SS2 (for AG Hefendehl): Integration
Desirée Brösamle
Dec 2021
Last updated: 2022-06-27
Checks: 7 0
Knit directory: scSeq_Hefendehl/
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Preprocessing
QC metrics from AG Hefendehl (Stroke)
# Visualize QC metrics as a violin plot
Idents(plates_hefendehl) <- "Plate"
VlnPlot(plates_hefendehl, features = c("nFeature_RNA", "nCount_RNA", "percent.mito","percent.ribo"),
group.by = "Plate", pt.size = 1, ncol = 4, )
plot1 | plot2
QC metrics from AG Neher (own additional WT control)
# plates_neher <- subset(plates_neher, subset = Genotype == "wt")
# Visualize QC metrics as a violin plot
Idents(plates_neher) <- "Plate"
VlnPlot(plates_neher, features = c("nFeature_RNA", "nCount_RNA", "percent.mito","percent.ribo"),
group.by = "Plate",pt.size =1, ncol = 4)
plot1a | plot2a
Combine both seurat objects and filter cells that have unique feature counts (gene number) over 7000 or less than 200 and cells with >5% mitochondrial counts.
QC metrics after filtering
plot3 | plot4
Perform integrated analysis
PCA, tSNE and UMAP from the first 30 dimensions and with a resolution of 0.8
# Visualization
Idents(samples.integrated) <- "seurat_clusters"
DimPlot(samples.integrated, reduction = "umap", split.by = "Treatment")
p1 + p1a + p1b
DimPlot(samples.integrated, group.by = "seurat_clusters", pt.size =1, label = T) + NoLegend()
DimPlot(samples.integrated, reduction = "umap", split.by = "Plate", pt.size = 1)
DimPlot(samples.integrated, reduction = "umap", split.by = "Genotype_Treatment", pt.size =1)
Cell cycle scoring
First, a score is assigned to each cell (Tirosh et al. 2016), based on its expression of G2/M and S phase markers. These markers should be anticorrelated in their expression levels and cells expression neither are likely not cycling and in G1 phase. Note: For downstream cell cylce regression the quantitative scores for G2/M and S phase are used, not the dicrete classification.
Cluster distribution
DimPlot(object = samples.integrated, pt.size = 1,reduction = "umap", group.by="Age",label = F) +
ggtitle("Cluster distribution according to Age") | DimPlot(object = samples.integrated, pt.size = 1,reduction = "umap", group.by="Genotype_Treatment",label = F) +
ggtitle("Cluster distribution according to Genotype + Treatment")
DimPlot(object = samples.integrated, pt.size = 1,reduction = "umap", group.by="Plate",label = F) +
ggtitle("Cluster distribution according to Plate") | DimPlot(object = samples.integrated, pt.size = 1,reduction = "umap", group.by = "seurat_clusters", label = T) +
ggtitle("Clustering")
Idents(samples.integrated) <- "seurat_clusters"
# use same colors for clusters as in plots
require(scales)
identities <- levels(samples.integrated$seurat_clusters) # Create vector with levels of object@ident
cluster_colors <- hue_pal()(length(identities)) # Create vector of default ggplot2 colors
# number of cells in each cluster
cluster_nCell <- as.data.frame.matrix(table(samples.integrated$seurat_clusters,
samples.integrated$Genotype_Age))
cluster_nCell["Total" ,] = colSums(cluster_nCell)
cluster_nCell APPPS1+_10mo APPPS1+_9mo MX04+_10mo MX04+_9mo wt_10mo wt_17mo wt_9mo
0 42 57 20 18 15 20 34
1 23 38 3 7 9 68 16
2 22 29 2 3 2 31 18
3 14 25 12 5 6 29 9
4 4 6 1 0 1 11 3
5 3 4 5 1 5 3 3
6 6 10 0 0 3 0 3
Total 114 169 43 34 41 162 86# % of cells in each cluster , grouped by genotype_Age
cluster_percent_Cell <- data.frame(round((prop.table(x = table(samples.integrated$seurat_clusters,
samples.integrated$Genotype_Age), margin = 2)*100),2))
colnames(cluster_percent_Cell) <- c("Genotype_Age", "Celltype", "Frequency")
cluster_percent_Cell Genotype_Age Celltype Frequency
1 0 APPPS1+_10mo 36.84
2 1 APPPS1+_10mo 20.18
3 2 APPPS1+_10mo 19.30
4 3 APPPS1+_10mo 12.28
5 4 APPPS1+_10mo 3.51
6 5 APPPS1+_10mo 2.63
7 6 APPPS1+_10mo 5.26
8 0 APPPS1+_9mo 33.73
9 1 APPPS1+_9mo 22.49
10 2 APPPS1+_9mo 17.16
11 3 APPPS1+_9mo 14.79
12 4 APPPS1+_9mo 3.55
13 5 APPPS1+_9mo 2.37
14 6 APPPS1+_9mo 5.92
15 0 MX04+_10mo 46.51
16 1 MX04+_10mo 6.98
17 2 MX04+_10mo 4.65
18 3 MX04+_10mo 27.91
19 4 MX04+_10mo 2.33
20 5 MX04+_10mo 11.63
21 6 MX04+_10mo 0.00
22 0 MX04+_9mo 52.94
23 1 MX04+_9mo 20.59
24 2 MX04+_9mo 8.82
25 3 MX04+_9mo 14.71
26 4 MX04+_9mo 0.00
27 5 MX04+_9mo 2.94
28 6 MX04+_9mo 0.00
29 0 wt_10mo 36.59
30 1 wt_10mo 21.95
31 2 wt_10mo 4.88
32 3 wt_10mo 14.63
33 4 wt_10mo 2.44
34 5 wt_10mo 12.20
35 6 wt_10mo 7.32
36 0 wt_17mo 12.35
37 1 wt_17mo 41.98
38 2 wt_17mo 19.14
39 3 wt_17mo 17.90
40 4 wt_17mo 6.79
41 5 wt_17mo 1.85
42 6 wt_17mo 0.00
43 0 wt_9mo 39.53
44 1 wt_9mo 18.60
45 2 wt_9mo 20.93
46 3 wt_9mo 10.47
47 4 wt_9mo 3.49
48 5 wt_9mo 3.49
49 6 wt_9mo 3.49# Grouped barchart of cell proportions
ggplot(cluster_percent_Cell, aes(fill=Genotype_Age, y=Frequency, x=Celltype)) +
geom_bar(position="dodge", stat="identity")+
ggtitle("Cell distribution according to Genotype_Age [%]") +
theme(axis.text.x = element_text(angle = 45, hjust=1, vjust=1)) +
xlab("")
# % of cells in each cluster , grouped by genotype_treatment
cluster_percent_Cell <- data.frame(round((prop.table(x = table(samples.integrated$seurat_clusters,
samples.integrated$Genotype_Treatment), margin = 2)*100),2))
colnames(cluster_percent_Cell) <- c("Genotype_Treatment", "Celltype", "Frequency")
cluster_percent_Cell Genotype_Treatment Celltype Frequency
1 0 APPPS1+_Ctrl 30.32
2 1 APPPS1+_Ctrl 23.87
3 2 APPPS1+_Ctrl 21.94
4 3 APPPS1+_Ctrl 14.84
5 4 APPPS1+_Ctrl 5.81
6 5 APPPS1+_Ctrl 1.94
7 6 APPPS1+_Ctrl 1.29
8 0 APPPS1+_Stroke 40.62
9 1 APPPS1+_Stroke 18.75
10 2 APPPS1+_Stroke 13.28
11 3 APPPS1+_Stroke 12.50
12 4 APPPS1+_Stroke 0.78
13 5 APPPS1+_Stroke 3.12
14 6 APPPS1+_Stroke 10.94
15 0 MX04+_Ctrl 50.68
16 1 MX04+_Ctrl 10.96
17 2 MX04+_Ctrl 6.85
18 3 MX04+_Ctrl 21.92
19 4 MX04+_Ctrl 1.37
20 5 MX04+_Ctrl 8.22
21 6 MX04+_Ctrl 0.00
22 0 MX04+_Stroke 25.00
23 1 MX04+_Stroke 50.00
24 2 MX04+_Stroke 0.00
25 3 MX04+_Stroke 25.00
26 4 MX04+_Stroke 0.00
27 5 MX04+_Stroke 0.00
28 6 MX04+_Stroke 0.00
29 0 wt_Ctrl 12.35
30 1 wt_Ctrl 41.98
31 2 wt_Ctrl 19.14
32 3 wt_Ctrl 17.90
33 4 wt_Ctrl 6.79
34 5 wt_Ctrl 1.85
35 6 wt_Ctrl 0.00
36 0 wt_Stroke 38.58
37 1 wt_Stroke 19.69
38 2 wt_Stroke 15.75
39 3 wt_Stroke 11.81
40 4 wt_Stroke 3.15
41 5 wt_Stroke 6.30
42 6 wt_Stroke 4.72# Grouped barchart of cell proportions
ggplot(cluster_percent_Cell, aes(fill=Genotype_Treatment, y=Frequency, x=Celltype)) +
geom_bar(position="dodge", stat="identity")+
ggtitle("Cell distribution according to Genotype_Treatment [%]") +
theme(axis.text.x = element_text(angle = 45, hjust=1, vjust=1)) +
xlab("")
Cell type identification
Finding differentially expressed features (cluster biomarkers)
Idents(samples.integrated) <- samples.integrated$seurat_clusters
DoHeatmap(samples.integrated, features = top20$gene) + NoLegend() + ggtitle("Top20 cluster marker genes")
SingleR
SingleR is an automatic annotation method for (scRNAseq) data (Aran et al. 2019). Given a reference dataset of samples (single-cell or bulk) with known labels, it labels new cells from a test dataset based on similarity to the reference set. Here we use the built-in references “Immgen” (830 microarray samples of sorted hematopoetic and immune cell populations) and “Mouse RNA-Seq” (358 non-specific mouse RNA-seq samples).
Immgen reference
Section Skipped as pred.immgen object is not available
MouseRNA-Seq reference Section Skipped as pred.mouseRNA object is not available
# number of cells in each cluster
cluster_nCell <- data.frame(table(samples.integrated$MouseRNASeq_sc_labels,samples.integrated$Genotype_Treatment))
colnames(cluster_nCell) <- c("Genotype_Treatment", "MouseRNASeq_sc_labels", "Number")
#cluster_nCell["Total" ,] = colSums(cluster_nCell)
# cluster_nCell
# % of cells in each cluster
cluster_percent_Cell <- data.frame(round((prop.table(x = table(samples.integrated$MouseRNASeq_sc_labels,
samples.integrated$Genotype_Treatment), margin = 2)*100),2))
colnames(cluster_percent_Cell) <- c("Genotype_Treatment", "MouseRNASeq_sc_labels", "Frequency")
# cluster_percent_Cell
# Grouped barchart of absolute cell numbers
ggplot(cluster_nCell, aes(fill=Genotype_Treatment, y=Number, x=MouseRNASeq_sc_labels)) +
geom_bar(position="dodge", stat="identity") +
ggtitle("Cell distribution according to MouseRNASeq reference (absolut values)") +
theme(axis.text.x = element_text(angle = 45, hjust=1, vjust=0.5)) +
xlab("")
# Grouped barchart of cell proportions
ggplot(cluster_percent_Cell, aes(fill=Genotype_Treatment, y=Frequency, x=MouseRNASeq_sc_labels)) +
geom_bar(position="dodge", stat="identity")+
ggtitle("Cell distribution according to MouseRNASeq reference [%]") +
theme(axis.text.x = element_text(angle = 45, hjust=1, vjust=0.5)) +
xlab("")
# number of cells in each cluster
cluster_nCell <- data.frame(table(samples.integrated$Immgen_sc_labels,samples.integrated$Treatment))
colnames(cluster_nCell) <- c("Treatment", "Immgen_sc_labels", "Number")
#cluster_nCell["Total" ,] = colSums(cluster_nCell)
# cluster_nCell
# % of cells in each cluster
cluster_percent_Cell <- data.frame(round((prop.table(x = table(samples.integrated$Immgen_sc_labels,
samples.integrated$Treatment), margin = 2)*100),2))
colnames(cluster_percent_Cell) <- c("Treatment", "Immgen_sc_labels", "Frequency")
# cluster_percent_Cell
# Grouped barchart of absolute cell numbers
ggplot(cluster_nCell, aes(fill=Treatment, y=Number, x=Immgen_sc_labels)) +
geom_bar(position="dodge", stat="identity") +
ggtitle("Cell distribution according to Immgen reference (absolut values)") +
theme(axis.text.x = element_text(angle = 45, hjust=1, vjust=0.5)) +
xlab("")
# Grouped barchart of cell proportions
ggplot(cluster_percent_Cell, aes(fill=Treatment, y=Frequency, x=Immgen_sc_labels)) +
geom_bar(position="dodge", stat="identity")+
ggtitle("Cell distribution according to Immgen reference [%]") +
theme(axis.text.x = element_text(angle = 45, hjust=1, vjust=0.5)) +
xlab("")
Renaming Clusters
DimPlot(samples.integrated, reduction = "umap", label=T) + NoLegend()
Zoom into Microglia clusters
A second Seurat cluster is generated, where all non-microglial immune cells are removed according to the mouseRNASeq_sc_labels.
p3 + p3a
DAMs (Cell. 2017 Jun 15;169(7):1276-1290.e17. doi: 10.1016/j.cell.2017.05.018)
DAM plotting
p5 | p5aWarning: Use of `expr_df[[axes[1]]]` is discouraged. Use `.data[[axes[1]]]`
instead.Warning: Use of `expr_df[[axes[2]]]` is discouraged. Use `.data[[axes[2]]]`
instead.
Renaming clusters
Idents(microglia) <- "Celltype"
DimPlot(microglia, reduction = "umap", label = TRUE, pt.size = 1)+ NoLegend()
Signature enrichment
DEGs and GSEA between microglia of different genotypes
WT vs App
WT vs MX04+
MX04+ vs App
DEGs and GSEA of microglia of different genotype and treatment
WT_Ctrl vs WT_Stroke
APP_Ctrl vs APP_Stroke
WT_Ctrl vs APP_Ctrl
WT_Stroke vs APP_Stroke
DEGs and GSEA of each cluster
Microglia_0
Only works if ReloadAllData_Harmony_Hefendehl_Stroke.RData is loaded. Otherwise, an error message will occur.
Microglia_1
Microglia_2
Microglia_4
DAM
Microglia_5
Only works if ReloadAllData_Harmony_Hefendehl_Stroke.RData is loaded. Otherwise, an error message will occur.
Genotype + Treatment comparison in each cluster
Microglia_0
Microglia_1
Microglia_2
DAM?
Microglia_4
Microglia_5
sessionInfo()R version 4.2.0 (2022-04-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur/Monterey 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] scales_1.2.0 pheatmap_1.0.12
[3] EnhancedVolcano_1.14.0 ggrepel_0.9.1
[5] SingleR_1.10.0 SummarizedExperiment_1.26.1
[7] Biobase_2.56.0 GenomicRanges_1.48.0
[9] GenomeInfoDb_1.32.2 IRanges_2.30.0
[11] S4Vectors_0.34.0 BiocGenerics_0.42.0
[13] MatrixGenerics_1.8.0 matrixStats_0.62.0
[15] ggplot2_3.3.6 sp_1.5-0
[17] SeuratObject_4.1.0 Seurat_4.1.1
[19] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] plyr_1.8.7 igraph_1.3.2
[3] lazyeval_0.2.2 splines_4.2.0
[5] BiocParallel_1.30.3 listenv_0.8.0
[7] scattermore_0.8 digest_0.6.29
[9] htmltools_0.5.2 fansi_1.0.3
[11] magrittr_2.0.3 ScaledMatrix_1.4.0
[13] tensor_1.5 cluster_2.1.3
[15] ROCR_1.0-11 globals_0.15.0
[17] spatstat.sparse_2.1-1 colorspace_2.0-3
[19] xfun_0.31 dplyr_1.0.9
[21] callr_3.7.0 crayon_1.5.1
[23] RCurl_1.98-1.7 jsonlite_1.8.0
[25] progressr_0.10.1 spatstat.data_2.2-0
[27] survival_3.3-1 zoo_1.8-10
[29] glue_1.6.2 polyclip_1.10-0
[31] gtable_0.3.0 zlibbioc_1.42.0
[33] XVector_0.36.0 leiden_0.4.2
[35] DelayedArray_0.22.0 BiocSingular_1.12.0
[37] future.apply_1.9.0 abind_1.4-5
[39] DBI_1.1.3 spatstat.random_2.2-0
[41] miniUI_0.1.1.1 Rcpp_1.0.8.3
[43] viridisLite_0.4.0 xtable_1.8-4
[45] reticulate_1.25 spatstat.core_2.4-4
[47] rsvd_1.0.5 htmlwidgets_1.5.4
[49] httr_1.4.3 RColorBrewer_1.1-3
[51] ellipsis_0.3.2 ica_1.0-2
[53] farver_2.1.0 pkgconfig_2.0.3
[55] sass_0.4.1 uwot_0.1.11
[57] deldir_1.0-6 utf8_1.2.2
[59] labeling_0.4.2 tidyselect_1.1.2
[61] rlang_1.0.2 reshape2_1.4.4
[63] later_1.3.0 munsell_0.5.0
[65] tools_4.2.0 cli_3.3.0
[67] generics_0.1.2 ggridges_0.5.3
[69] evaluate_0.15 stringr_1.4.0
[71] fastmap_1.1.0 yaml_2.3.5
[73] goftest_1.2-3 processx_3.6.1
[75] knitr_1.39 fs_1.5.2
[77] fitdistrplus_1.1-8 purrr_0.3.4
[79] RANN_2.6.1 sparseMatrixStats_1.8.0
[81] pbapply_1.5-0 future_1.26.1
[83] nlme_3.1-158 whisker_0.4
[85] mime_0.12 compiler_4.2.0
[87] rstudioapi_0.13 plotly_4.10.0
[89] png_0.1-7 spatstat.utils_2.3-1
[91] tibble_3.1.7 bslib_0.3.1
[93] stringi_1.7.6 highr_0.9
[95] ps_1.7.1 rgeos_0.5-9
[97] lattice_0.20-45 Matrix_1.4-1
[99] vctrs_0.4.1 pillar_1.7.0
[101] lifecycle_1.0.1 spatstat.geom_2.4-0
[103] lmtest_0.9-40 jquerylib_0.1.4
[105] BiocNeighbors_1.14.0 RcppAnnoy_0.0.19
[107] data.table_1.14.2 cowplot_1.1.1
[109] bitops_1.0-7 irlba_2.3.5
[111] httpuv_1.6.5 patchwork_1.1.1
[113] R6_2.5.1 promises_1.2.0.1
[115] KernSmooth_2.23-20 gridExtra_2.3
[117] parallelly_1.32.0 codetools_0.2-18
[119] MASS_7.3-57 assertthat_0.2.1
[121] rprojroot_2.0.3 withr_2.5.0
[123] sctransform_0.3.3 GenomeInfoDbData_1.2.8
[125] mgcv_1.8-40 parallel_4.2.0
[127] beachmat_2.12.0 grid_4.2.0
[129] rpart_4.1.16 tidyr_1.2.0
[131] DelayedMatrixStats_1.18.0 rmarkdown_2.14
[133] Rtsne_0.16 git2r_0.30.1
[135] getPass_0.2-2 shiny_1.7.1