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File | Version | Author | Date | Message |
---|---|---|---|---|
Rmd | cd0f44f | Dave Tang | 2025-01-16 | Module scores |
html | 72b0d6d | Dave Tang | 2025-01-15 | Build site. |
Rmd | bcb5c7b | Dave Tang | 2025-01-15 | Manually use {presto} to calculate pvalues |
html | 8539125 | Dave Tang | 2025-01-15 | Build site. |
Rmd | 240ee62 | Dave Tang | 2025-01-15 | Compare p-value calculations |
html | 50fef6c | Dave Tang | 2025-01-15 | Build site. |
Rmd | cb2011a | Dave Tang | 2025-01-15 | FindMarkers with groups |
html | 522ba27 | Dave Tang | 2024-12-25 | Build site. |
Rmd | ecf10e9 | Dave Tang | 2024-12-25 | FindMarkers in parallel |
html | 612e4f9 | Dave Tang | 2024-12-24 | Build site. |
Rmd | f0f7a57 | Dave Tang | 2024-12-24 | Finding Markers with Seurat |
Use the Peripheral Blood Mononuclear Cells (PBMCs) 2,700 cells dataset to test finding markers with Seurat.
Install the following packages, if necessary.
install.packages("remotes")
remotes::install_github("immunogenomics/presto")
install.packages("Seurat")
install.packages("bench")
Load Seurat
and bench
for some
benchmarking.
suppressPackageStartupMessages(library("Seurat"))
suppressPackageStartupMessages(library("bench"))
suppressPackageStartupMessages(library("presto"))
suppressPackageStartupMessages(library("ggplot2"))
To follow the tutorial, you’ll need the 10X data, which can be download from AWS.
mkdir -p data/pbmc3k && cd data/pbmc3k
wget -c https://s3-us-west-2.amazonaws.com/10x.files/samples/cell/pbmc3k/pbmc3k_filtered_gene_bc_matrices.tar.gz
tar -xzf pbmc3k_filtered_gene_bc_matrices.tar.gz
Load 10x data into a matrix using Read10X()
.
pbmc.data <- Read10X(
data.dir = "data/pbmc3k/filtered_gene_bc_matrices/hg19/"
)
Create the Seurat object using CreateSeuratObject
; see
?SeuratObject
for more information on the class.
seurat_obj <- CreateSeuratObject(
counts = pbmc.data,
min.cells = 3,
min.features = 200,
project = "pbmc3k"
)
Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')
class(seurat_obj)
[1] "Seurat"
attr(,"package")
[1] "SeuratObject"
Run the workflow as separate steps; they can be piped together but sometimes errors occur, so it is useful to split up the steps.
debug_flag <- FALSE
seurat_obj <- NormalizeData(seurat_obj, normalization.method = "LogNormalize", scale.factor = 1e4, verbose = debug_flag)
seurat_obj <- FindVariableFeatures(seurat_obj, selection.method = 'vst', nfeatures = 2000, verbose = debug_flag)
seurat_obj <- ScaleData(seurat_obj, verbose = debug_flag)
seurat_obj <- RunPCA(seurat_obj, verbose = debug_flag)
seurat_obj <- RunUMAP(seurat_obj, dims = 1:30, verbose = debug_flag)
Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
seurat_obj <- FindNeighbors(seurat_obj, dims = 1:30, verbose = debug_flag)
seurat_obj <- FindClusters(seurat_obj, resolution = 0.5, verbose = debug_flag)
seurat_obj
An object of class Seurat
13714 features across 2700 samples within 1 assay
Active assay: RNA (13714 features, 2000 variable features)
3 layers present: counts, data, scale.data
2 dimensional reductions calculated: pca, umap
FindAllMarkers()
will find markers (differentially
expressed genes) for each of the identity classes in a dataset.
levels(Idents(seurat_obj))
[1] "0" "1" "2" "3" "4" "5" "6" "7"
Find all markers.
all_markers <- FindAllMarkers(seurat_obj, verbose = debug_flag)
dim(all_markers)
[1] 17899 7
FindMarkers()
finds markers (differentially expressed
genes) for identity classes. Things to note:
data
slot/layer; this contains
normalised values (after running NormalizeData()
)ident.1
- Identity class to define markers for; pass an
object of class phylo
or clustertree
to find
markers for a node in a cluster tree; passing clustertree
requires BuildClusterTree()
to have been runident.2
- A second identity class for comparison; if
NULL, use all other cells for comparison; if an object of class
phylo
or clustertree
is passed to
ident.1
, must pass a node to find markers forgroup.by
- Regroup cells into a different identity
class prior to performing differential expressionsubset.ident
- Subset a particular identity class prior
to regrouping. Only relevant if group.by is setpbmc_small
dataset.
data(pbmc_small)
pbmc_small
An object of class Seurat
230 features across 80 samples within 1 assay
Active assay: RNA (230 features, 20 variable features)
3 layers present: counts, data, scale.data
2 dimensional reductions calculated: pca, tsne
pbmc_small
metadata.
table(
pbmc_small@meta.data$RNA_snn_res.1,
pbmc_small@meta.data$groups
)
g1 g2
0 20 16
1 14 11
2 10 9
Take all cells in cluster 2, and find markers that separate cells in the ‘g1’ group (metadata variable ‘group’).
pbmc_small_markers <- FindMarkers(pbmc_small, ident.1 = "g1", group.by = 'groups', subset.ident = "2")
head(pbmc_small_markers)
p_val avg_log2FC pct.1 pct.2 p_val_adj
GSTP1 0.01601528 2.603521 0.7 0.111 1
LINC00936 0.02048683 7.182496 0.5 0.000 1
TPM4 0.02048683 7.488007 0.5 0.000 1
LGALS2 0.04515259 7.403075 0.4 0.000 1
IFI30 0.04515259 7.794332 0.4 0.000 1
RHOC 0.04515259 7.016294 0.4 0.000 1
Perform some sanity checks.
get_exp <- function(gene){
gene_exp <- pbmc_small[['RNA']]['data'][gene, ]
pbmc_small@meta.data |>
dplyr::filter(RNA_snn_res.1 == 2, groups == 'g1') |>
row.names() -> g1_c2
pbmc_small@meta.data |>
dplyr::filter(RNA_snn_res.1 == 2, groups == 'g2') |>
row.names() -> g2_c2
g1 <- gene_exp[g1_c2]
g2 <- gene_exp[g2_c2]
rbind(
data.frame(exp = g1, group = "g1"),
data.frame(exp = g2, group = "g2")
)
}
plot_gene <- function(gene){
my_df <- get_exp(gene)
boxplot(
exp~group,
data = my_df,
main = gene
)
}
head(pbmc_small_markers, 3) |>
row.names() -> genes_to_check
sapply(genes_to_check, plot_gene) -> dev_null
Version | Author | Date |
---|---|---|
50fef6c | Dave Tang | 2025-01-15 |
Version | Author | Date |
---|---|---|
50fef6c | Dave Tang | 2025-01-15 |
Version | Author | Date |
---|---|---|
50fef6c | Dave Tang | 2025-01-15 |
Perform Wilcoxon Rank Sum and Signed Rank Tests using
wilcox.test
and compare results.
purrr::map_dbl(row.names(pbmc_small_markers), \(x){
wilcox.test(exp~group, data = get_exp(x))$p.value
}) |>
suppressWarnings() -> manual_p_values
plot(pbmc_small_markers$p_val, manual_p_values, pch = 16)
abline(a = 0, b = 1, lty = 2, col = 2)
Version | Author | Date |
---|---|---|
8539125 | Dave Tang | 2025-01-15 |
Fast Wilcoxon rank sum test and auROC using
presto::wilcoxauc()
.
run_presto_wilcox <- function(gene){
wanted <- pbmc_small@meta.data$RNA_snn_res.1 == "2"
seurat_obj <- pbmc_small[, wanted]
seurat_obj[['RNA']]$data |>
as.matrix() -> data_mat
my_exp <- data_mat[gene, ]
my_mat <- matrix(my_exp, nrow = 1)
colnames(my_mat) <- names(my_exp)
rownames(my_mat) <- gene
y <- factor(seurat_obj@meta.data$groups)
res <- presto::wilcoxauc(my_mat, y)
res <- res[1:(nrow(x = res)/2),]
res$pval
}
purrr::map_dbl(row.names(pbmc_small_markers), run_presto_wilcox) -> presto_p_values
plot(pbmc_small_markers$p_val, presto_p_values, pch = 16)
abline(a = 0, b = 1, lty = 2, col = 2)
Version | Author | Date |
---|---|---|
72b0d6d | Dave Tang | 2025-01-15 |
p-value adjustment is performed using bonferroni correction based on the total number of genes in the dataset. Other correction methods are not recommended, as Seurat pre-filters genes using the arguments above, reducing the number of tests performed. Lastly, as Aaron Lun has pointed out, p-values should be interpreted cautiously, as the genes used for clustering are the same genes tested for differential expression.
all(p.adjust(manual_p_values, method = "bonferroni") == pbmc_small_markers$p_val_adj)
[1] TRUE
Find markers for cluster 0 in pbmc3k.
cluster_0_markers <- FindMarkers(seurat_obj, ident.1 = "0")
dim(cluster_0_markers)
[1] 8434 5
Cluster 0 markers from FindAllMarkers()
.
all_markers |>
dplyr::filter(cluster == 0) |>
dim()
[1] 3139 7
The start of the results are the same.
head(cluster_0_markers)
p_val avg_log2FC pct.1 pct.2 p_val_adj
LDHB 1.547138e-240 1.9351689 0.922 0.473 2.121746e-236
RPS12 3.595829e-228 0.8665851 1.000 0.987 4.931320e-224
CD74 2.127919e-225 -3.1636831 0.735 0.925 2.918227e-221
HLA-DRB1 3.113535e-225 -4.3722870 0.129 0.715 4.269901e-221
CYBA 2.054958e-213 -1.8108145 0.730 0.933 2.818169e-209
HLA-DRA 7.109002e-213 -4.6393725 0.291 0.765 9.749286e-209
all_markers |>
dplyr::filter(cluster == 0) |>
dplyr::select(-cluster, -gene) |>
head()
p_val avg_log2FC pct.1 pct.2 p_val_adj
LDHB 1.547138e-240 1.9351689 0.922 0.473 2.121746e-236
RPS12 3.595829e-228 0.8665851 1.000 0.987 4.931320e-224
CD74 2.127919e-225 -3.1636831 0.735 0.925 2.918227e-221
HLA-DRB1 3.113535e-225 -4.3722870 0.129 0.715 4.269901e-221
CYBA 2.054958e-213 -1.8108145 0.730 0.933 2.818169e-209
HLA-DRA 7.109002e-213 -4.6393725 0.291 0.765 9.749286e-209
The tail of the results are the same too, except that in
FindAllMarkers()
results have been trimmed.
cluster_0_markers[3134:3139, ]
p_val avg_log2FC pct.1 pct.2 p_val_adj
SCML1 0.009913768 1.2125839 0.028 0.014 1
CGGBP1 0.009914211 0.3048076 0.152 0.117 1
CCT3 0.009950407 0.2610577 0.231 0.190 1
ZNF32 0.009955859 0.1339321 0.108 0.079 1
RNF214 0.009977100 0.8208791 0.043 0.025 1
P2RX7 0.009979523 -1.7709166 0.003 0.013 1
all_markers |>
dplyr::filter(cluster == 0) |>
dplyr::select(-cluster, -gene) |>
tail()
p_val avg_log2FC pct.1 pct.2 p_val_adj
SCML1 0.009913768 1.2125839 0.028 0.014 1
CGGBP1 0.009914211 0.3048076 0.152 0.117 1
CCT3 0.009950407 0.2610577 0.231 0.190 1
ZNF32 0.009955859 0.1339321 0.108 0.079 1
RNF214 0.009977100 0.8208791 0.043 0.025 1
P2RX7 0.009979523 -1.7709166 0.003 0.013 1
Trimming seems to be from p_val < 0.01
cluster_0_markers[3139:3142, ]
p_val avg_log2FC pct.1 pct.2 p_val_adj
P2RX7 0.009979523 -1.7709166 0.003 0.013 1
CBFB 0.010029322 0.6492086 0.068 0.046 1
ATF6B 0.010045052 -0.4047457 0.130 0.165 1
PCNT 0.010051913 -1.8088730 0.003 0.013 1
Find markers in parallel to speed up FindAllMarkers()
.
Use imap()
to get the name of each list (.y
);
.x
is each element of the list.
library(future)
library(future.apply)
clusters <- levels(Idents(seurat_obj))
plan(multisession, workers = 4)
markers <- future_lapply(
clusters,
function(x){
FindMarkers(seurat_obj, ident.1 = x)
},
future.seed = TRUE
)
names(markers) <- clusters
purrr::map(
markers,
\(x) tibble::rownames_to_column(.data = x, var = "gene") |> tibble::remove_rownames()
) |>
purrr::imap(~ dplyr::mutate(.x, cluster = .y)) |>
purrr::list_rbind() |>
dplyr::filter(p_val < 0.01) |>
dplyr::mutate(cluster = factor(cluster, levels = clusters)) |>
dplyr::select(p_val, avg_log2FC, pct.1, pct.2, p_val_adj, cluster, gene) -> all_markers_parallel
all.equal(
all_markers_parallel,
tibble::remove_rownames(all_markers)
)
[1] TRUE
The function AddModuleScore():
Calculate the average expression levels of each program (cluster) on single cell level, subtracted by the aggregated expression of control feature sets. All analyzed features are binned based on averaged expression, and the control features are randomly selected from each bin.
ctrl
- Number of control features selected from the
same bin per analyzed featurepbmc_small_markers |>
head(10) |>
row.names() -> my_features
feature_list <- list(my_features)
AddModuleScore(
object = pbmc_small,
features = feature_list,
ctrl = 5,
name = 'cluster_2_markers'
) -> pbmc_small
pbmc_small@meta.data
orig.ident nCount_RNA nFeature_RNA RNA_snn_res.0.8
ATGCCAGAACGACT SeuratProject 70 47 0
CATGGCCTGTGCAT SeuratProject 85 52 0
GAACCTGATGAACC SeuratProject 87 50 1
TGACTGGATTCTCA SeuratProject 127 56 0
AGTCAGACTGCACA SeuratProject 173 53 0
TCTGATACACGTGT SeuratProject 70 48 0
TGGTATCTAAACAG SeuratProject 64 36 0
GCAGCTCTGTTTCT SeuratProject 72 45 0
GATATAACACGCAT SeuratProject 52 36 0
AATGTTGACAGTCA SeuratProject 100 41 0
AGGTCATGAGTGTC SeuratProject 62 31 0
AGAGATGATCTCGC SeuratProject 191 61 0
GGGTAACTCTAGTG SeuratProject 101 41 0
CATGAGACACGGGA SeuratProject 51 26 0
TACGCCACTCCGAA SeuratProject 99 45 0
CTAAACCTGTGCAT SeuratProject 168 44 0
GTAAGCACTCATTC SeuratProject 67 33 0
TTGGTACTGAATCC SeuratProject 135 45 0
CATCATACGGAGCA SeuratProject 79 43 0
TACATCACGCTAAC SeuratProject 109 41 0
TTACCATGAATCGC SeuratProject 298 65 1
ATAGGAGAAACAGA SeuratProject 406 74 1
GCGCACGACTTTAC SeuratProject 213 48 1
ACTCGCACGAAAGT SeuratProject 231 49 1
ATTACCTGCCTTAT SeuratProject 463 77 1
CCCAACTGCAATCG SeuratProject 87 42 1
AAATTCGAATCACG SeuratProject 327 62 1
CCATCCGATTCGCC SeuratProject 224 50 1
TCCACTCTGAGCTT SeuratProject 361 76 1
CATCAGGATGCACA SeuratProject 353 80 1
CTAAACCTCTGACA SeuratProject 246 59 0
GATAGAGAAGGGTG SeuratProject 115 51 0
CTAACGGAACCGAT SeuratProject 189 53 0
AGATATACCCGTAA SeuratProject 187 61 0
TACTCTGAATCGAC SeuratProject 156 48 0
GCGCATCTTGCTCC SeuratProject 164 47 0
GTTGACGATATCGG SeuratProject 221 67 0
ACAGGTACTGGTGT SeuratProject 151 59 0
GGCATATGCTTATC SeuratProject 126 53 0
CATTACACCAACTG SeuratProject 316 65 0
TAGGGACTGAACTC SeuratProject 156 60 0
GCTCCATGAGAAGT SeuratProject 139 61 0
TACAATGATGCTAG SeuratProject 108 44 0
CTTCATGACCGAAT SeuratProject 41 32 0
CTGCCAACAGGAGC SeuratProject 146 47 0
TTGCATTGAGCTAC SeuratProject 104 40 0
AAGCAAGAGCTTAG SeuratProject 126 48 0
CGGCACGAACTCAG SeuratProject 94 55 0
GGTGGAGATTACTC SeuratProject 204 52 0
GGCCGATGTACTCT SeuratProject 99 45 0
CGTAGCCTGTATGC SeuratProject 371 75 1
TGAGCTGAATGCTG SeuratProject 387 83 1
CCTATAACGAGACG SeuratProject 139 50 1
ATAAGTTGGTACGT SeuratProject 99 42 1
AAGCGACTTTGACG SeuratProject 443 77 1
ACCAGTGAATACCG SeuratProject 417 75 0
ATTGCACTTGCTTT SeuratProject 502 81 1
CTAGGTGATGGTTG SeuratProject 324 76 1
GCACTAGACCTTTA SeuratProject 292 71 1
CATGCGCTAGTCAC SeuratProject 443 81 0
TTGAGGACTACGCA SeuratProject 787 88 0
ATACCACTCTAAGC SeuratProject 612 69 1
CATATAGACTAAGC SeuratProject 286 68 0
TTTAGCTGTACTCT SeuratProject 462 86 1
GACATTCTCCACCT SeuratProject 872 96 1
ACGTGATGCCATGA SeuratProject 709 94 1
ATTGTAGATTCCCG SeuratProject 745 84 1
GATAGAGATCACGA SeuratProject 328 72 1
AATGCGTGGACGGA SeuratProject 389 73 1
GCGTAAACACGGTT SeuratProject 754 83 0
ATTCAGCTCATTGG SeuratProject 212 38 0
GGCATATGGGGAGT SeuratProject 172 29 0
ATCATCTGACACCA SeuratProject 168 37 0
GTCATACTTCGCCT SeuratProject 210 33 0
TTACGTACGTTCAG SeuratProject 228 39 0
GAGTTGTGGTAGCT SeuratProject 527 47 0
GACGCTCTCTCTCG SeuratProject 202 30 0
AGTCTTACTTCGGA SeuratProject 157 29 0
GGAACACTTCAGAC SeuratProject 150 30 0
CTTGATTGATCTTC SeuratProject 233 76 1
letter.idents groups RNA_snn_res.1 cluster_2_markers1
ATGCCAGAACGACT A g2 0 -1.33722664
CATGGCCTGTGCAT A g1 0 -0.50364216
GAACCTGATGAACC B g2 0 -0.11497629
TGACTGGATTCTCA A g2 0 0.17070415
AGTCAGACTGCACA A g2 0 0.45524703
TCTGATACACGTGT A g1 0 -0.74228782
TGGTATCTAAACAG A g1 0 -0.32057176
GCAGCTCTGTTTCT A g1 0 0.43854514
GATATAACACGCAT A g1 0 0.33480306
AATGTTGACAGTCA A g1 0 -0.13294010
AGGTCATGAGTGTC A g2 2 -0.11576571
AGAGATGATCTCGC A g1 2 0.03312215
GGGTAACTCTAGTG A g2 2 -0.46989681
CATGAGACACGGGA A g2 2 0.06461428
TACGCCACTCCGAA A g2 2 -0.71578377
CTAAACCTGTGCAT A g1 2 1.74821041
GTAAGCACTCATTC A g2 2 -0.51715398
TTGGTACTGAATCC A g1 2 1.11666693
CATCATACGGAGCA A g1 2 -0.36919951
TACATCACGCTAAC A g2 2 -0.31473220
TTACCATGAATCGC B g1 1 0.18808088
ATAGGAGAAACAGA B g1 1 0.02324997
GCGCACGACTTTAC B g2 1 0.17251111
ACTCGCACGAAAGT B g2 1 -0.20030815
ATTACCTGCCTTAT B g1 1 0.90448669
CCCAACTGCAATCG B g2 1 -0.32371672
AAATTCGAATCACG B g2 1 -0.31797461
CCATCCGATTCGCC B g2 1 -0.32290368
TCCACTCTGAGCTT B g2 1 0.30717082
CATCAGGATGCACA B g1 1 0.36639313
CTAAACCTCTGACA A g1 0 0.28303109
GATAGAGAAGGGTG A g1 2 0.41631402
CTAACGGAACCGAT A g1 0 -0.16063845
AGATATACCCGTAA A g2 0 -0.79839288
TACTCTGAATCGAC A g1 0 0.02501654
GCGCATCTTGCTCC A g1 0 -1.05801258
GTTGACGATATCGG A g2 0 0.01909060
ACAGGTACTGGTGT A g1 0 0.05357335
GGCATATGCTTATC A g1 0 -0.55180166
CATTACACCAACTG A g2 0 0.15809224
TAGGGACTGAACTC A g1 0 0.16762845
GCTCCATGAGAAGT A g2 2 -1.07316109
TACAATGATGCTAG A g2 0 -0.87825328
CTTCATGACCGAAT A g2 0 -0.96000395
CTGCCAACAGGAGC A g1 2 -0.86055830
TTGCATTGAGCTAC A g2 2 -0.42909202
AAGCAAGAGCTTAG A g1 0 -0.59622949
CGGCACGAACTCAG A g2 0 -0.66099116
GGTGGAGATTACTC A g1 0 -0.04225170
GGCCGATGTACTCT A g2 0 -0.72243718
CGTAGCCTGTATGC B g1 1 0.99311443
TGAGCTGAATGCTG B g2 1 -0.36480818
CCTATAACGAGACG B g2 2 -0.13022704
ATAAGTTGGTACGT B g2 1 0.17692520
AAGCGACTTTGACG B g1 1 -0.14086788
ACCAGTGAATACCG A g1 1 0.17023860
ATTGCACTTGCTTT B g1 1 -0.26933878
CTAGGTGATGGTTG B g1 1 0.08355725
GCACTAGACCTTTA B g2 1 0.53324431
CATGCGCTAGTCAC A g1 0 -0.28413974
TTGAGGACTACGCA A g1 2 1.06270210
ATACCACTCTAAGC B g1 1 -0.17493615
CATATAGACTAAGC A g1 2 1.31511823
TTTAGCTGTACTCT B g1 1 0.12989081
GACATTCTCCACCT B g1 2 0.45770873
ACGTGATGCCATGA B g2 1 0.09650469
ATTGTAGATTCCCG B g2 1 0.46572139
GATAGAGATCACGA B g1 1 0.77577411
AATGCGTGGACGGA B g1 1 0.70095876
GCGTAAACACGGTT A g1 2 0.99945792
ATTCAGCTCATTGG A g2 0 -0.10901099
GGCATATGGGGAGT A g1 0 0.11826827
ATCATCTGACACCA A g2 0 -0.51384812
GTCATACTTCGCCT A g2 0 0.20837912
TTACGTACGTTCAG A g1 0 -0.27993443
GAGTTGTGGTAGCT A g1 0 0.15675206
GACGCTCTCTCTCG A g2 0 0.28180439
AGTCTTACTTCGGA A g1 0 0.01625193
GGAACACTTCAGAC A g2 0 0.36041270
CTTGATTGATCTTC B g1 1 0.72434944
Plot module scores; feature_list
contains genes that are
markers for g1
within cluster 2. The boxplot confirms the
results by showing higher module scores in cluster 2 of g1.
ggplot(pbmc_small@meta.data, aes(RNA_snn_res.1, cluster_2_markers1)) +
geom_boxplot() +
theme_minimal() +
facet_grid(~groups)
Visualise module scores on the UMAP.
pbmc_small <- RunUMAP(object = pbmc_small, dims = 1:19)
03:35:17 UMAP embedding parameters a = 0.9922 b = 1.112
03:35:17 Read 80 rows and found 19 numeric columns
03:35:17 Using Annoy for neighbor search, n_neighbors = 30
03:35:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
03:35:17 Writing NN index file to temp file /tmp/RtmplGp79N/file8b7960248475
03:35:17 Searching Annoy index using 4 threads, search_k = 3000
03:35:17 Annoy recall = 100%
03:35:17 Commencing smooth kNN distance calibration using 4 threads with target n_neighbors = 30
03:35:17 7 smooth knn distance failures
03:35:17 Initializing from normalized Laplacian + noise (using RSpectra)
03:35:17 Commencing optimization for 500 epochs, with 2664 positive edges
03:35:17 Optimization finished
cbind(
pbmc_small@meta.data,
pbmc_small@reductions$umap@cell.embeddings[, 1:2]
) |>
ggplot(aes(umap_1, umap_2, colour = cluster_2_markers1, shape = RNA_snn_res.1)) +
geom_point() +
theme_minimal() +
facet_grid(~groups)
sessionInfo()
R version 4.4.1 (2024-06-14)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.5 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; LAPACK version 3.10.0
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
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] future.apply_1.11.3 future_1.34.0 ggplot2_3.5.1
[4] presto_1.0.0 data.table_1.16.2 Rcpp_1.0.13
[7] bench_1.1.3 Seurat_5.1.0 SeuratObject_5.0.2
[10] sp_2.1-4 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] RColorBrewer_1.1-3 rstudioapi_0.17.1 jsonlite_1.8.9
[4] magrittr_2.0.3 spatstat.utils_3.1-0 farver_2.1.2
[7] rmarkdown_2.28 fs_1.6.4 vctrs_0.6.5
[10] ROCR_1.0-11 spatstat.explore_3.3-3 htmltools_0.5.8.1
[13] sass_0.4.9 sctransform_0.4.1 parallelly_1.38.0
[16] KernSmooth_2.23-24 bslib_0.8.0 htmlwidgets_1.6.4
[19] ica_1.0-3 plyr_1.8.9 plotly_4.10.4
[22] zoo_1.8-12 cachem_1.1.0 whisker_0.4.1
[25] igraph_2.1.2 mime_0.12 lifecycle_1.0.4
[28] pkgconfig_2.0.3 Matrix_1.7-0 R6_2.5.1
[31] fastmap_1.2.0 fitdistrplus_1.2-1 shiny_1.9.1
[34] digest_0.6.37 colorspace_2.1-1 patchwork_1.3.0
[37] ps_1.8.1 rprojroot_2.0.4 tensor_1.5
[40] RSpectra_0.16-2 irlba_2.3.5.1 labeling_0.4.3
[43] progressr_0.15.0 fansi_1.0.6 spatstat.sparse_3.1-0
[46] httr_1.4.7 polyclip_1.10-7 abind_1.4-8
[49] compiler_4.4.1 withr_3.0.2 fastDummies_1.7.4
[52] highr_0.11 R.utils_2.12.3 MASS_7.3-60.2
[55] tools_4.4.1 lmtest_0.9-40 httpuv_1.6.15
[58] goftest_1.2-3 R.oo_1.27.0 glue_1.8.0
[61] callr_3.7.6 nlme_3.1-164 promises_1.3.0
[64] grid_4.4.1 Rtsne_0.17 getPass_0.2-4
[67] cluster_2.1.6 reshape2_1.4.4 generics_0.1.3
[70] gtable_0.3.6 spatstat.data_3.1-2 R.methodsS3_1.8.2
[73] tidyr_1.3.1 utf8_1.2.4 spatstat.geom_3.3-3
[76] RcppAnnoy_0.0.22 ggrepel_0.9.6 RANN_2.6.2
[79] pillar_1.9.0 stringr_1.5.1 limma_3.62.1
[82] spam_2.11-0 RcppHNSW_0.6.0 later_1.3.2
[85] splines_4.4.1 dplyr_1.1.4 lattice_0.22-6
[88] survival_3.6-4 deldir_2.0-4 tidyselect_1.2.1
[91] miniUI_0.1.1.1 pbapply_1.7-2 knitr_1.48
[94] git2r_0.35.0 gridExtra_2.3 scattermore_1.2
[97] xfun_0.48 statmod_1.5.0 matrixStats_1.4.1
[100] stringi_1.8.4 lazyeval_0.2.2 yaml_2.3.10
[103] evaluate_1.0.1 codetools_0.2-20 tibble_3.2.1
[106] cli_3.6.3 uwot_0.2.2 xtable_1.8-4
[109] reticulate_1.39.0 munsell_0.5.1 processx_3.8.4
[112] jquerylib_0.1.4 globals_0.16.3 spatstat.random_3.3-2
[115] png_0.1-8 spatstat.univar_3.0-1 parallel_4.4.1
[118] dotCall64_1.2 listenv_0.9.1 viridisLite_0.4.2
[121] scales_1.3.0 ggridges_0.5.6 leiden_0.4.3.1
[124] purrr_1.0.2 rlang_1.1.4 cowplot_1.1.3