CO_analysis
petrsh
2022-01-10
Last updated: 2022-01-10
Checks: 6 1
Knit directory: AD_CO_scRNAseq/
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Dependencies
library(Seurat)
library(dplyr)
library(here)
library(ggplot2)
library(ggthemes)
library(SeuratDisk)
# not loaded but required:
# scater - function plotExpression
# SoupX - function quickMarkers
# Nebulosa - function plot_densityImporting CellRanger output and cell-level quality control
D1 <- Read10X(here("data","CO"))
Sat_D1 <- CreateSeuratObject(D1, min.cells = 3)
Sat_D1 <- PercentageFeatureSet(Sat_D1, pattern = "^MT-", col.name = "percent.mt")
Sat_D1 <- PercentageFeatureSet(Sat_D1, pattern = "^RP[SL][[:digit:]]|^RPLP[[:digit:]]|^RPSA", col.name = "percent.ribo")
Idents(Sat_D1) <- rep("CO", length(Sat_D1$orig.ident))
VlnPlot(Sat_D1, c("nFeature_RNA", "nCount_RNA", "percent.mt", "percent.ribo"), ncol = 2, cols = "#4EA2A2") &
theme_tufte() &
theme(legend.position="none")
summary(Sat_D1$nFeature_RNA) Min. 1st Qu. Median Mean 3rd Qu. Max.
48 1552 3190 3330 4576 11885 summary(Sat_D1$nCount_RNA) Min. 1st Qu. Median Mean 3rd Qu. Max.
500 3028 8894 12929 16344 133090 summary(Sat_D1$percent.mt) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 2.516 3.860 10.233 6.094 90.238 summary(Sat_D1$percent.ribo) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.692 11.338 18.710 18.207 24.381 69.609 plot1 <- FeatureScatter(Sat_D1, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(Sat_D1, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
Sat_D1 <- subset(Sat_D1, subset = nFeature_RNA > 1000 & nFeature_RNA < 7000 & percent.mt < 10)Normalization
Sat_D1 <- SCTransform(Sat_D1, verbose = FALSE)Cell-cycle scoring and dimension reduction
Sat_D1 <- CellCycleScoring(Sat_D1, s.features = cc.genes.updated.2019$s.genes,
g2m.features = cc.genes.updated.2019$g2m.genes, set.ident = TRUE)
Sat_D1 <- RunPCA(Sat_D1, features = VariableFeatures(Sat_D1))
Sat_D1 <- RunUMAP(Sat_D1, dims = 1:15)
DimPlot(Sat_D1, group.by = "Phase") +
theme_tufte()
Clustering
set.seed(42)
Sat_D1 <- FindNeighbors(Sat_D1, dims=1:15, verbose = FALSE)
Sat_D1 <- FindClusters(Sat_D1, resolution = 0.8, verbose = FALSE)
autumn_palette <- c("#751A33", "#B34233", "#D28F33", "#D4B95E", "#4EA2A2", "#506432",
"#1A8693", "#cbdfbd", "#d4e09b", "#f6f4d2", "#f19c79", "#a44a3f")
DimPlot(Sat_D1, label = T, group.by = "SCT_snn_res.0.8", cols = autumn_palette) +
theme_tufte()
Cluster-level quality control
VlnPlot(Sat_D1, c("nFeature_RNA", "nCount_RNA", "percent.mt", "percent.ribo"), ncol = 2, cols = autumn_palette) &
theme_tufte() &
theme(legend.position="none")
We can see that the cluster 5 has suspiciously low percentage of expressed mitochondrial genes and low number of detected genes. We will explore the markers of this cluster before proceeding with the further analyses.
FindMarkers(Sat_D1, ident.1 = "5", logfc.threshold = 0.5, features = VariableFeatures(Sat_D1), verbose = FALSE) %>%
head(n=20) p_val avg_log2FC pct.1 pct.2 p_val_adj
MT-CO3 2.097968e-74 -1.8344515 0.901 0.994 4.427341e-70
MT-CO2 4.034208e-74 -1.8511021 0.885 0.996 8.513389e-70
MT-ATP6 2.939489e-70 -1.7831739 0.921 0.994 6.203205e-66
MT-CYB 3.381054e-67 -1.6618836 0.906 0.993 7.135038e-63
MT-CO1 3.307518e-64 -1.5518968 0.906 0.996 6.979856e-60
MT-ND3 3.844274e-64 -1.5954677 0.859 0.993 8.112571e-60
MT-ND4 9.223773e-62 -1.4133227 0.895 0.995 1.946493e-57
SNHG12 4.812971e-60 1.4208926 0.607 0.168 1.015681e-55
GADD45B 1.550028e-57 1.6002299 0.382 0.060 3.271025e-53
DDIT3 4.844988e-54 2.1528115 0.723 0.299 1.022438e-49
EIF1 7.314929e-54 1.3165931 0.995 1.000 1.543669e-49
MT-ND1 6.295736e-51 -1.1313272 0.853 0.990 1.328589e-46
SLC3A2 8.953602e-49 1.5061217 0.927 0.609 1.889479e-44
HSPA6 3.516092e-46 0.6778029 0.131 0.004 7.420010e-42
IGFBP2 1.542992e-42 -1.1962256 0.702 0.922 3.256176e-38
PPP1R15A 2.662413e-41 1.2090298 0.545 0.189 5.618490e-37
ATF4 4.877250e-41 1.0996582 0.848 0.544 1.029246e-36
MT-ND2 2.363002e-40 -0.9112057 0.916 0.993 4.986643e-36
ZFAND2A 7.961523e-40 0.8633638 0.482 0.142 1.680120e-35
SNHG1 1.601679e-39 1.3140208 0.874 0.612 3.380024e-35Among top upregulated genes we can find the genes that are involved in the pro-apoptotic signaling. Given the low expression of mitochondrial genes and low number of detected genes, we think that these are damaged cells without cytoplasmatic membrane and therefore we will remove this cluster and redo the preprocessing.
Sat_D1_sub <- subset(Sat_D1, SCT_snn_res.0.8 == 5, invert=T)
Sat_D1_sub <- SCTransform(Sat_D1_sub, assay = 'RNA', new.assay.name = 'SCT', verbose = FALSE)
Sat_D1_sub <- RunPCA(Sat_D1_sub, features = VariableFeatures(Sat_D1_sub))
Sat_D1_sub <- RunUMAP(Sat_D1_sub, dims = 1:15)
DimPlot(Sat_D1_sub, label = T, group.by = "Phase") +
theme_tufte()
set.seed(42)
Sat_D1_sub <- FindNeighbors(Sat_D1_sub, dims=1:15, verbose = FALSE)
Sat_D1_sub <- FindClusters(Sat_D1_sub, resolution = 0.8, verbose = FALSE)
DimPlot(Sat_D1_sub, label = T, group.by = "SCT_snn_res.0.8", cols = autumn_palette) +
theme_tufte()
VlnPlot(Sat_D1_sub, c("nFeature_RNA", "nCount_RNA", "percent.mt", "percent.ribo"), cols = autumn_palette ,ncol = 2) &
theme_tufte() &
theme(legend.position="none")
# save cell barcodes to subset the aggregated dataset
write.csv(colnames(Sat_D1_sub), here("output", "CO_filtered_barcodes.csv"),
row.names = FALSE)
# save clusters
write.csv(Sat_D1_sub$SCT_snn_res.0.8, here("output", "CO_clusters_res08.csv"),
row.names = TRUE)Regress out the difference between S and G2M score.
Sat_D1_sub$cc_difference <- Sat_D1_sub$S.Score - Sat_D1_sub$G2M.Score
Sat_D1_sub <- SCTransform(Sat_D1_sub, assay = 'RNA', new.assay.name = 'SCT', vars.to.regress = "cc_difference", verbose = FALSE)
Sat_D1_sub <- RunPCA(Sat_D1_sub, features = VariableFeatures(Sat_D1_sub))
Sat_D1_sub <- RunUMAP(Sat_D1_sub, dims = 1:15)
DimPlot(Sat_D1_sub, group.by = "Phase") +
theme_tufte()
set.seed(42)
Sat_D1_sub <- FindNeighbors(Sat_D1_sub, dims=1:15, verbose = FALSE)
Sat_D1_sub <- FindClusters(Sat_D1_sub, resolution = 0.8, verbose = FALSE)
Sat_D1_sub$annot <- as.character(Sat_D1_sub$SCT_snn_res.0.8)
DimPlot(Sat_D1_sub, label = F, group.by = "SCT_snn_res.0.8", cols = autumn_palette) +
theme_tufte()
VlnPlot(Sat_D1_sub, c("nFeature_RNA", "nCount_RNA", "percent.mt", "percent.ribo"), cols = autumn_palette ,ncol = 2) &
theme_tufte() &
theme(legend.position="none")
# save Sat_D1_sub for SingleR classification
#saveRDS(Sat_D1_sub, here("output", "Sat_D1_sub.rds"))Identification of clusters specific genes using tf-idf (term frequency–inverse document frequency)
CO_markers <- SoupX::quickMarkers(Sat_D1_sub@assays$RNA@counts, Sat_D1_sub$SCT_snn_res.0.8, N = 6)
SCE_D1_sub <- as.SingleCellExperiment(Sat_D1_sub)
scater::plotExpression(SCE_D1_sub, features = unique(CO_markers$gene)[1:10], x = "SCT_snn_res.0.8", colour_by = "SCT_snn_res.0.8") +
scale_color_manual(values = autumn_palette)
scater::plotExpression(SCE_D1_sub, features = unique(CO_markers$gene)[11:20], x = "SCT_snn_res.0.8", colour_by = "SCT_snn_res.0.8") +
scale_color_manual(values = autumn_palette)
scater::plotExpression(SCE_D1_sub, features = unique(CO_markers$gene)[21:30], x = "SCT_snn_res.0.8",colour_by = "SCT_snn_res.0.8") +
scale_color_manual(values = autumn_palette)
scater::plotExpression(SCE_D1_sub, features = unique(CO_markers$gene)[31:33], x = "SCT_snn_res.0.8",colour_by = "SCT_snn_res.0.8") +
scale_color_manual(values = autumn_palette)
Plot genes selected from the literature
genes_to_plot <- c("MKI67", "SOX2", "SOX1", "PAX6", "NES", "DCX", "TUBB3", "MAP2",
"MAPT", "OLIG2", "PLP1", "S100B", "TMEM119", "RAX", "SIX3",
"GAD2","ASCL1", "NEUROD1", "NEUROD4", "NEUROG1", "EOMES", "APOE")
# not expressed in this dataset: "LY6H","PDGFR1", "NEUROG4"
for (i in genes_to_plot){
g <- Nebulosa::plot_density(Sat_D1_sub, features = i) +
theme_tufte() +
theme(legend.position="right", axis.ticks = element_blank())
print(g)
}
genes_to_plot <- c("HES1", "HES4", "HES5", "NOTCH1")
for (i in genes_to_plot){
g <- Nebulosa::plot_density(Sat_D1_sub, features = i) +
theme_tufte() +
theme(legend.position="right", axis.ticks = element_blank())
print(g)
}
Subset non-proliferating NSC, neurons and radial glia for CellRank analysis
#saveRDS(Sat_D1_sub, here("output", "Sat_D1_sub.rds"))
Sat_D1_sub_neuro <- subset(Sat_D1_sub, SCT_snn_res.0.8 %in% c(0,4,5,9,6,10), invert = TRUE)
#
tmp_umap <- Sat_D1_sub_neuro@reductions$umap@cell.embeddings
tmp_umap <- tmp_umap[tmp_umap[,1] < 0,]
Sat_D1_sub_neuro <- subset(Sat_D1_sub_neuro, cells = rownames(tmp_umap))
set.seed(42)
Sat_D1_sub_neuro <- FindNeighbors(Sat_D1_sub_neuro, dims=1:15, verbose = FALSE)
Sat_D1_sub_neuro <- FindClusters(Sat_D1_sub_neuro, resolution = 0.8, verbose = FALSE)
DimPlot(Sat_D1_sub_neuro, label = F, group.by = "SCT_snn_res.0.8", cols = autumn_palette) +
theme_tufte()
VlnPlot(Sat_D1_sub_neuro, c("nFeature_RNA", "nCount_RNA", "percent.mt", "percent.ribo"), ncol = 2, cols = autumn_palette) &
theme_tufte() &
theme(legend.position="none")
# save cell barcodes
write.csv(colnames(Sat_D1_sub_neuro), here("output", "CO_sub_neuro.csv"),
row.names = FALSE)
# save clusters
write.csv(Sat_D1_sub_neuro$SCT_snn_res.0.8, here("output", "CO_sub_neuro_res08.csv"),
row.names = TRUE)
# convert to h5ad for CellRank
# Sat_D1_sub_neuro <- RenameCells(Sat_D1_sub_neuro, new.names = paste("D1_", sub("-1", "", colnames(Sat_D1_sub_neuro)), sep=""))
# SaveH5Seurat(Sat_D1_sub_neuro, filename = here("output","Sat_D1_sub.h5Seurat"))
# Convert(here("output","Sat_D1_sub.h5Seurat"), dest="h5ad")Identification of clusters specific genes using tf-idf (term frequency–inverse document frequency)
CO_sub_markers <- SoupX::quickMarkers(Sat_D1_sub_neuro@assays$RNA@counts, Sat_D1_sub_neuro$SCT_snn_res.0.8, N = 6)
SCE_D1_sub_neuro <- as.SingleCellExperiment(Sat_D1_sub_neuro)
scater::plotExpression(SCE_D1_sub_neuro, features = unique(CO_sub_markers$gene)[1:10], x = "SCT_snn_res.0.8", colour_by = "SCT_snn_res.0.8") +
scale_color_manual(values = autumn_palette)
scater::plotExpression(SCE_D1_sub_neuro, features = unique(CO_sub_markers$gene)[11:20], x = "SCT_snn_res.0.8", colour_by = "SCT_snn_res.0.8") +
scale_color_manual(values = autumn_palette)
sessionInfo()R version 4.1.1 (2021-08-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.3 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
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
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] SeuratDisk_0.0.0.9019 ggthemes_4.2.4 ggplot2_3.3.5
[4] here_1.0.1 dplyr_1.0.7 SeuratObject_4.0.2
[7] Seurat_4.0.3.9011 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] utf8_1.2.1 ks_1.13.2
[3] reticulate_1.20 tidyselect_1.1.1
[5] htmlwidgets_1.5.3 grid_4.1.1
[7] BiocParallel_1.26.1 Rtsne_0.15
[9] munsell_0.5.0 ScaledMatrix_1.0.0
[11] codetools_0.2-18 ica_1.0-2
[13] future_1.21.0 miniUI_0.1.1.1
[15] withr_2.4.2 colorspace_2.0-2
[17] Biobase_2.52.0 highr_0.9
[19] knitr_1.33 stats4_4.1.1
[21] SingleCellExperiment_1.14.1 ROCR_1.0-11
[23] tensor_1.5 listenv_0.8.0
[25] MatrixGenerics_1.4.0 labeling_0.4.2
[27] git2r_0.28.0 GenomeInfoDbData_1.2.6
[29] polyclip_1.10-0 bit64_4.0.5
[31] farver_2.1.0 Nebulosa_1.2.0
[33] rprojroot_2.0.2 parallelly_1.26.1
[35] vctrs_0.3.8 generics_0.1.0
[37] xfun_0.24 R6_2.5.0
[39] GenomeInfoDb_1.28.1 ggbeeswarm_0.6.0
[41] rsvd_1.0.5 hdf5r_1.3.3
[43] bitops_1.0-7 spatstat.utils_2.2-0
[45] DelayedArray_0.18.0 assertthat_0.2.1
[47] promises_1.2.0.1 scales_1.1.1
[49] beeswarm_0.4.0 gtable_0.3.0
[51] beachmat_2.8.0 globals_0.14.0
[53] goftest_1.2-2 rlang_0.4.11
[55] splines_4.1.1 lazyeval_0.2.2
[57] spatstat.geom_2.2-2 yaml_2.2.1
[59] reshape2_1.4.4 abind_1.4-5
[61] httpuv_1.6.1 tools_4.1.1
[63] ellipsis_0.3.2 spatstat.core_2.3-0
[65] jquerylib_0.1.4 RColorBrewer_1.1-2
[67] BiocGenerics_0.38.0 ggridges_0.5.3
[69] Rcpp_1.0.6 plyr_1.8.6
[71] sparseMatrixStats_1.4.0 zlibbioc_1.38.0
[73] purrr_0.3.4 RCurl_1.98-1.3
[75] rpart_4.1-15 deldir_0.2-10
[77] pbapply_1.4-3 viridis_0.6.1
[79] cowplot_1.1.1 S4Vectors_0.30.0
[81] zoo_1.8-9 SummarizedExperiment_1.22.0
[83] ggrepel_0.9.1 cluster_2.1.2
[85] fs_1.5.0 magrittr_2.0.1
[87] data.table_1.14.0 RSpectra_0.16-0
[89] scattermore_0.7 lmtest_0.9-38
[91] RANN_2.6.1 mvtnorm_1.1-2
[93] fitdistrplus_1.1-5 matrixStats_0.59.0
[95] patchwork_1.1.1 mime_0.11
[97] evaluate_0.14 xtable_1.8-4
[99] mclust_5.4.7 IRanges_2.26.0
[101] gridExtra_2.3 compiler_4.1.1
[103] scater_1.20.1 tibble_3.1.2
[105] KernSmooth_2.23-20 crayon_1.4.1
[107] htmltools_0.5.1.1 mgcv_1.8-38
[109] later_1.2.0 tidyr_1.1.3
[111] SoupX_1.5.2 DBI_1.1.1
[113] MASS_7.3-54 Matrix_1.4-0
[115] cli_3.0.0 parallel_4.1.1
[117] igraph_1.2.6 GenomicRanges_1.44.0
[119] pkgconfig_2.0.3 plotly_4.9.4.1
[121] scuttle_1.2.0 spatstat.sparse_2.0-0
[123] vipor_0.4.5 bslib_0.2.5.1
[125] XVector_0.32.0 stringr_1.4.0
[127] digest_0.6.27 pracma_2.3.3
[129] sctransform_0.3.2 RcppAnnoy_0.0.18
[131] spatstat.data_2.1-0 rmarkdown_2.9
[133] leiden_0.3.8 uwot_0.1.10
[135] DelayedMatrixStats_1.14.0 shiny_1.6.0
[137] lifecycle_1.0.0 nlme_3.1-153
[139] jsonlite_1.7.2 BiocNeighbors_1.10.0
[141] viridisLite_0.4.0 limma_3.48.1
[143] fansi_0.5.0 pillar_1.6.1
[145] lattice_0.20-45 fastmap_1.1.0
[147] httr_1.4.2 survival_3.2-13
[149] glue_1.4.2 png_0.1-7
[151] bit_4.0.4 stringi_1.6.2
[153] sass_0.4.0 BiocSingular_1.8.1
[155] irlba_2.3.3 future.apply_1.7.0