1
0	0.9033041	0.963	0.188	0	7	Slc16a2
0	0.8731274	0.889	0.556	0	7	Slc4a10
0	0.8715270	0.778	0.198	0	7	Gm28376
0	0.8700451	0.852	0.435	0	7	Cab39l
0	0.8429674	0.926	0.297	0	7	Prdm16

THP7.markers.mast %>%
  group_by(cluster) %>%
  slice_max(n = 20, order_by = avg_log10FC) %>%
  kable("html") %>%
  kable_material(
    bootstrap_options = c("bordered",
                          "condensed",
                          "responsive",
                          "striped"),
    position = "left",
    font_size = 14
  )

p_val	avg_log10FC	pct.1	pct.2	p_val_adj	cluster	gene
0	0.7640263	0.950	0.664	0	1	Kcnip4
0	0.7242854	0.940	0.440	0	1	Tenm2
0	0.6964606	0.855	0.418	0	1	Brinp3
0	0.6258743	0.840	0.433	0	1	Cntn5
0	0.6240539	0.840	0.312	0	1	Gm20754
0	0.6158324	0.810	0.401	0	1	Kcnq5
0	0.6118205	0.890	0.444	0	1	Lrrtm4
0	0.5953479	0.730	0.291	0	1	Pcdh15
0	0.5813549	0.365	0.069	0	1	Npsr1
0	0.5753793	0.730	0.222	0	1	Tafa2
0	0.5498454	0.805	0.446	0	1	Dcc
0	0.5455645	0.945	0.556	0	1	Opcml
0	0.5315668	0.805	0.532	0	1	Zfp804b
0	0.5139852	0.865	0.347	0	1	Khdrbs2
0	0.5019838	0.795	0.388	0	1	Gria1
0	0.5018060	0.915	0.461	0	1	Sntg1
0	0.4986099	0.625	0.356	0	1	Nxph1
0	0.4919916	0.670	0.244	0	1	4930509J09Rik
0	0.4905154	0.795	0.263	0	1	Pcsk2
0	0.4835379	0.700	0.321	0	1	Prr16
0	1.4353259	0.959	0.107	0	2	Cfap299
0	1.4343721	0.959	0.055	0	2	Dnah12
0	1.3442395	0.951	0.253	0	2	Adamts20
0	1.2221474	0.951	0.124	0	2	Dnah6
0	1.2009083	0.951	0.137	0	2	Rgs22
0	1.1988035	0.951	0.103	0	2	Hydin
0	1.1947244	0.959	0.164	0	2	Cfap61
0	1.1822099	0.959	0.098	0	2	Ak7
0	1.1679765	0.967	0.234	0	2	Cfap54
0	1.1573976	0.934	0.138	0	2	Cfap44
0	1.1542253	0.959	0.214	0	2	Ccdc162
0	1.1378544	0.959	0.085	0	2	Ak9
0	1.1361903	0.967	0.162	0	2	Kif6
0	1.1337913	0.959	0.148	0	2	Spef2
0	1.1293963	0.959	0.232	0	2	Gm973
0	1.1273505	0.959	0.234	0	2	Dnah9
0	1.1142377	0.951	0.292	0	2	Spag16
0	1.1087591	0.967	0.077	0	2	Cfap65
0	1.1018309	0.918	0.054	0	2	Spag17
0	1.1011970	0.951	0.111	0	2	Dnah11
0	1.1422599	0.916	0.163	0	3	6330411D24Rik
0	0.7427098	0.935	0.309	0	3	Pex5l
0	0.7227560	0.963	0.438	0	3	Ntng1
0	0.6957097	0.963	0.418	0	3	Arpp21
0	0.6897156	0.944	0.397	0	3	Ryr3
0	0.6812756	0.888	0.336	0	3	Spock1
0	0.6269982	0.907	0.381	0	3	Edil3
0	0.6125840	0.935	0.382	0	3	Camk4
0	0.5941741	0.953	0.659	0	3	Hs6st3
0	0.5767449	0.832	0.275	0	3	Adarb2
0	0.5651408	0.953	0.539	0	3	Lrrc7
0	0.5642189	0.944	0.400	0	3	Zmat4
0	0.5489119	0.935	0.408	0	3	Hs3st5
0	0.5474077	0.963	0.515	0	3	Syt1
0	0.5442789	0.841	0.160	0	3	4930419G24Rik
0	0.5364514	0.888	0.348	0	3	Cntn3
0	0.5343979	0.907	0.481	0	3	Adarb1
0	0.5270442	0.935	0.332	0	3	Nell1
0	0.5266853	0.963	0.508	0	3	Cntnap2
0	0.5239474	0.832	0.233	0	3	Gm48749
0	1.0601642	0.974	0.323	0	4	Gm3764
0	1.0035949	0.974	0.491	0	4	Slc4a4
0	0.8680456	0.987	0.653	0	4	Ptprz1
0	0.8458438	0.921	0.464	0	4	Gm48747
0	0.8097657	0.921	0.170	0	4	Bmpr1b
0	0.7941314	0.803	0.197	0	4	Slc6a11
0	0.7930614	0.934	0.313	0	4	Apoe
0	0.7570380	0.868	0.558	0	4	Luzp2
0	0.7566663	0.987	0.315	0	4	Plpp3
0	0.7471816	0.803	0.473	0	4	Gpc5
0	0.7341970	0.908	0.264	0	4	Lrig1
0	0.7309734	0.842	0.216	0	4	Pla2g7
0	0.7302177	0.921	0.614	0	4	Trim9
0	0.7299330	0.711	0.162	0	4	Tnc
0	0.7265035	0.974	0.594	0	4	Ptn
0	0.7001316	0.829	0.158	0	4	Egfr
0	0.6932982	0.987	0.650	0	4	Gpm6b
0	0.6907836	1.000	0.726	0	4	Npas3
0	0.6835288	0.855	0.228	0	4	Cdh20
0	0.6759784	1.000	0.774	0	4	Qk
0	1.2233431	0.585	0.070	0	5	Flt1
0	1.1530867	0.938	0.437	0	5	Dlc1
0	1.1040712	0.831	0.132	0	5	Ebf1
0	0.9695445	0.385	0.080	0	5	Atp13a5
0	0.9447831	0.646	0.050	0	5	Itga1
0	0.9233525	0.723	0.115	0	5	Cped1
0	0.9164770	0.692	0.052	0	5	Ets1
0	0.9156116	0.538	0.139	0	5	Adamts12
0	0.9017114	0.492	0.187	0	5	Mecom
0	0.8743776	0.708	0.262	0	5	Dach1
0	0.8571195	0.615	0.262	0	5	Adam12
0	0.8482186	0.738	0.077	0	5	Lama4
0	0.8392114	0.600	0.242	0	5	Slc7a1
0	0.8326942	0.646	0.384	0	5	Rapgef5
0	0.8221493	0.523	0.025	0	5	Rgs5
0	0.8115852	0.615	0.062	0	5	Fli1
0	0.7913175	0.462	0.027	0	5	Egfl7
0	0.7888761	0.569	0.247	0	5	Hmcn1
0	0.7753925	0.677	0.526	0	5	Slc38a2
0	0.7750763	0.538	0.033	0	5	Morrbid
0	1.0385184	0.940	0.312	0	6	Hs3st4
0	0.9788616	0.970	0.531	0	6	Dpp10
0	0.9569103	1.000	0.668	0	6	Lrrc4c
0	0.9222581	0.985	0.503	0	6	Galntl6
0	0.9116642	0.821	0.198	0	6	4930445B16Rik
0	0.8126178	0.970	0.606	0	6	Nrg1
0	0.7723695	0.910	0.183	0	6	Gm15155
0	0.7621331	0.970	0.737	0	6	Asic2
0	0.7432652	0.881	0.315	0	6	Kcnmb2
0	0.7145113	1.000	0.467	0	6	Sgcz
0	0.7131163	0.925	0.338	0	6	Ubash3b
0	0.7086752	0.940	0.377	0	6	Fign
0	0.6962755	0.866	0.243	0	6	Meis2
0	0.6839052	0.940	0.420	0	6	Galnt13
0	0.6736126	0.806	0.164	0	6	D030068K23Rik
0	0.6671779	1.000	0.757	0	6	Nrxn3
0	0.6667063	0.955	0.451	0	6	Zfp804a
0	0.6661510	0.940	0.104	0	6	Gad2
0	0.6614709	0.881	0.104	0	6	Gad1
0	0.6524320	0.881	0.367	0	6	Ak5
0	1.9640315	1.000	0.140	0	7	Htr2c
0	1.4597121	1.000	0.177	0	7	Ttr
0	1.2423272	0.926	0.052	0	7	Gmnc
0	1.1559174	1.000	0.253	0	7	Vat1l
0	1.1478074	1.000	0.769	0	7	Trpm3
0	1.1352757	1.000	0.143	0	7	Sulf1
0	1.1263682	1.000	0.089	0	7	Rbm47
0	1.0851914	0.963	0.253	0	7	Enpp2
0	1.0289727	1.000	0.322	0	7	Otx2os1
0	1.0243521	0.963	0.656	0	7	Stk39
0	1.0196880	1.000	0.336	0	7	C330002G04Rik
0	1.0076501	0.963	0.378	0	7	Nhsl2
0	0.9995940	1.000	0.630	0	7	Wdr17
0	0.9747735	0.926	0.317	0	7	Atp2b3
0	0.9328334	0.815	0.046	0	7	Col8a1
0	0.9033041	0.963	0.188	0	7	Slc16a2
0	0.8731274	0.889	0.556	0	7	Slc4a10
0	0.8715270	0.778	0.198	0	7	Gm28376
0	0.8700451	0.852	0.435	0	7	Cab39l
0	0.8429674	0.926	0.297	0	7	Prdm16

THP7.markers.mast %>%
    group_by(cluster) %>%
    top_n(n = 10, wt = avg_log10FC) -> top10
DoHeatmap(THP7, features = top10$gene) + NoLegend()

Past versions of pl-heatmap-THP7-1.png

Version	Author	Date
3b155ad	Evgenii O. Tretiakov	2023-03-05
8bf2341	Evgenii O. Tretiakov	2023-03-04
3b19c24	Evgenii O. Tretiakov	2023-02-02

Past versions of pl_clst_dotplot_THP7-1.png

Version	Author	Date
3b155ad	Evgenii O. Tretiakov	2023-03-05
8bf2341	Evgenii O. Tretiakov	2023-03-04
3b19c24	Evgenii O. Tretiakov	2023-02-02

FeaturePlot_scCustom(THP7, "Galr1", pt.size = 3, order = T, colors_use = combined.srt@misc$expr_Colour_Pal) + 
  ggtitle("Galr1: ") + theme(plot.title = element_text(size = 24))

Past versions of unnamed-chunk-28-1.png

Version	Author	Date
3b155ad	Evgenii O. Tretiakov	2023-03-05
3b19c24	Evgenii O. Tretiakov	2023-02-02
c82f5c0	Evgenii O. Tretiakov	2023-01-25

FeaturePlot_scCustom(THP7, "Gal", pt.size = 3, order = T, colors_use = combined.srt@misc$expr_Colour_Pal) + 
  ggtitle("Gal: ") + theme(plot.title = element_text(size = 24))

Past versions of unnamed-chunk-29-1.png

Version	Author	Date
3b155ad	Evgenii O. Tretiakov	2023-03-05
3b19c24	Evgenii O. Tretiakov	2023-02-02

SaveH5Seurat(THP7, 
             filename = here(data_dir,
                             "THP7_clusters.h5Seurat"),
             overwrite = TRUE)
Convert(here(data_dir,
             "THP7_clusters.h5Seurat"), 
        dest = "h5ad",
        overwrite = TRUE)

Principal sensory trigeminal nucleus

plan("sequential")
invisible(gc())
plan("multisession", workers = 4)
n_cores <- 4
options(future.globals.maxSize = 8000 * 1024^2)

n_pcs <- 100
c(Pr5P7, Pr5P7.markers.logreg, Pr5P7.markers.mast)  %<-% DeriveKTree(Pr5P7)

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Idents(Pr5P7) <- "k_tree"
Pr5P7$sep_clstr <- Idents(Pr5P7)

Pr5P7.markers.logreg %>%
  group_by(cluster) %>%
  slice_max(n = 20, order_by = avg_log10FC) %>%
  kable("html") %>%
  kable_material(
    bootstrap_options = c("bordered",
                          "condensed",
                          "responsive",
                          "striped"),
    position = "left",
    font_size = 14
  )

p_val	avg_log10FC	pct.1	pct.2	p_val_adj	cluster	gene
0.0000000	0.7682727	0.994	0.522	0.0000000	1	Ntng1
0.0000000	0.6587305	0.987	0.528	0.0000000	1	Tenm1
0.0000000	0.6417097	0.994	0.472	0.0000000	1	Rnf220
0.0000000	0.6295557	0.987	0.399	0.0000000	1	Samd5
0.0000000	0.6142961	0.962	0.548	0.0000000	1	Shisa9
0.0000000	0.6052970	0.885	0.300	0.0000000	1	Gm48749
0.0000000	0.6023628	0.974	0.492	0.0000000	1	Tafa1
0.0000000	0.5527608	0.827	0.317	0.0000000	1	Zmat4
0.0000000	0.5396341	0.808	0.286	0.0000000	1	6330411D24Rik
0.0000000	0.5392844	0.885	0.349	0.0000000	1	Cdh6
0.0000000	0.5356545	0.897	0.417	0.0000000	1	Thsd7b
0.0000000	0.5311141	0.962	0.516	0.0000000	1	Egfem1
0.0000000	0.5296590	0.936	0.389	0.0000000	1	Nell1
0.0000000	0.5153773	0.994	0.536	0.0000000	1	Syt1
0.0000000	0.4994833	0.929	0.407	0.0000000	1	Nell2
0.0000000	0.4905964	0.968	0.512	0.0000000	1	Tox
0.0000000	0.4753295	0.635	0.196	0.0000000	1	Lef1
0.0000000	0.4737435	0.974	0.585	0.0000000	1	Rims1
0.0000000	0.4729598	0.878	0.310	0.0000000	1	Kcnh8
0.0000000	0.4711861	0.987	0.812	0.0000000	1	Ptprd
0.0000000	0.5694511	0.814	0.335	0.0000000	2	Unc5d
0.0000000	0.5683756	0.831	0.536	0.0000000	2	Galntl6
0.0000000	0.5150861	0.780	0.367	0.0000000	2	Gm15398
0.0000000	0.5064937	0.856	0.313	0.0000000	2	Tafa2
0.0000000	0.4883352	0.958	0.534	0.0000000	2	Dpp10
0.0000000	0.4771407	0.780	0.382	0.0000000	2	Zfp804b
0.0000000	0.4499682	0.839	0.301	0.0000000	2	Grin2a
0.0000000	0.4485589	0.992	0.627	0.0000000	2	Nrg1
0.0000000	0.4462044	0.720	0.167	0.0000000	2	B230217J21Rik
0.0000000	0.4445790	0.907	0.532	0.0000000	2	Cdh18
0.0000000	0.4296846	0.814	0.386	0.0000000	2	Rmst
0.0000000	0.4176821	0.856	0.301	0.0000000	2	Dlgap2
0.0000000	0.4149623	0.873	0.337	0.0000000	2	Kif26b
0.0000000	0.3945519	0.839	0.468	0.0000000	2	Galnt13
0.0000241	0.3940254	0.297	0.178	0.5111098	2	Meis2
0.0000000	0.3916516	0.992	0.655	0.0000000	2	Ahi1
0.0000000	0.3846131	0.898	0.605	0.0000000	2	Asic2
0.0000000	0.3834957	0.805	0.391	0.0000000	2	Esrrg
0.0000000	0.3797631	0.500	0.187	0.0000000	2	Klhl1
0.0000000	0.3796274	0.788	0.470	0.0000000	2	Grm8
0.0000000	0.8745001	0.378	0.043	0.0000000	3	Flt1
0.0000000	0.7594669	0.378	0.045	0.0000000	3	Mecom
0.0000000	0.7378892	0.694	0.195	0.0000000	3	Dach1
0.0000000	0.6856541	0.418	0.063	0.0000000	3	Slc7a5
0.0000000	0.6395670	0.439	0.043	0.0000000	3	Fli1
0.0000000	0.5893545	0.531	0.164	0.0000000	3	Slc7a1
0.0000000	0.5835394	0.551	0.125	0.0000000	3	Ccdc141
0.0000000	0.5390925	0.347	0.042	0.0000000	3	Slco1c1
0.0000000	0.5380678	0.347	0.060	0.0000000	3	Ets1
0.0000000	0.5341887	0.286	0.016	0.0000000	3	Egfl7
0.0000000	0.5124712	0.337	0.027	0.0000000	3	Adgrl4
0.0000000	0.5078686	0.796	0.307	0.0000000	3	Rapgef5
0.0000000	0.4877063	0.837	0.377	0.0000000	3	Dlc1
0.0000000	0.4856556	0.347	0.023	0.0000000	3	Ptprb
0.0000000	0.4692441	0.296	0.013	0.0000000	3	Rassf9
0.0000000	0.4623950	0.337	0.063	0.0000000	3	Itga1
0.0000000	0.4617441	0.296	0.027	0.0000000	3	Cyyr1
0.0000000	0.4599659	0.796	0.309	0.0000000	3	Mef2c
0.0000000	0.4437452	0.592	0.227	0.0000000	3	Tfrc
0.0000000	0.4402024	0.898	0.578	0.0000000	3	Igf1r
0.0000000	0.5455166	0.912	0.832	0.0000299	4	Gm42418
0.0000000	0.4732368	0.647	0.493	0.0000055	4	Cdh4
0.0000000	0.4105305	0.750	0.543	0.0000013	4	Rbfox3
0.0000000	0.4041384	0.735	0.509	0.0000000	4	Gm26871
0.0000000	0.4007033	0.809	0.423	0.0000000	4	Rmst
0.0000000	0.3995068	0.618	0.435	0.0000717	4	Zfp804b
0.0000000	0.3977313	0.735	0.536	0.0000002	4	Klhl29
0.0000014	0.3971734	0.485	0.397	0.0297113	4	Gm30382
0.0000006	0.3781426	0.618	0.467	0.0133712	4	Gria1
0.0000043	0.3733441	0.426	0.348	0.0905385	4	Matk
0.0000000	0.3706291	0.691	0.368	0.0000001	4	Dlgap2
0.0000005	0.3619140	0.559	0.442	0.0106278	4	Olfm2
0.0000003	0.3617732	0.529	0.418	0.0058702	4	Sorcs1
0.0000000	0.3608576	0.735	0.538	0.0000000	4	Nxph1
0.0000000	0.3543118	0.794	0.586	0.0000000	4	Grik1
0.0000002	0.3509807	0.706	0.551	0.0034683	4	Gpi1
0.0000000	0.3468623	0.662	0.481	0.0000950	4	Camk2b
0.0000000	0.3441086	0.721	0.502	0.0000002	4	Ephb2
0.0000000	0.3312443	0.515	0.339	0.0008383	4	Cpne7
0.0000000	0.3201622	0.559	0.413	0.0002159	4	Pdzrn4
0.0000000	1.0964172	0.971	0.310	0.0000000	5	Gm3764
0.0000000	1.0318885	0.926	0.433	0.0000000	5	Wdr17
0.0000000	0.9875801	0.985	0.402	0.0000000	5	Slc4a4
0.0000000	0.9368011	1.000	0.622	0.0000000	5	Npas3
0.0000000	0.8908786	0.985	0.529	0.0000000	5	Ptprz1
0.0000000	0.8719069	0.632	0.118	0.0000000	5	Tnc
0.0000000	0.8546109	0.971	0.437	0.0000000	5	Gm48747
0.0000000	0.8507387	0.779	0.245	0.0000000	5	Slc6a11
0.0000000	0.8316447	0.882	0.322	0.0000000	5	Nhsl1
0.0000000	0.8060870	0.853	0.243	0.0000000	5	Sparcl1
0.0000000	0.8015543	0.985	0.620	0.0000000	5	Luzp2
0.0000000	0.7850233	0.868	0.209	0.0000000	5	Lrig1
0.0000000	0.7839993	0.809	0.212	0.0000000	5	Bmpr1b
0.0000000	0.7433248	0.853	0.432	0.0000000	5	Slc1a2
0.0000000	0.7171399	0.676	0.173	0.0000000	5	Tnfaip8
0.0000000	0.7138261	0.779	0.378	0.0000000	5	Sfxn5
0.0000000	0.7008990	0.912	0.341	0.0000000	5	Mir9-3hg
0.0000000	0.6985497	0.632	0.092	0.0000000	5	Slc39a12
0.0000000	0.6907878	0.897	0.500	0.0000000	5	Trim9
0.0000000	0.6816227	0.662	0.125	0.0000000	5	Pla2g7
0.0000000	1.0605910	0.449	0.209	0.0000000	6	Mbp
0.0000000	1.0156204	0.592	0.100	0.0000000	6	Bcas1
0.0000000	1.0120110	0.878	0.285	0.0000000	6	Sox6
0.0000000	0.9753918	0.694	0.138	0.0000000	6	Gm38505
0.0000000	0.9180520	0.714	0.090	0.0000000	6	Sox10
0.0000000	0.9156013	0.837	0.420	0.0000000	6	Tnr
0.0000000	0.9155389	0.286	0.080	0.0000000	6	Plp1
0.0000000	0.8841322	0.714	0.235	0.0000000	6	4930588A03Rik
0.0000000	0.8707625	0.449	0.182	0.0000000	6	9630013A20Rik
0.0000000	0.8562393	0.918	0.401	0.0000000	6	Sox2ot
0.0000000	0.8056588	0.653	0.368	0.0000000	6	Pacrg
0.0000000	0.7909532	0.265	0.065	0.0000000	6	St18
0.0000000	0.7803360	0.653	0.192	0.0000000	6	Tns3
0.0000000	0.7175360	0.449	0.045	0.0000000	6	Bcas1os2
0.0000000	0.6976459	0.143	0.015	0.0000294	6	Mobp
0.0000000	0.6891951	0.714	0.078	0.0000000	6	Prkcq
0.0000000	0.6870148	0.571	0.161	0.0000000	6	Pdgfra
0.0000000	0.6845440	0.857	0.313	0.0000000	6	Dscaml1
0.0000000	0.6835054	0.796	0.496	0.0000000	6	Pcdh15
0.0000001	0.6520577	0.245	0.090	0.0014227	6	Cnksr3
0.0000000	1.5543447	0.729	0.079	0.0000000	7	Ptgds
0.0000000	1.3853127	1.000	0.084	0.0000000	7	Ranbp3l
0.0000000	1.2886189	0.938	0.076	0.0000000	7	Slc6a20a
0.0000000	1.1765734	0.875	0.833	0.0000000	7	Trpm3
0.0000000	1.1306027	0.917	0.257	0.0000000	7	Sidt1
0.0000000	1.0809888	0.938	0.331	0.0000000	7	Adam12
0.0000000	1.0798617	0.938	0.051	0.0000000	7	Slc6a13
0.0000000	1.0558545	0.833	0.114	0.0000000	7	Bmp6
0.0000000	1.0432585	0.979	0.321	0.0000000	7	Atp1a2
0.0000000	1.0375787	0.938	0.255	0.0000000	7	Bicc1
0.0000000	1.0160532	0.812	0.169	0.0000000	7	Adamts12
0.0000000	1.0154212	0.833	0.192	0.0000000	7	Lrmda
0.0000000	1.0065613	0.771	0.656	0.0000000	7	Nnat
0.0000000	1.0044631	0.792	0.195	0.0000000	7	Slc7a11
0.0000000	0.9952095	0.875	0.248	0.0000000	7	Sned1
0.0000000	0.9610983	0.896	0.248	0.0000000	7	Tmtc4
0.0000000	0.9392163	0.958	0.131	0.0000000	7	Arhgap29
0.0000000	0.8767605	0.896	0.240	0.0000000	7	Pdzrn3
0.0000000	0.8741322	0.792	0.111	0.0000000	7	Eya1
0.0000000	0.8643599	0.854	0.131	0.0000000	7	Colec12
0.0000000	1.5180739	1.000	0.125	0.0000000	8	Bnc2
0.0000000	1.2317607	0.897	0.104	0.0000000	8	Adamtsl3
0.0000000	1.1389751	0.931	0.177	0.0000000	8	Thsd4
0.0000000	1.1196392	0.931	0.061	0.0000000	8	Trabd2b
0.0000000	1.1169845	0.931	0.525	0.0000000	8	Slc4a10
0.0000000	1.1058182	1.000	0.413	0.0000000	8	Slc38a2
0.0000000	1.0887117	0.828	0.343	0.0000000	8	Col25a1
0.0000000	1.0463921	1.000	0.474	0.0000000	8	Fbxl7
0.0000000	1.0442771	1.000	0.605	0.0000000	8	Foxp1
0.0000000	1.0199695	0.759	0.002	0.0000000	8	Slc47a1
0.0000000	1.0069592	0.862	0.265	0.0000000	8	Itgbl1
0.0000000	0.9757935	0.793	0.088	0.0000000	8	Dock5
0.0000000	0.9689388	0.966	0.213	0.0000000	8	Hmcn1
0.0000000	0.9624254	0.931	0.388	0.0000000	8	Nr3c2
0.0000000	0.9498822	0.931	0.368	0.0000000	8	Gulp1
0.0000000	0.9328857	0.586	0.111	0.0000000	8	Crispld1
0.0000000	0.9293280	0.862	0.324	0.0000000	8	Sh3pxd2a
0.0000000	0.9111383	0.931	0.165	0.0000000	8	Eya2
0.0000000	0.8943753	0.828	0.294	0.0000000	8	Slit2
0.0000000	0.8872551	0.931	0.490	0.0000000	8	Tmtc1
0.0000000	0.7973422	0.833	0.375	0.0000000	9	Stxbp5l
0.0000000	0.7943548	0.500	0.052	0.0000062	9	Gm42397
0.0000000	0.7756423	0.944	0.699	0.0000000	9	Unc5c
0.0000000	0.7623770	0.444	0.036	0.0000076	9	Lmx1a
0.0000009	0.7539547	0.556	0.218	0.0184195	9	Reln
0.0000004	0.7345614	0.333	0.044	0.0091203	9	Gm27016
0.0000052	0.7092636	0.667	0.333	0.1092290	9	Chn2
0.0000000	0.7032384	0.500	0.188	0.0000886	9	Adamts18
0.0000001	0.6979407	0.722	0.205	0.0025532	9	Ror1
0.0000000	0.6910469	0.667	0.131	0.0000210	9	Tll1
0.0000000	0.6586472	0.722	0.379	0.0002766	9	Cntnap4
0.0000000	0.6332334	0.500	0.058	0.0000007	9	Gm2694
0.0000000	0.6099621	0.556	0.076	0.0000139	9	Tmem178
0.0000000	0.6086758	1.000	0.658	0.0000000	9	Arpp21
0.0000147	0.6040286	0.556	0.213	0.3114939	9	Ano3
0.0000001	0.6038905	0.778	0.620	0.0019772	9	Mgat5
0.0000000	0.5908940	0.833	0.243	0.0009734	9	Meis1
0.0000231	0.5869130	0.722	0.566	0.4897056	9	Zfp804a
0.0000003	0.5791601	0.500	0.238	0.0061775	9	Vegfc
0.0021706	0.5738713	0.500	0.424	1.0000000	9	Gm45321

Pr5P7.markers.mast %>%
  group_by(cluster) %>%
  slice_max(n = 20, order_by = avg_log10FC) %>%
  kable("html") %>%
  kable_material(
    bootstrap_options = c("bordered",
                          "condensed",
                          "responsive",
                          "striped"),
    position = "left",
    font_size = 14
  )

p_val	avg_log10FC	pct.1	pct.2	p_val_adj	cluster	gene
0.0000000	0.7682727	0.994	0.522	0.0000000	1	Ntng1
0.0000000	0.6587305	0.987	0.528	0.0000000	1	Tenm1
0.0000000	0.6417097	0.994	0.472	0.0000000	1	Rnf220
0.0000000	0.6295557	0.987	0.399	0.0000000	1	Samd5
0.0000000	0.6142961	0.962	0.548	0.0000000	1	Shisa9
0.0000000	0.6052970	0.885	0.300	0.0000000	1	Gm48749
0.0000000	0.6023628	0.974	0.492	0.0000000	1	Tafa1
0.0000000	0.5527608	0.827	0.317	0.0000000	1	Zmat4
0.0000000	0.5396341	0.808	0.286	0.0000000	1	6330411D24Rik
0.0000000	0.5392844	0.885	0.349	0.0000000	1	Cdh6
0.0000000	0.5356545	0.897	0.417	0.0000000	1	Thsd7b
0.0000000	0.5311141	0.962	0.516	0.0000000	1	Egfem1
0.0000000	0.5296590	0.936	0.389	0.0000000	1	Nell1
0.0000000	0.5153773	0.994	0.536	0.0000000	1	Syt1
0.0000000	0.4994833	0.929	0.407	0.0000000	1	Nell2
0.0000000	0.4905964	0.968	0.512	0.0000000	1	Tox
0.0000000	0.4753295	0.635	0.196	0.0000000	1	Lef1
0.0000000	0.4737435	0.974	0.585	0.0000000	1	Rims1
0.0000000	0.4729598	0.878	0.310	0.0000000	1	Kcnh8
0.0000000	0.4711861	0.987	0.812	0.0000000	1	Ptprd
0.0000000	0.5694511	0.814	0.335	0.0000000	2	Unc5d
0.0000000	0.5683756	0.831	0.536	0.0000000	2	Galntl6
0.0000000	0.5150861	0.780	0.367	0.0000000	2	Gm15398
0.0000000	0.5064937	0.856	0.313	0.0000000	2	Tafa2
0.0000000	0.4883352	0.958	0.534	0.0000000	2	Dpp10
0.0000000	0.4771407	0.780	0.382	0.0000000	2	Zfp804b
0.0000000	0.4499682	0.839	0.301	0.0000000	2	Grin2a
0.0000000	0.4485589	0.992	0.627	0.0000000	2	Nrg1
0.0000000	0.4462044	0.720	0.167	0.0000000	2	B230217J21Rik
0.0000000	0.4445790	0.907	0.532	0.0000000	2	Cdh18
0.0000000	0.4296846	0.814	0.386	0.0000000	2	Rmst
0.0000000	0.4176821	0.856	0.301	0.0000000	2	Dlgap2
0.0000000	0.4149623	0.873	0.337	0.0000000	2	Kif26b
0.0000000	0.3945519	0.839	0.468	0.0000000	2	Galnt13
0.0000001	0.3940254	0.297	0.178	0.0014188	2	Meis2
0.0000000	0.3916516	0.992	0.655	0.0000000	2	Ahi1
0.0000000	0.3846131	0.898	0.605	0.0000000	2	Asic2
0.0000000	0.3834957	0.805	0.391	0.0000000	2	Esrrg
0.0000000	0.3797631	0.500	0.187	0.0000000	2	Klhl1
0.0000000	0.3796274	0.788	0.470	0.0000255	2	Grm8
0.0000000	0.8745001	0.378	0.043	0.0000000	3	Flt1
0.0000000	0.7594669	0.378	0.045	0.0000000	3	Mecom
0.0000000	0.7378892	0.694	0.195	0.0000000	3	Dach1
0.0000000	0.6856541	0.418	0.063	0.0000000	3	Slc7a5
0.0000000	0.6395670	0.439	0.043	0.0000000	3	Fli1
0.0000000	0.5893545	0.531	0.164	0.0000000	3	Slc7a1
0.0000000	0.5835394	0.551	0.125	0.0000000	3	Ccdc141
0.0000000	0.5390925	0.347	0.042	0.0000000	3	Slco1c1
0.0000000	0.5380678	0.347	0.060	0.0000000	3	Ets1
0.0000000	0.5341887	0.286	0.016	0.0000000	3	Egfl7
0.0000000	0.5124712	0.337	0.027	0.0000000	3	Adgrl4
0.0000000	0.5078686	0.796	0.307	0.0000000	3	Rapgef5
0.0000000	0.4877063	0.837	0.377	0.0000000	3	Dlc1
0.0000000	0.4856556	0.347	0.023	0.0000000	3	Ptprb
0.0000000	0.4692441	0.296	0.013	0.0000000	3	Rassf9
0.0000000	0.4623950	0.337	0.063	0.0000000	3	Itga1
0.0000000	0.4617441	0.296	0.027	0.0000000	3	Cyyr1
0.0000000	0.4599659	0.796	0.309	0.0000000	3	Mef2c
0.0000000	0.4437452	0.592	0.227	0.0000004	3	Tfrc
0.0000000	0.4402024	0.898	0.578	0.0000000	3	Igf1r
0.0000000	0.5455166	0.912	0.832	0.0000002	4	Gm42418
0.0000000	0.4732368	0.647	0.493	0.0000024	4	Cdh4
0.0000000	0.4105305	0.750	0.543	0.0000000	4	Rbfox3
0.0008440	0.4085170	0.721	0.729	1.0000000	4	Gm26917
0.0000000	0.4041384	0.735	0.509	0.0000000	4	Gm26871
0.0000000	0.4007033	0.809	0.423	0.0000000	4	Rmst
0.0000000	0.3995068	0.618	0.435	0.0003662	4	Zfp804b
0.0000000	0.3977313	0.735	0.536	0.0000000	4	Klhl29
0.0000069	0.3971734	0.485	0.397	0.1461315	4	Gm30382
0.0000011	0.3781426	0.618	0.467	0.0234642	4	Gria1
0.0000054	0.3733441	0.426	0.348	0.1140371	4	Matk
0.0000000	0.3706291	0.691	0.368	0.0000000	4	Dlgap2
0.0000003	0.3619140	0.559	0.442	0.0058549	4	Olfm2
0.0000007	0.3617732	0.529	0.418	0.0155928	4	Sorcs1
0.0000000	0.3608576	0.735	0.538	0.0000001	4	Nxph1
0.0000000	0.3543118	0.794	0.586	0.0000000	4	Grik1
0.0000001	0.3509807	0.706	0.551	0.0026335	4	Gpi1
0.0000000	0.3468623	0.662	0.481	0.0000871	4	Camk2b
0.0000000	0.3441086	0.721	0.502	0.0000000	4	Ephb2
0.0000002	0.3312443	0.515	0.339	0.0041291	4	Cpne7
0.0000000	1.0964172	0.971	0.310	0.0000000	5	Gm3764
0.0000000	1.0318885	0.926	0.433	0.0000000	5	Wdr17
0.0000000	0.9875801	0.985	0.402	0.0000000	5	Slc4a4
0.0000000	0.9368011	1.000	0.622	0.0000000	5	Npas3
0.0000000	0.8908786	0.985	0.529	0.0000000	5	Ptprz1
0.0000000	0.8719069	0.632	0.118	0.0000000	5	Tnc
0.0000000	0.8546109	0.971	0.437	0.0000000	5	Gm48747
0.0000000	0.8507387	0.779	0.245	0.0000000	5	Slc6a11
0.0000000	0.8316447	0.882	0.322	0.0000000	5	Nhsl1
0.0000000	0.8060870	0.853	0.243	0.0000000	5	Sparcl1
0.0000000	0.8015543	0.985	0.620	0.0000000	5	Luzp2
0.0000000	0.7850233	0.868	0.209	0.0000000	5	Lrig1
0.0000000	0.7839993	0.809	0.212	0.0000000	5	Bmpr1b
0.0000000	0.7433248	0.853	0.432	0.0000000	5	Slc1a2
0.0000000	0.7171399	0.676	0.173	0.0000000	5	Tnfaip8
0.0000000	0.7138261	0.779	0.378	0.0000000	5	Sfxn5
0.0000000	0.7008990	0.912	0.341	0.0000000	5	Mir9-3hg
0.0000000	0.6985497	0.632	0.092	0.0000000	5	Slc39a12
0.0000000	0.6907878	0.897	0.500	0.0000000	5	Trim9
0.0000000	0.6816227	0.662	0.125	0.0000000	5	Pla2g7
0.0000000	1.0605910	0.449	0.209	0.0000000	6	Mbp
0.0000000	1.0156204	0.592	0.100	0.0000000	6	Bcas1
0.0000000	1.0120110	0.878	0.285	0.0000000	6	Sox6
0.0000000	0.9753918	0.694	0.138	0.0000000	6	Gm38505
0.0000000	0.9180520	0.714	0.090	0.0000000	6	Sox10
0.0000000	0.9156013	0.837	0.420	0.0000000	6	Tnr
0.0000000	0.9155389	0.286	0.080	0.0000000	6	Plp1
0.0000000	0.8841322	0.714	0.235	0.0000000	6	4930588A03Rik
0.0000000	0.8707625	0.449	0.182	0.0000000	6	9630013A20Rik
0.0000000	0.8562393	0.918	0.401	0.0000000	6	Sox2ot
0.0000000	0.8056588	0.653	0.368	0.0000000	6	Pacrg
0.0000000	0.7909532	0.265	0.065	0.0000000	6	St18
0.0000000	0.7803360	0.653	0.192	0.0000000	6	Tns3
0.0000000	0.7175360	0.449	0.045	0.0000000	6	Bcas1os2
0.0000000	0.6976459	0.143	0.015	0.0000287	6	Mobp
0.0000000	0.6891951	0.714	0.078	0.0000000	6	Prkcq
0.0000000	0.6870148	0.571	0.161	0.0000000	6	Pdgfra
0.0000000	0.6845440	0.857	0.313	0.0000000	6	Dscaml1
0.0000000	0.6835054	0.796	0.496	0.0000000	6	Pcdh15
0.0000000	0.6520577	0.245	0.090	0.0000804	6	Cnksr3
0.0000000	1.5543447	0.729	0.079	0.0000000	7	Ptgds
0.0000000	1.3853127	1.000	0.084	0.0000000	7	Ranbp3l
0.0000000	1.2886189	0.938	0.076	0.0000000	7	Slc6a20a
0.0000000	1.1765734	0.875	0.833	0.0000000	7	Trpm3
0.0000000	1.1306027	0.917	0.257	0.0000000	7	Sidt1
0.0000000	1.0809888	0.938	0.331	0.0000000	7	Adam12
0.0000000	1.0798617	0.938	0.051	0.0000000	7	Slc6a13
0.0000000	1.0558545	0.833	0.114	0.0000000	7	Bmp6
0.0000000	1.0432585	0.979	0.321	0.0000000	7	Atp1a2
0.0000000	1.0375787	0.938	0.255	0.0000000	7	Bicc1
0.0000000	1.0160532	0.812	0.169	0.0000000	7	Adamts12
0.0000000	1.0154212	0.833	0.192	0.0000000	7	Lrmda
0.0000000	1.0065613	0.771	0.656	0.0000000	7	Nnat
0.0000000	1.0044631	0.792	0.195	0.0000000	7	Slc7a11
0.0000000	0.9952095	0.875	0.248	0.0000000	7	Sned1
0.0000000	0.9610983	0.896	0.248	0.0000000	7	Tmtc4
0.0000000	0.9392163	0.958	0.131	0.0000000	7	Arhgap29
0.0000000	0.8767605	0.896	0.240	0.0000000	7	Pdzrn3
0.0000000	0.8741322	0.792	0.111	0.0000000	7	Eya1
0.0000000	0.8643599	0.854	0.131	0.0000000	7	Colec12
0.0000000	1.5180739	1.000	0.125	0.0000000	8	Bnc2
0.0000000	1.2317607	0.897	0.104	0.0000000	8	Adamtsl3
0.0000000	1.1389751	0.931	0.177	0.0000000	8	Thsd4
0.0000000	1.1196392	0.931	0.061	0.0000000	8	Trabd2b
0.0000000	1.1169845	0.931	0.525	0.0000000	8	Slc4a10
0.0000000	1.1058182	1.000	0.413	0.0000000	8	Slc38a2
0.0000000	1.0887117	0.828	0.343	0.0000000	8	Col25a1
0.0000000	1.0463921	1.000	0.474	0.0000000	8	Fbxl7
0.0000000	1.0442771	1.000	0.605	0.0000000	8	Foxp1
0.0000000	1.0199695	0.759	0.002	0.0000000	8	Slc47a1
0.0000000	1.0069592	0.862	0.265	0.0000000	8	Itgbl1
0.0000000	0.9757935	0.793	0.088	0.0000000	8	Dock5
0.0000000	0.9689388	0.966	0.213	0.0000000	8	Hmcn1
0.0000000	0.9624254	0.931	0.388	0.0000000	8	Nr3c2
0.0000000	0.9498822	0.931	0.368	0.0000000	8	Gulp1
0.0000000	0.9328857	0.586	0.111	0.0000000	8	Crispld1
0.0000000	0.9293280	0.862	0.324	0.0000000	8	Sh3pxd2a
0.0000000	0.9111383	0.931	0.165	0.0000000	8	Eya2
0.0000000	0.8943753	0.828	0.294	0.0000000	8	Slit2
0.0000000	0.8872551	0.931	0.490	0.0000000	8	Tmtc1
0.0000000	0.7973422	0.833	0.375	0.0000000	9	Stxbp5l
0.0000000	0.7943548	0.500	0.052	0.0000693	9	Gm42397
0.0000000	0.7756423	0.944	0.699	0.0000000	9	Unc5c
0.0000000	0.7623770	0.444	0.036	0.0003760	9	Lmx1a
0.0000006	0.7539547	0.556	0.218	0.0128781	9	Reln
0.0000012	0.7345614	0.333	0.044	0.0257720	9	Gm27016
0.0000001	0.7092636	0.667	0.333	0.0018579	9	Chn2
0.0000000	0.7032384	0.500	0.188	0.0000002	9	Adamts18
0.0000003	0.6979407	0.722	0.205	0.0062991	9	Ror1
0.0000000	0.6910469	0.667	0.131	0.0000111	9	Tll1
0.0000000	0.6586472	0.722	0.379	0.0000169	9	Cntnap4
0.0000000	0.6332334	0.500	0.058	0.0001458	9	Gm2694
0.0000000	0.6099621	0.556	0.076	0.0000234	9	Tmem178
0.0000000	0.6086758	1.000	0.658	0.0000000	9	Arpp21
0.0000081	0.6040286	0.556	0.213	0.1715667	9	Ano3
0.0000000	0.6038905	0.778	0.620	0.0000007	9	Mgat5
0.0000002	0.5908940	0.833	0.243	0.0048746	9	Meis1
0.0000018	0.5869130	0.722	0.566	0.0389649	9	Zfp804a
0.0000000	0.5791601	0.500	0.238	0.0000748	9	Vegfc
0.0023235	0.5738713	0.500	0.424	1.0000000	9	Gm45321

Pr5P7.markers.mast %>%
    group_by(cluster) %>%
    top_n(n = 10, wt = avg_log10FC) -> top10
DoHeatmap(Pr5P7, features = top10$gene) + NoLegend()

Past versions of pl-heatmap-Pr5P7-1.png

Version	Author	Date
3b155ad	Evgenii O. Tretiakov	2023-03-05
8bf2341	Evgenii O. Tretiakov	2023-03-04
3b19c24	Evgenii O. Tretiakov	2023-02-02

Past versions of pl_clst_dotplot_Pr5P7-1.png

Version	Author	Date
3b155ad	Evgenii O. Tretiakov	2023-03-05
8bf2341	Evgenii O. Tretiakov	2023-03-04
3b19c24	Evgenii O. Tretiakov	2023-02-02

FeaturePlot_scCustom(Pr5P7, "Galr1", pt.size = 3, order = T, colors_use = combined.srt@misc$expr_Colour_Pal) + 
  ggtitle("Galr1: ") + theme(plot.title = element_text(size = 24))

Past versions of unnamed-chunk-32-1.png

Version	Author	Date
3b155ad	Evgenii O. Tretiakov	2023-03-05
3b19c24	Evgenii O. Tretiakov	2023-02-02
c82f5c0	Evgenii O. Tretiakov	2023-01-25

FeaturePlot_scCustom(Pr5P7, "Gal", pt.size = 3, order = T, colors_use = combined.srt@misc$expr_Colour_Pal) + 
  ggtitle("Gal: ") + theme(plot.title = element_text(size = 24))

Past versions of unnamed-chunk-33-1.png

Version	Author	Date
3b155ad	Evgenii O. Tretiakov	2023-03-05
3b19c24	Evgenii O. Tretiakov	2023-02-02

SaveH5Seurat(Pr5P7, 
             filename = here(data_dir,
                             "Pr5P7_clusters.h5Seurat"),
             overwrite = TRUE)
Convert(here(data_dir,
             "Pr5P7_clusters.h5Seurat"), 
        dest = "h5ad",
        overwrite = TRUE)

Session information

sessionInfo()

R version 4.2.2 (2022-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.1 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] ggraph_2.1.0.9000     gprofiler2_0.2.1      mrtree_0.0.0.9000    
 [4] Nebulosa_1.8.0        scCustomize_1.1.1     Scillus_0.5.0        
 [7] qs_0.25.4             patchwork_1.1.2.9000  glmGamPoi_1.10.2     
[10] sctransform_0.3.5     SeuratDisk_0.0.0.9020 SeuratWrappers_0.3.1 
[13] SeuratObject_4.1.3    Seurat_4.3.0          reticulate_1.28      
[16] kableExtra_1.3.4      zeallot_0.1.0         future_1.31.0        
[19] skimr_2.1.5           magrittr_2.0.3        lubridate_1.9.0      
[22] timechange_0.1.1      forcats_0.5.2         stringr_1.5.0        
[25] dplyr_1.1.0           purrr_1.0.1           readr_2.1.3          
[28] tidyr_1.3.0           tibble_3.1.8          ggplot2_3.4.1        
[31] tidyverse_1.3.2.9000  viridis_0.6.2         viridisLite_0.4.1    
[34] RColorBrewer_1.1-3    here_1.0.1            workflowr_1.7.0      

loaded via a namespace (and not attached):
  [1] rsvd_1.0.5                  ica_1.0-3                  
  [3] svglite_2.1.0               ps_1.7.2                   
  [5] foreach_1.5.2               lmtest_0.9-40              
  [7] rprojroot_2.0.3             crayon_1.5.2               
  [9] MASS_7.3-58.1               MAST_1.24.1                
 [11] nlme_3.1-161                backports_1.4.1            
 [13] rlang_1.0.6                 XVector_0.38.0             
 [15] ROCR_1.0-11                 irlba_2.3.5.1              
 [17] callr_3.7.3                 limma_3.54.1               
 [19] stringfish_0.15.7           data.tree_1.0.0            
 [21] rjson_0.2.21                bit64_4.0.5                
 [23] glue_1.6.2                  parallel_4.2.2             
 [25] processx_3.8.0              vipor_0.4.5                
 [27] spatstat.sparse_3.0-0       BiocGenerics_0.44.0        
 [29] spatstat.geom_3.0-6         tidyselect_1.2.0           
 [31] SummarizedExperiment_1.28.0 fitdistrplus_1.1-8         
 [33] zoo_1.8-11                  xtable_1.8-4               
 [35] formattable_0.2.1           evaluate_0.20              
 [37] cli_3.6.0                   zlibbioc_1.44.0            
 [39] rstudioapi_0.14             miniUI_0.1.1.1             
 [41] sp_1.6-0                    whisker_0.4.1              
 [43] bslib_0.4.2                 fastmatch_1.1-3            
 [45] treeio_1.23.0               maps_3.4.1                 
 [47] shiny_1.7.4                 xfun_0.37                  
 [49] clue_0.3-63                 cluster_2.1.4              
 [51] tidygraph_1.2.2             clusterGeneration_1.3.7    
 [53] expm_0.999-6                SymSim_0.0.0.9000          
 [55] ggrepel_0.9.2.9999          ape_5.6-2                  
 [57] listenv_0.9.0               dendextend_1.16.0          
 [59] png_0.1-8                   withr_2.5.0                
 [61] bitops_1.0-7                ggforce_0.4.1.9000         
 [63] plyr_1.8.8                  pracma_2.4.2               
 [65] coda_0.19-4                 pillar_1.8.1               
 [67] RcppParallel_5.1.5          GlobalOptions_0.1.2        
 [69] cachem_1.0.6                fs_1.6.1                   
 [71] scatterplot3d_0.3-42        hdf5r_1.3.7                
 [73] GetoptLong_1.0.5            paletteer_1.5.0            
 [75] vctrs_0.5.2                 ellipsis_0.3.2             
 [77] generics_0.1.3              RApiSerialize_0.1.2        
 [79] tools_4.2.2                 beeswarm_0.4.0             
 [81] munsell_0.5.0               tweenr_2.0.2               
 [83] DelayedArray_0.24.0         fastmap_1.1.0              
 [85] compiler_4.2.2              abind_1.4-5                
 [87] httpuv_1.6.9                ggimage_0.3.1              
 [89] plotly_4.10.1               GenomeInfoDbData_1.2.9     
 [91] gridExtra_2.3               lattice_0.20-45            
 [93] deldir_1.0-6                utf8_1.2.3                 
 [95] later_1.3.0                 prismatic_1.1.1            
 [97] jsonlite_1.8.4              scales_1.2.1               
 [99] tidytree_0.4.2              pbapply_1.7-0              
[101] lazyeval_0.2.2              promises_1.2.0.1           
[103] doParallel_1.0.17           R.utils_2.12.2             
[105] goftest_1.2-3               spatstat.utils_3.0-1       
[107] checkmate_2.1.0             rmarkdown_2.20             
[109] cowplot_1.1.1               webshot_0.5.4              
[111] Rtsne_0.16                  Biobase_2.58.0             
[113] uwot_0.1.14                 igraph_1.3.5               
[115] survival_3.4-0              numDeriv_2016.8-1.1        
[117] yaml_2.3.7                  plotrix_3.8-2              
[119] systemfonts_1.0.4           htmltools_0.5.4            
[121] graphlayouts_0.8.4          IRanges_2.32.0             
[123] quadprog_1.5-8              digest_0.6.31              
[125] mime_0.12                   repr_1.1.4                 
[127] yulab.utils_0.0.6           future.apply_1.10.0        
[129] ggmin_0.0.0.9000            remotes_2.4.2              
[131] data.table_1.14.8           S4Vectors_0.36.1           
[133] R.oo_1.25.0                 splines_4.2.2              
[135] labeling_0.4.2              rematch2_2.1.2             
[137] Cairo_1.6-0                 RCurl_1.98-1.9             
[139] ks_1.14.0                   hms_1.1.2                  
[141] colorspace_2.1-0            base64enc_0.1-3            
[143] BiocManager_1.30.19         mnormt_2.1.1               
[145] ggbeeswarm_0.7.1.9000       GenomicRanges_1.50.2       
[147] shape_1.4.6                 aplot_0.1.9                
[149] ggrastr_1.0.1               sass_0.4.5                 
[151] Rcpp_1.0.10                 mclust_6.0.0               
[153] RANN_2.6.1                  mvtnorm_1.1-3              
[155] circlize_0.4.15             fansi_1.0.4                
[157] tzdb_0.3.0                  parallelly_1.34.0          
[159] R6_2.5.1                    grid_4.2.2                 
[161] ggridges_0.5.4              lifecycle_1.0.3            
[163] phytools_1.2-0              leiden_0.4.3               
[165] phangorn_2.10.0             jquerylib_0.1.4            
[167] snakecase_0.11.0            Matrix_1.5-3               
[169] RcppAnnoy_0.0.20            iterators_1.0.14           
[171] spatstat.explore_3.0-6      htmlwidgets_1.6.1          
[173] polyclip_1.10-4             gridGraphics_0.5-1         
[175] optimParallel_1.0-2         rvest_1.0.3                
[177] ComplexHeatmap_2.14.0       globals_0.16.2             
[179] spatstat.random_3.1-3       progressr_0.13.0           
[181] codetools_0.2-18            matrixStats_0.63.0         
[183] prettyunits_1.1.1           getPass_0.2-2              
[185] SingleCellExperiment_1.20.0 RSpectra_0.16-1            
[187] R.methodsS3_1.8.2           GenomeInfoDb_1.34.9        
[189] DBI_1.1.3                   gtable_0.3.1               
[191] git2r_0.30.1                stats4_4.2.2               
[193] ggfun_0.0.9                 tensor_1.5                 
[195] httr_1.4.4                  highr_0.10                 
[197] KernSmooth_2.23-20          progress_1.2.2             
[199] stringi_1.7.12              vroom_1.6.0                
[201] reshape2_1.4.4              farver_2.1.1               
[203] magick_2.7.3                ggtree_3.7.1.002           
[205] xml2_1.3.3                  combinat_0.0-8             
[207] ggplotify_0.1.0             scattermore_0.8            
[209] bit_4.0.5                   clustree_0.5.0             
[211] MatrixGenerics_1.10.0       spatstat.data_3.0-0        
[213] janitor_2.2.0.9000          pkgconfig_2.0.3            
[215] ggprism_1.0.4               knitr_1.42