AD_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
D2 <- Read10X(here("data","AD"))
Sat_D2 <- CreateSeuratObject(D2, min.cells = 3)
Sat_D2 <- PercentageFeatureSet(Sat_D2, pattern = "^MT-", col.name = "percent.mt")
Sat_D2 <- PercentageFeatureSet(Sat_D2, pattern = "^RP[SL][[:digit:]]|^RPLP[[:digit:]]|^RPSA", col.name = "percent.ribo")
Idents(Sat_D2) <- rep("AD", length(Sat_D2$orig.ident))
VlnPlot(Sat_D2, c("nFeature_RNA", "nCount_RNA", "percent.mt", "percent.ribo"), ncol = 2, cols = "#4EA2A2") &
theme_tufte() &
theme(legend.position="none")
summary(Sat_D2$nFeature_RNA) Min. 1st Qu. Median Mean 3rd Qu. Max.
52 1139 2973 2923 4228 11301 summary(Sat_D2$nCount_RNA) Min. 1st Qu. Median Mean 3rd Qu. Max.
500 2218 7705 10058 14137 135137 summary(Sat_D2$percent.mt) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 2.402 4.120 13.614 8.739 91.966 summary(Sat_D2$percent.ribo) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.4205 7.7544 13.2051 14.9465 21.2908 71.0363 plot1 <- FeatureScatter(Sat_D2, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(Sat_D2, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
Sat_D2 <- subset(Sat_D2, subset = nFeature_RNA > 1000 & nFeature_RNA < 7000 & percent.mt < 10)Normalization
Sat_D2 <- SCTransform(Sat_D2, verbose = FALSE)Cell-cycle scoring and dimension reduction
Sat_D2 <- CellCycleScoring(Sat_D2, s.features = cc.genes.updated.2019$s.genes,
g2m.features = cc.genes.updated.2019$g2m.genes, set.ident = TRUE)
Sat_D2 <- RunPCA(Sat_D2, features = VariableFeatures(Sat_D2))
Sat_D2 <- RunUMAP(Sat_D2, dims = 1:15)
DimPlot(Sat_D2, group.by = "Phase") +
theme_tufte()
Clustering
set.seed(42)
Sat_D2 <- FindNeighbors(Sat_D2, dims=1:15, verbose = FALSE)
Sat_D2 <- FindClusters(Sat_D2, resolution = 0.8, verbose = FALSE)
autumn_palette <- c("#751A33", "#B34233", "#D28F33", "#D4B95E", "#4EA2A2", "#506432",
"#1A8693", "#cbdfbd", "#d4e09b", "#f6f4d2", "#f19c79", "#a44a3f")
DimPlot(Sat_D2, label = T, group.by = "SCT_snn_res.0.8", cols = c(autumn_palette, "grey")) +
theme_tufte()
Cluster-level quality control
VlnPlot(Sat_D2, c("nFeature_RNA", "nCount_RNA", "percent.mt", "percent.ribo"), ncol = 2, cols = c(autumn_palette, "grey")) &
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_D2, ident.1 = "5", logfc.threshold = 0.5, features = VariableFeatures(Sat_D2), verbose = FALSE) %>%
head(n=20) p_val avg_log2FC pct.1 pct.2 p_val_adj
MT-ND3 6.926450e-65 -1.3897269 0.889 0.992 1.522711e-60
MT-CO3 4.307718e-63 -1.3833762 0.898 0.994 9.470087e-59
SLC3A2 7.141368e-62 1.6383437 0.906 0.601 1.569958e-57
MT-CO2 7.684752e-61 -1.3803355 0.910 0.993 1.689416e-56
DDIT3 5.986437e-57 1.7169770 0.697 0.312 1.316058e-52
MT-ATP6 1.223633e-55 -1.3268214 0.922 0.995 2.690035e-51
TMSB15A 3.159841e-54 -1.1052841 0.889 0.961 6.946595e-50
NREP 6.009393e-54 -1.2241557 0.648 0.893 1.321105e-49
MT-CYB 7.146861e-51 -1.1300007 0.910 0.993 1.571166e-46
GADD45B 4.048980e-47 1.1478647 0.307 0.058 8.901277e-43
GDF15 2.247286e-46 0.7795066 0.242 0.034 4.940434e-42
TUBA1A 8.884144e-46 -1.4418068 0.971 0.994 1.953090e-41
SNHG12 1.239532e-45 1.0919364 0.541 0.196 2.724988e-41
STMN1 1.028665e-43 -1.0874911 0.963 0.992 2.261417e-39
MT-CO1 2.173821e-43 -0.9521556 0.922 0.995 4.778929e-39
ZFAS1 4.345152e-42 1.3269365 0.980 0.941 9.552381e-38
ATF3 1.132432e-41 1.0917357 0.439 0.135 2.489538e-37
HRK 1.878270e-39 0.7561372 0.295 0.062 4.129190e-35
FTL 3.792618e-39 1.4372162 0.996 1.000 8.337692e-35
TUBB3 2.284768e-38 -1.6938210 0.902 0.934 5.022835e-34Among 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_D2_sub <- subset(Sat_D2, SCT_snn_res.0.8 == 5, invert=T)
Sat_D2_sub <- SCTransform(Sat_D2_sub, assay = 'RNA', new.assay.name = 'SCT', verbose = FALSE)
Sat_D2_sub <- RunPCA(Sat_D2_sub, features = VariableFeatures(Sat_D2_sub))
Sat_D2_sub <- RunUMAP(Sat_D2_sub, dims = 1:15)
DimPlot(Sat_D2_sub, label = T, group.by = "Phase") +
theme_tufte()
set.seed(42)
Sat_D2_sub <- FindNeighbors(Sat_D2_sub, dims=1:15, verbose = FALSE)
Sat_D2_sub <- FindClusters(Sat_D2_sub, resolution = 0.8, verbose = FALSE)
DimPlot(Sat_D2_sub, label = T, group.by = "SCT_snn_res.0.8", cols = autumn_palette) +
theme_tufte()
VlnPlot(Sat_D2_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(sub("-1","-2",colnames(Sat_D2_sub)), here("output", "AD_filtered_barcodes.csv"),
row.names = FALSE)
# save clusters
write.csv(Sat_D2_sub$SCT_snn_res.0.8, here("output", "AD_clusters_res08.csv"),
row.names = TRUE)Regress out the difference between S and G2M score.
Sat_D2_sub$cc_difference <- Sat_D2_sub$S.Score - Sat_D2_sub$G2M.Score
Sat_D2_sub <- SCTransform(Sat_D2_sub, assay = 'RNA', new.assay.name = 'SCT', vars.to.regress = "cc_difference", verbose = FALSE)
Sat_D2_sub <- RunPCA(Sat_D2_sub, features = VariableFeatures(Sat_D2_sub))
Sat_D2_sub <- RunUMAP(Sat_D2_sub, dims = 1:15)
DimPlot(Sat_D2_sub, label = T, group.by = "Phase") +
theme_tufte()
set.seed(42)
Sat_D2_sub <- FindNeighbors(Sat_D2_sub, dims=1:15, verbose = FALSE)
Sat_D2_sub <- FindClusters(Sat_D2_sub, resolution = 0.8, verbose = FALSE)
DimPlot(Sat_D2_sub, label = F, group.by = "SCT_snn_res.0.8", cols = autumn_palette) +
theme_tufte()
VlnPlot(Sat_D2_sub, c("nFeature_RNA", "nCount_RNA", "percent.mt", "percent.ribo"), cols = autumn_palette, ncol = 2) &
theme_tufte() &
theme(legend.position="none")
# save Sat_D2_sub for SingleR classification
#saveRDS(Sat_D2_sub, here("output", "Sat_D2_sub.rds"))Identification of clusters specific genes using tf-idf (term frequency–inverse document frequency)
AD_markers <- SoupX::quickMarkers(Sat_D2_sub@assays$RNA@counts, Sat_D2_sub$SCT_snn_res.0.8, N = 6)
SCE_D2_sub <- as.SingleCellExperiment(Sat_D2_sub)
scater::plotExpression(SCE_D2_sub, features = unique(AD_markers$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_D2_sub, features = unique(AD_markers$gene)[11:20], x = "SCT_snn_res.0.8", colour_by = "SCT_snn_res.0.8") +
scale_color_manual(values = autumn_palette)
scater::plotExpression(SCE_D2_sub, features = unique(AD_markers$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_D2_sub, features = unique(AD_markers$gene)[31:33], x = "SCT_snn_res.0.8", colour_by = "SCT_snn_res.0.8") +
scale_color_manual(values = autumn_palette)
Plot genes selected from the literature
genes_to_plot <- c("LY6H","MKI67", "SOX2", "SOX1", "PAX6", "NES", "DCX", "TUBB3", "MAP2",
"MAPT", "OLIG2", "PLP1", "S100B", "TMEM119", "RAX", "SIX3",
"GAD2","ASCL1", "NEUROD1", "NEUROD4", "NEUROG1", "EOMES", "APOE")
# not expressed in this dataset:"PDGFR1", "NEUROG4"
for (i in genes_to_plot){
g <- Nebulosa::plot_density(Sat_D2_sub, features = i) +
theme_tufte() +
theme(legend.position="none", axis.ticks = element_blank()) #legend.position="right"
print(g)
}
genes_to_plot <- c("HES1", "HES4", "HES5", "NOTCH1")
for (i in genes_to_plot){
g <- Nebulosa::plot_density(Sat_D2_sub, features = i) +
theme_tufte() +
theme(legend.position="none", axis.ticks = element_blank())
print(g)
}
Subset non-proliferating NSC, neurons, radial glia and choroid plexus for CellRank analysis
Sat_D2_sub_neuro <- subset(Sat_D2_sub, SCT_snn_res.0.8 %in% c(0,8,9,10), invert = TRUE)
#
tmp_umap <- Sat_D2_sub_neuro@reductions$umap@cell.embeddings
tmp_umap <- tmp_umap[tmp_umap[,1] > -10,]
tmp_umap <- tmp_umap[tmp_umap[,2] < 7,]
Sat_D2_sub_neuro <- subset(Sat_D2_sub_neuro, cells = rownames(tmp_umap))
set.seed(42)
Sat_D2_sub_neuro <- FindNeighbors(Sat_D2_sub_neuro, dims=1:15, verbose = FALSE)
Sat_D2_sub_neuro <- FindClusters(Sat_D2_sub_neuro, resolution = 0.8, verbose = FALSE)
DimPlot(Sat_D2_sub_neuro, label = F, group.by = "SCT_snn_res.0.8", cols = autumn_palette) +
theme_tufte()
VlnPlot(Sat_D2_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(sub("-1","-2",colnames(Sat_D2_sub_neuro)), here("output", "AD_sub_neuro_barcodes.csv"),
row.names = FALSE)
# save clusters
write.csv(Sat_D2_sub_neuro$SCT_snn_res.0.8, here("output", "AD_sub_neuro_res08.csv"),
row.names = TRUE)
# convert to h5ad for CellRank
# Sat_D2_sub_neuro <- RenameCells(Sat_D2_sub_neuro, new.names = paste("D2_", sub("-1", "", colnames(Sat_D2_sub_neuro)), sep=""))
# SaveH5Seurat(Sat_D2_sub_neuro, filename = here("output","Sat_D2_sub.h5Seurat"))
# Convert(here("output","Sat_D2_sub.h5Seurat"), dest="h5ad")Identification of clusters specific genes using tf-idf (term frequency–inverse document frequency)
AD_sub_markers <- SoupX::quickMarkers(Sat_D2_sub_neuro@assays$RNA@counts, Sat_D2_sub_neuro$SCT_snn_res.0.8, N = 6)
SCE_D2_sub_neuro <- as.SingleCellExperiment(Sat_D2_sub_neuro)
scater::plotExpression(SCE_D2_sub_neuro, features = unique(AD_sub_markers$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_D2_sub_neuro, features = unique(AD_sub_markers$gene)[11:22], 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