NSC annotation
Katharina Hembach
7/2/2020
Last updated: 2021-08-27
Checks: 7 0
Knit directory: neural_scRNAseq/
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Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were made to the R Markdown (analysis/NSC-2-annotation.Rmd) and HTML (docs/NSC-2-annotation.html) files. If you've configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 4545d4d | khembach | 2021-08-25 | Build site. |
| Rmd | aac7415 | khembach | 2021-08-25 | fix ident |
| html | d710af5 | khembach | 2021-08-24 | Build site. |
| Rmd | ff22f3d | khembach | 2021-08-24 | add violin plot with NSC marker expression |
| html | 76cf2b5 | khembach | 2021-04-06 | Build site. |
| Rmd | 91b5b55 | khembach | 2021-04-06 | change heatmap color sclale and labels |
| html | 6b858c7 | khembach | 2020-08-07 | Build site. |
| Rmd | 0e960ce | khembach | 2020-08-07 | write files with top cluster markers; include more markers in heatmap |
| html | f7ee347 | khembach | 2020-08-05 | Build site. |
| Rmd | bbc4c2e | khembach | 2020-08-05 | use RNA assay for featurePlot |
| html | 0afd60f | khembach | 2020-07-02 | Build site. |
| Rmd | 6d0742b | khembach | 2020-07-02 | wflow_publish(c("analysis/NSC-1-clustering.Rmd", "analysis/NSC-2-annotation.Rmd", |
Load packages
library(ComplexHeatmap)
library(cowplot)
library(ggplot2)
library(dplyr)
library(muscat)
library(purrr)
library(RColorBrewer)
library(viridis)
library(scran)
library(Seurat)
library(SingleCellExperiment)
library(stringr)
library(RCurl)
library(BiocParallel)Load data & convert to SCE
so <- readRDS(file.path("output", "NSC_1_clustering.rds"))
sce <- as.SingleCellExperiment(so, assay = "RNA")
colData(sce) <- as.data.frame(colData(sce)) %>%
mutate_if(is.character, as.factor) %>%
DataFrame(row.names = colnames(sce))Number of clusters by resolution
cluster_cols <- grep("res.[0-9]", colnames(colData(sce)), value = TRUE)
sapply(colData(sce)[cluster_cols], nlevels)integrated_snn_res.0.1 integrated_snn_res.0.2 integrated_snn_res.0.4
4 5 7
integrated_snn_res.0.8 integrated_snn_res.1 integrated_snn_res.1.2
11 16 17
integrated_snn_res.2
24 Cluster-sample counts
# set cluster IDs to resolution 0.4 clustering
so <- SetIdent(so, value = "integrated_snn_res.0.4")
so@meta.data$cluster_id <- Idents(so)
sce$cluster_id <- Idents(so)
(n_cells <- table(sce$cluster_id, sce$sample_id))
1NSC 2NSC
0 2853 2973
1 1694 1731
2 1635 1594
3 1068 1053
4 721 704
5 333 332
6 27 21Relative cluster-abundances
fqs <- prop.table(n_cells, margin = 2)
mat <- round(as.matrix(unclass(fqs))*100, 2)
colfunc <- colorRampPalette(c("ghostwhite", "deepskyblue4"))
Heatmap(mat,
# col = rev(brewer.pal(11, "RdGy")[-6]),
col = colfunc(10),
name = "Percentage\nof cells",
cluster_rows = FALSE,
cluster_columns = FALSE,
row_names_side = "left",
row_title = "cluster ID",
column_title = "sample ID",
column_title_side = "bottom",
rect_gp = gpar(col = "white"),
cell_fun = function(i, j, x, y, width, height, fill)
grid.text(mat[j, i], x = x, y = y,
gp = gpar(col = "black", fontsize = 10)))
Cell cycle scoring with Seurat
We assign each cell a cell cycle scores and visualize them in the DR plots. We use known G2/M and S phase markers that come with the Seurat package. The markers are anticorrelated and cells that to not express the markers should be in G1 phase.
We compute cell cycle phase:
DefaultAssay(so) <- "RNA"
# A list of cell cycle markers, from Tirosh et al, 2015
cc_file <- getURL("https://raw.githubusercontent.com/hbc/tinyatlas/master/cell_cycle/Homo_sapiens.csv")
cc_genes <- read.csv(text = cc_file)
# match the marker genes to the features
m <- match(cc_genes$geneID[cc_genes$phase == "S"],
str_split(rownames(GetAssayData(so)),
pattern = "\\.", simplify = TRUE)[,1])
s_genes <- rownames(GetAssayData(so))[m]
(s_genes <- s_genes[!is.na(s_genes)]) [1] "ENSG00000012963.UBR7" "ENSG00000049541.RFC2"
[3] "ENSG00000051180.RAD51" "ENSG00000073111.MCM2"
[5] "ENSG00000075131.TIPIN" "ENSG00000076003.MCM6"
[7] "ENSG00000076248.UNG" "ENSG00000077514.POLD3"
[9] "ENSG00000092470.WDR76" "ENSG00000092853.CLSPN"
[11] "ENSG00000093009.CDC45" "ENSG00000094804.CDC6"
[13] "ENSG00000095002.MSH2" "ENSG00000100297.MCM5"
[15] "ENSG00000101868.POLA1" "ENSG00000104738.MCM4"
[17] "ENSG00000111247.RAD51AP1" "ENSG00000112312.GMNN"
[19] "ENSG00000117748.RPA2" "ENSG00000118412.CASP8AP2"
[21] "ENSG00000119969.HELLS" "ENSG00000131153.GINS2"
[23] "ENSG00000132646.PCNA" "ENSG00000132780.NASP"
[25] "ENSG00000136492.BRIP1" "ENSG00000136982.DSCC1"
[27] "ENSG00000143476.DTL" "ENSG00000144354.CDCA7"
[29] "ENSG00000151725.CENPU" "ENSG00000156802.ATAD2"
[31] "ENSG00000159259.CHAF1B" "ENSG00000162607.USP1"
[33] "ENSG00000163950.SLBP" "ENSG00000167325.RRM1"
[35] "ENSG00000168496.FEN1" "ENSG00000171848.RRM2"
[37] "ENSG00000174371.EXO1" "ENSG00000175305.CCNE2"
[39] "ENSG00000176890.TYMS" "ENSG00000197299.BLM"
[41] "ENSG00000198056.PRIM1" "ENSG00000276043.UHRF1" m <- match(cc_genes$geneID[cc_genes$phase == "G2/M"],
str_split(rownames(GetAssayData(so)),
pattern = "\\.", simplify = TRUE)[,1])
g2m_genes <- rownames(GetAssayData(so))[m]
(g2m_genes <- g2m_genes[!is.na(g2m_genes)]) [1] "ENSG00000010292.NCAPD2" "ENSG00000011426.ANLN"
[3] "ENSG00000013810.TACC3" "ENSG00000072571.HMMR"
[5] "ENSG00000075218.GTSE1" "ENSG00000080986.NDC80"
[7] "ENSG00000087586.AURKA" "ENSG00000088325.TPX2"
[9] "ENSG00000089685.BIRC5" "ENSG00000092140.G2E3"
[11] "ENSG00000094916.CBX5" "ENSG00000100401.RANGAP1"
[13] "ENSG00000102974.CTCF" "ENSG00000111665.CDCA3"
[15] "ENSG00000112742.TTK" "ENSG00000113810.SMC4"
[17] "ENSG00000114346.ECT2" "ENSG00000115163.CENPA"
[19] "ENSG00000117399.CDC20" "ENSG00000117650.NEK2"
[21] "ENSG00000117724.CENPF" "ENSG00000120802.TMPO"
[23] "ENSG00000123485.HJURP" "ENSG00000123975.CKS2"
[25] "ENSG00000126787.DLGAP5" "ENSG00000129195.PIMREG"
[27] "ENSG00000131747.TOP2A" "ENSG00000134222.PSRC1"
[29] "ENSG00000134690.CDCA8" "ENSG00000136108.CKAP2"
[31] "ENSG00000137804.NUSAP1" "ENSG00000137807.KIF23"
[33] "ENSG00000138160.KIF11" "ENSG00000138182.KIF20B"
[35] "ENSG00000138778.CENPE" "ENSG00000139354.GAS2L3"
[37] "ENSG00000142945.KIF2C" "ENSG00000143228.NUF2"
[39] "ENSG00000143401.ANP32E" "ENSG00000143815.LBR"
[41] "ENSG00000148773.MKI67" "ENSG00000157456.CCNB2"
[43] "ENSG00000158402.CDC25C" "ENSG00000164104.HMGB2"
[45] "ENSG00000169607.CKAP2L" "ENSG00000169679.BUB1"
[47] "ENSG00000170312.CDK1" "ENSG00000173207.CKS1B"
[49] "ENSG00000175063.UBE2C" "ENSG00000175216.CKAP5"
[51] "ENSG00000178999.AURKB" "ENSG00000184661.CDCA2"
[53] "ENSG00000188229.TUBB4B" "ENSG00000189159.JPT1" so <- CellCycleScoring(so, s.features = s_genes, g2m.features = g2m_genes,
set.ident = FALSE)
DefaultAssay(so) <- "integrated"DR colored by cluster ID
cs <- sample(colnames(so), 5e3)
.plot_dr <- function(so, dr, id)
DimPlot(so, cells = cs, group.by = id, reduction = dr, pt.size = 0.4) +
guides(col = guide_legend(nrow = 11,
override.aes = list(size = 3, alpha = 1))) +
theme_void() + theme(aspect.ratio = 1)
ids <- c("cluster_id", "sample_id", "Phase")
for (id in ids) {
cat("## ", id, "\n")
p1 <- .plot_dr(so, "tsne", id)
lgd <- get_legend(p1)
p1 <- p1 + theme(legend.position = "none")
p2 <- .plot_dr(so, "umap", id) + theme(legend.position = "none")
ps <- plot_grid(plotlist = list(p1, p2), nrow = 1)
p <- plot_grid(ps, lgd, nrow = 1, rel_widths = c(1, 0.2))
print(p)
cat("\n\n")
}cluster_id
sample_id
Find markers using scran
We identify candidate marker genes for each cluster that enable a separation of that group from all other groups.
scran_markers <- findMarkers(sce,
groups = sce$cluster_id, block = sce$sample_id,
direction = "up", lfc = 2, full.stats = TRUE)Heatmap of mean marker-exprs. by cluster
We aggregate the cells to pseudobulks and plot the average expression of the candidate marker genes in each of the clusters.
gs <- lapply(scran_markers, function(u) rownames(u)[u$Top == 1])
## candidate cluster markers
lapply(gs, function(x) str_split(x, pattern = "\\.", simplify = TRUE)[,2])$`0`
[1] "NOC2L" "IGFBP5" "SFRP2" "PTN"
$`1`
[1] "NOC2L" "SFRP2" "HIST1H4C" "FABP7" "PTN"
$`2`
[1] "CENPF" "PTN"
$`3`
[1] "S100A11" "TPM1"
$`4`
[1] "ANXA1" "VIM" "TAGLN" "HSP90AA1"
$`5`
[1] "SFRP2" "FABP7" "PTN" "EIF4EBP1" "SLC3A2"
$`6`
[1] "TAGLN3" "CRABP1"sub <- sce[unique(unlist(gs)), ]
pbs <- aggregateData(sub, assay = "logcounts", by = "cluster_id", fun = "mean")
mat <- t(muscat:::.scale(assay(pbs)))
## remove the Ensembl ID from the gene names
colnames(mat) <- str_split(colnames(mat), pattern = "\\.", simplify = TRUE)[,2]
Heatmap(mat,
name = "scaled avg.\nexpression",
col = viridis(10),
cluster_rows = FALSE,
cluster_columns = FALSE,
row_names_side = "left",
row_title = "cluster_id",
rect_gp = gpar(col = "white"))
We write tables with the top marker genes per cluster.
gs2 <- lapply(scran_markers, function(u) u[u$Top %in% 1:5,])
for (i in seq_along(gs2)) {
write.table(x = gs2[[i]] %>% as.data.frame %>%
dplyr::mutate(gene = rownames(gs2[[i]])) %>%
dplyr::relocate(gene),
file = file.path("output",
paste0("NSC_cluster", i, "_marker_genes.txt")),
sep = "\t", quote = FALSE, row.names = FALSE)
}Heatmap including marker genes of rank 2 and 3.
gs <- lapply(scran_markers, function(u) rownames(u)[u$Top %in% 1:5])
## candidate cluster markers
lapply(gs, function(x) str_split(x, pattern = "\\.", simplify = TRUE)[,2])$`0`
[1] "NOC2L" "IGFBP5" "SFRP2" "PTN" "HES4" "FABP7"
[7] "VIM" "CRABP1" "ISG15" "HSPA8" "AGRN" "PTX3"
[13] "C1orf159" "ENO1" "SOX4"
$`1`
[1] "NOC2L" "SFRP2" "HIST1H4C" "FABP7" "PTN" "HES4"
[7] "NASP" "VIM" "ISG15" "HMGN2" "TYMS" "AGRN"
[13] "HMGB1" "C1orf159" "ENO1" "DEK"
$`2`
[1] "CENPF" "PTN" "HMGB2" "TOP2A" "UBE2C" "NOC2L" "CKS1B" "HES4" "HMGN2"
[10] "CCNB1" "PTTG1" "UBE2S"
$`3`
[1] "S100A11" "TPM1" "VIM" "TAGLN" "ANXA2" "IFITM3" "S100A10"
[8] "CALD1" "PTN" "NQO1"
$`4`
[1] "ANXA1" "VIM" "TAGLN" "HSP90AA1" "IGFBP5" "PTN"
[7] "ENO1" "HSPE1" "VGF" "SQSTM1" "HMGA1" "CRABP1"
$`5`
[1] "SFRP2" "FABP7" "PTN" "EIF4EBP1" "SLC3A2" "VIM"
[7] "MGST1" "ASNS" "PSAT1" "ENO1" "DDIT4" "PHGDH"
[13] "MTHFD2" "IGFBP5"
$`6`
[1] "TAGLN3" "CRABP1" "STMN2" "CDKN1C" "CKB" "DLL3" "HES6" sub <- sce[unique(unlist(gs)), ]
pbs <- aggregateData(sub, assay = "logcounts", by = "cluster_id", fun = "mean")
mat <- t(muscat:::.scale(assay(pbs)))
## remove the Ensembl ID from the gene names
colnames(mat) <- str_split(colnames(mat), pattern = "\\.", simplify = TRUE)[,2]
Heatmap(mat,
name = "scaled avg.\nexpression",
col = viridis(10),
cluster_rows = FALSE,
cluster_columns = FALSE,
row_names_side = "left",
row_title = "cluster_id",
rect_gp = gpar(col = "white"))
Known marker genes
## source file with list of known marker genes
source(file.path("data", "known_NSC_markers.R"))
fs <- lapply(fs, sapply, function(g)
grep(pattern = paste0("\\.", g, "$"), rownames(sce), value = TRUE)
)
fs <- lapply(fs, function(x) unlist(x[lengths(x) !=0]) )
gs <- gsub(".*\\.", "", unlist(fs))
ns <- vapply(fs, length, numeric(1))
ks <- rep.int(names(fs), ns)
labs <- lapply(fs, function(x) gsub(".*\\.", "",x))Heatmap of mean marker-exprs. by cluster
# split cells by cluster
cs_by_k <- split(colnames(sce), sce$cluster_id)
# compute cluster-marker means
ms_by_cluster <- lapply(fs, function(gs) vapply(cs_by_k, function(i)
Matrix::rowMeans(logcounts(sce)[gs, i, drop = FALSE]),
numeric(length(gs))))
# prep. for plotting & scale b/w 0 and 1
mat <- do.call("rbind", ms_by_cluster)
mat <- muscat:::.scale(mat)
rownames(mat) <- gs
cols <- muscat:::.cluster_colors[seq_along(fs)]
cols <- setNames(cols, names(fs))
row_anno <- rowAnnotation(
df = data.frame(label = factor(ks, levels = names(fs))),
col = list(label = cols), gp = gpar(col = "white"))
# percentage of cells from each of the samples per cluster
sample_props <- prop.table(n_cells, margin = 1)
col_mat <- as.matrix(unclass(sample_props))
sample_cols <- c("#882255", "#CC6677")
sample_cols <- setNames(sample_cols, colnames(col_mat))
col_anno <- HeatmapAnnotation(
perc_sample = anno_barplot(col_mat, gp = gpar(fill = sample_cols),
height = unit(2, "cm"),
border = FALSE),
annotation_label = "fraction of sample\nin cluster",
gap = unit(10, "points"))
col_lgd <- Legend(labels = names(sample_cols),
title = "sample",
legend_gp = gpar(fill = sample_cols))
hm <- Heatmap(mat,
name = "scaled avg.\nexpression",
col = viridis(10),
cluster_rows = FALSE,
cluster_columns = FALSE,
row_names_side = "left",
column_title = "cluster_id",
column_title_side = "bottom",
column_names_side = "bottom",
column_names_rot = 0,
column_names_centered = TRUE,
rect_gp = gpar(col = "white"),
left_annotation = row_anno,
top_annotation = col_anno)
draw(hm, annotation_legend_list = list(col_lgd))
DR colored by marker expression
# downsample to 5000 cells
cs <- sample(colnames(sce), 5e3)
DefaultAssay(so) <- "RNA"
sub <- subset(so, cells = cs)
# UMAPs colored by marker-expression
for (m in seq_along(fs)) {
cat("## ", names(fs)[m], "\n")
ps <- lapply(seq_along(fs[[m]]), function(i) {
if (!fs[[m]][i] %in% rownames(so)) return(NULL)
FeaturePlot(sub, features = fs[[m]][i], reduction = "umap", pt.size = 0.4) +
theme(aspect.ratio = 1, legend.position = "none") +
ggtitle(labs[[m]][i]) + theme_void() + theme(aspect.ratio = 1)
})
# arrange plots in grid
ps <- ps[!vapply(ps, is.null, logical(1))]
p <- plot_grid(plotlist = ps, ncol = 4, label_size = 10)
print(p)
cat("\n\n")
}NSC
proliferating
neuronal
mature_astrocytes
glial_astrocytic
radial_glia
oligodendrocyte
Violin plot of selected NSC markers
p <- VlnPlot(sub, features = fs[["NSC"]][1:6], combine = FALSE, pt.size = 0.01,
same.y.lims = TRUE)
p <- lapply(seq_along(p), function(x) {
p[[x]] + xlab("cluster ID") + ggtitle(names(fs[["NSC"]])[x]) +
theme(legend.position = "none",
axis.text.x = element_text(angle = 0, vjust = 0.5, hjust=1))
})
plot_grid(plotlist = p, ncol = 3, label_size = 10)
DefaultAssay(so) <- "integrated"
sessionInfo()R version 4.0.5 (2021-03-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.5 LTS
Matrix products: default
BLAS: /usr/local/R/R-4.0.5/lib/libRblas.so
LAPACK: /usr/local/R/R-4.0.5/lib/libRlapack.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] parallel stats4 grid stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] BiocParallel_1.22.0 RCurl_1.98-1.3
[3] stringr_1.4.0 SeuratObject_4.0.1
[5] Seurat_4.0.1 scran_1.16.0
[7] SingleCellExperiment_1.10.1 SummarizedExperiment_1.18.1
[9] DelayedArray_0.14.0 matrixStats_0.56.0
[11] Biobase_2.48.0 GenomicRanges_1.40.0
[13] GenomeInfoDb_1.24.2 IRanges_2.22.2
[15] S4Vectors_0.26.1 BiocGenerics_0.34.0
[17] viridis_0.5.1 viridisLite_0.3.0
[19] RColorBrewer_1.1-2 purrr_0.3.4
[21] muscat_1.2.1 dplyr_1.0.2
[23] ggplot2_3.3.2 cowplot_1.0.0
[25] ComplexHeatmap_2.4.2 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] reticulate_1.16 tidyselect_1.1.0
[3] lme4_1.1-23 RSQLite_2.2.0
[5] AnnotationDbi_1.50.1 htmlwidgets_1.5.1
[7] Rtsne_0.15 munsell_0.5.0
[9] codetools_0.2-16 ica_1.0-2
[11] statmod_1.4.34 future_1.17.0
[13] miniUI_0.1.1.1 withr_2.4.1
[15] colorspace_1.4-1 knitr_1.29
[17] ROCR_1.0-11 tensor_1.5
[19] listenv_0.8.0 labeling_0.3
[21] git2r_0.27.1 GenomeInfoDbData_1.2.3
[23] polyclip_1.10-0 farver_2.0.3
[25] bit64_0.9-7 glmmTMB_1.0.2.1
[27] rprojroot_1.3-2 vctrs_0.3.4
[29] generics_0.0.2 xfun_0.15
[31] R6_2.4.1 doParallel_1.0.15
[33] ggbeeswarm_0.6.0 clue_0.3-57
[35] rsvd_1.0.3 locfit_1.5-9.4
[37] spatstat.utils_2.1-0 bitops_1.0-6
[39] cachem_1.0.4 promises_1.1.1
[41] scales_1.1.1 beeswarm_0.2.3
[43] gtable_0.3.0 globals_0.12.5
[45] goftest_1.2-2 rlang_0.4.10
[47] genefilter_1.70.0 GlobalOptions_0.1.2
[49] splines_4.0.5 TMB_1.7.16
[51] lazyeval_0.2.2 spatstat.geom_2.1-0
[53] abind_1.4-5 yaml_2.2.1
[55] reshape2_1.4.4 backports_1.1.9
[57] httpuv_1.5.4 tools_4.0.5
[59] spatstat.core_2.1-2 ellipsis_0.3.1
[61] gplots_3.0.4 ggridges_0.5.2
[63] Rcpp_1.0.5 plyr_1.8.6
[65] progress_1.2.2 zlibbioc_1.34.0
[67] prettyunits_1.1.1 rpart_4.1-15
[69] deldir_0.2-10 pbapply_1.4-2
[71] GetoptLong_1.0.1 zoo_1.8-8
[73] ggrepel_0.8.2 cluster_2.1.0
[75] colorRamps_2.3 fs_1.5.0
[77] variancePartition_1.18.2 magrittr_1.5
[79] data.table_1.12.8 scattermore_0.7
[81] lmerTest_3.1-2 circlize_0.4.10
[83] lmtest_0.9-37 RANN_2.6.1
[85] whisker_0.4 fitdistrplus_1.1-1
[87] hms_0.5.3 patchwork_1.0.1
[89] mime_0.9 evaluate_0.14
[91] xtable_1.8-4 pbkrtest_0.4-8.6
[93] XML_3.99-0.4 gridExtra_2.3
[95] shape_1.4.4 compiler_4.0.5
[97] scater_1.16.2 tibble_3.0.3
[99] KernSmooth_2.23-17 crayon_1.3.4
[101] minqa_1.2.4 htmltools_0.5.0
[103] mgcv_1.8-31 later_1.1.0.1
[105] tidyr_1.1.0 geneplotter_1.66.0
[107] DBI_1.1.0 MASS_7.3-51.6
[109] rappdirs_0.3.1 boot_1.3-25
[111] Matrix_1.3-3 gdata_2.18.0
[113] igraph_1.2.5 pkgconfig_2.0.3
[115] numDeriv_2016.8-1.1 spatstat.sparse_2.0-0
[117] plotly_4.9.2.1 foreach_1.5.0
[119] annotate_1.66.0 vipor_0.4.5
[121] dqrng_0.2.1 blme_1.0-4
[123] XVector_0.28.0 digest_0.6.25
[125] sctransform_0.3.2 RcppAnnoy_0.0.18
[127] spatstat.data_2.1-0 rmarkdown_2.3
[129] leiden_0.3.3 uwot_0.1.10
[131] edgeR_3.30.3 DelayedMatrixStats_1.10.1
[133] shiny_1.5.0 gtools_3.8.2
[135] rjson_0.2.20 nloptr_1.2.2.2
[137] lifecycle_1.0.0 nlme_3.1-148
[139] jsonlite_1.7.2 BiocNeighbors_1.6.0
[141] limma_3.44.3 pillar_1.4.6
[143] lattice_0.20-41 fastmap_1.0.1
[145] httr_1.4.2 survival_3.2-3
[147] glue_1.4.2 png_0.1-7
[149] iterators_1.0.12 bit_1.1-15.2
[151] stringi_1.4.6 blob_1.2.1
[153] DESeq2_1.28.1 BiocSingular_1.4.0
[155] caTools_1.18.0 memoise_2.0.0
[157] irlba_2.3.3 future.apply_1.6.0