Cluster analysis of group integrated cells
Katharina Hembach
8/26/2020
Last updated: 2020-09-07
Checks: 7 0
Knit directory: neural_scRNAseq/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | f4c394f | khembach | 2020-09-07 | fix tabset |
| html | 48fb578 | khembach | 2020-09-07 | Build site. |
| Rmd | b6abb6f | khembach | 2020-09-07 | add marker genes, heatmap, comparison with clusters before organoid |
| html | 1e1dcab | khembach | 2020-09-03 | Build site. |
| Rmd | e72cff9 | khembach | 2020-09-03 | add sample abundance plot |
| html | b93b07d | khembach | 2020-09-02 | Build site. |
| Rmd | 043115f | khembach | 2020-09-02 | group organoid integration cluster abundances |
Load packages
library(ComplexHeatmap)
library(cowplot)
library(ggplot2)
library(dplyr)
library(muscat)
library(RColorBrewer)
library(Seurat)
library(SingleCellExperiment)
library(scran)
library(stringr)
library(viridis)Load data & convert to SCE
so <- readRDS(file.path("output", "so_04-group_integration.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))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 3NC52 4NC52 5NC96 6NC96 H9 409b2
0 17 16 5165 4290 1352 2047 2722 2391
1 4357 4307 281 193 421 49 64 78
2 11 12 1307 1316 802 1321 1787 2672
3 3111 3232 30 17 7 2 414 395
4 0 0 393 337 90 96 2483 3335
5 1 0 7 31 6 7 3656 1244
6 35 22 780 522 360 565 475 1016
7 0 0 0 0 0 0 2017 1708
8 0 0 1 0 0 0 2409 817
9 4 1 9 9 12 4 1866 1174
10 3 3 28 12 7 8 1619 1351
11 5 7 39 40 3 6 1472 1382
12 539 553 270 232 271 247 215 358
13 0 2 0 0 0 1 811 1149
14 1 3 191 162 51 101 666 754
15 244 245 186 277 156 141 127 170
16 3 5 0 0 0 0 423 278Relative cluster-abundances
fqs <- prop.table(n_cells, margin = 2)
mat <- as.matrix(unclass(fqs))
Heatmap(mat,
col = rev(brewer.pal(11, "RdGy")[-6]),
name = "Frequency",
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(round(mat[j, i] * 100, 2), x = x, y = y,
gp = gpar(col = "white", fontsize = 10)))
| Version | Author | Date |
|---|---|---|
| b93b07d | khembach | 2020-09-02 |
n_cells <- table(sce$sample_id, sce$cluster_id)
fqs <- prop.table(n_cells, margin = 2)
mat <- as.matrix(unclass(fqs))
Heatmap(mat,
col = rev(brewer.pal(11, "RdGy")[-6]),
name = "Frequency",
cluster_rows = FALSE,
cluster_columns = FALSE,
row_names_side = "left",
row_title = "sample_id",
column_title = "cluster_id",
column_title_side = "bottom",
rect_gp = gpar(col = "white"),
cell_fun = function(i, j, x, y, width, height, fill)
grid.text(round(mat[j, i] * 100, 2), x = x, y = y,
gp = gpar(col = "white", fontsize = 10)))
(n_cells_group <- table(sce$cluster_id, sce$group_id))
P22 D52 D96 iPSCs EB Neuroectoderm Neuroepithelium Organoid-1M
0 33 9455 3399 0 0 0 0 266
1 8664 474 470 0 0 0 1 18
2 23 2623 2123 0 0 0 0 5
3 6343 47 9 8 16 4 29 297
4 0 730 186 0 0 0 0 0
5 1 38 13 0 0 0 2 67
6 57 1302 925 0 0 1 0 753
7 0 0 0 3656 16 53 0 0
8 0 1 0 16 3203 7 0 0
9 5 18 16 19 6 2635 375 5
10 6 40 15 0 0 1 7 2350
11 12 79 9 0 0 0 0 49
12 1092 502 518 0 0 2 7 161
13 2 0 1 11 15 9 913 1012
14 4 353 152 0 0 0 0 9
15 489 463 297 0 0 4 4 78
16 8 0 0 590 44 41 25 0
Organoid-2M Organoid-4M
0 3878 969
1 102 21
2 3139 1315
3 398 57
4 3553 2265
5 1476 3355
6 735 2
7 0 0
8 0 0
9 0 0
10 607 5
11 1955 850
12 241 162
13 0 0
14 983 428
15 152 59
16 1 0fqs <- prop.table(n_cells_group, margin = 2)
mat <- as.matrix(unclass(fqs))
Heatmap(mat,
col = rev(brewer.pal(11, "RdGy")[-6]),
name = "Frequency",
cluster_rows = FALSE,
cluster_columns = FALSE,
row_names_side = "left",
row_title = "cluster_id",
column_title = "group_id",
column_title_side = "bottom",
rect_gp = gpar(col = "white"),
cell_fun = function(i, j, x, y, width, height, fill)
grid.text(round(mat[j, i] * 100, 2), x = x, y = y,
gp = gpar(col = "white", fontsize = 10)))
n_cells_lineage <- table(sce$cluster_id, sce$cl_FullLineage)
fqs <- prop.table(n_cells_lineage, margin = 2)
mat <- as.matrix(unclass(fqs))
cn <- colnames(mat)
Heatmap(mat,
col = rev(brewer.pal(11, "RdGy")[-6]),
name = "Frequency",
cluster_rows = FALSE,
cluster_columns = FALSE,
show_column_names = FALSE,
row_names_side = "left",
row_title = "cluster_id",
column_title = "cl_FullLineage",
column_title_side = "bottom",
rect_gp = gpar(col = "white"),
cell_fun = function(i, j, x, y, width, height, fill)
grid.text(round(mat[j, i] * 100, 1), x = x, y = y,
gp = gpar(col = "white", fontsize = 10)),
bottom_annotation = HeatmapAnnotation(
text = anno_text(cn, rot = 80, just = "right")))
| Version | Author | Date |
|---|---|---|
| 1e1dcab | khembach | 2020-09-03 |
n_cells_lineage <- table(sce$cl_FullLineage, sce$cluster_id)
fqs <- prop.table(n_cells_lineage, margin = 2)
mat <- as.matrix(unclass(fqs))
Heatmap(mat,
col = rev(brewer.pal(11, "RdGy")[-6]),
name = "Frequency",
cluster_rows = FALSE,
cluster_columns = FALSE,
row_names_side = "left",
row_title = "cl_FullLineage",
row_names_rot = 10,
column_title = "cluster_id",
column_title_side = "bottom",
rect_gp = gpar(col = "white"),
cell_fun = function(i, j, x, y, width, height, fill)
grid.text(round(mat[j, i] * 100, 1), x = x, y = y,
gp = gpar(col = "white", fontsize = 10)))
| Version | Author | Date |
|---|---|---|
| 1e1dcab | khembach | 2020-09-03 |
Evaluation of cluster before and after integration
We evaluate if cells which were together in a cluster before the integration of the organoid cells are still in the same cluster after integration.
## Load the Seurat object from our NSC analysis
so_before <- readRDS(file.path("output", "so_04_clustering.rds"))
so_before <- SetIdent(so_before, value = "integrated_snn_res.0.4")
so_before@meta.data$cluster_id <- Idents(so_before)
table(so_before@meta.data$cluster_id)
0 1 2 3 4 5 6 7 8 9 10 11 12
11194 3938 2856 2763 2608 2481 2467 2332 1948 1870 1340 1328 1176
13 14 15 16
976 915 488 317 ## subset to our cells
cs <- which(so@meta.data$integration_group %in% c("P22", "D52", "D96"))
sub <- subset(so, cells = cs)
table(sub@meta.data$cluster_id)
0 1 2 3 4 5 6 7 8 9 10 11 12
12887 9608 4769 6399 916 52 2284 0 1 39 61 100 2112
13 14 15 16
3 509 1249 8 ## join the cluster_ids from both clustering runs
before <- data.frame(cell = colnames(so_before),
cluster_before = so_before@meta.data[,c("cluster_id")])
after <- data.frame(cell = colnames(sub),
cluster_after = sub@meta.data[,c("cluster_id")])
clusters <- before %>% full_join(after)
## check if cells from the same cluster are still in the same cluster
(n_clusters <- table(clusters$cluster_after, clusters$cluster_before))
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
0 31 3854 0 1 0 1560 2378 2296 0 1624 1 128 125 840 48
1 7371 32 13 514 70 7 17 5 530 46 761 110 44 26 10
2 367 0 4 313 2518 0 0 0 1262 0 256 8 0 0 0
3 3126 0 2727 284 3 0 0 0 14 0 236 3 2 0 0
4 0 4 0 0 0 836 57 10 0 1 0 8 0 0 0
5 1 7 0 1 0 6 3 10 0 2 0 18 1 2 0
6 34 33 0 0 0 34 0 0 0 189 2 7 1002 104 850
7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
8 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
9 2 0 2 7 0 0 0 0 0 0 26 2 0 0 0
10 11 0 3 10 2 0 0 0 17 0 13 3 0 0 0
11 1 0 96 3 0 0 0 0 0 0 0 0 0 0 0
12 176 0 9 1531 2 0 0 0 66 0 5 6 0 0 0
13 0 0 2 1 0 0 0 0 0 0 0 0 0 0 0
14 17 0 0 79 6 29 0 0 18 0 0 1 0 0 2
15 51 8 0 17 6 9 12 11 41 8 40 1034 2 4 5
16 6 0 0 1 1 0 0 0 0 0 0 0 0 0 0
15 16
0 1 0
1 52 0
2 41 0
3 4 0
4 0 0
5 1 0
6 29 0
7 0 0
8 0 0
9 0 0
10 2 0
11 0 0
12 0 317
13 0 0
14 357 0
15 1 0
16 0 0fqs <- prop.table(n_clusters, margin = 2)
mat <- as.matrix(unclass(fqs))
Heatmap(mat,
col = rev(brewer.pal(11, "RdGy")[-6]),
name = "Frequency",
cluster_rows = FALSE,
cluster_columns = FALSE,
row_names_side = "left",
row_title = "clusters after organoid integration",
column_title = "clusters before organoid integration",
column_title_side = "bottom",
rect_gp = gpar(col = "white"),
cell_fun = function(i, j, x, y, width, height, fill)
grid.text(round(mat[j, i] * 100, 2), x = x, y = y,
gp = gpar(col = "white", fontsize = 8)))
| Version | Author | Date |
|---|---|---|
| 48fb578 | khembach | 2020-09-07 |
## add the old cluster identities to the Seurat object
so@meta.data$cluster_id_before <- so_before@meta.data$cluster_id[
match(colnames(so), colnames(so_before))]Dimension reduction plots
## merge the lineage labels of identical cell types
so$cl_FullLineage_merged <- as.factor(so$cl_FullLineage)
levels(so$cl_FullLineage_merged) <- c("choroid plexus/mesenchymal-like cells",
"cortical neurons", "cortical neurons",
"cycling dorsal progenitors", "cycling ventral progenitors",
"ectodermal/neuroectodermal-like cells",
"gliogenic/outer RGCs and astrocytes",
"IPs and early cortical neurons", "midbrain/hindbrain cells",
"neuroepithelial-like cells", "retina progenitors", "RGCs",
"RGCs early", "RGCs early", "stem cells", "stem cells",
"stem cells", "ventral progenitors and neurons",
"ventral progenitors and neurons",
"ventral progenitors and neurons")
cs <- sample(colnames(so), 10e3)
.plot_dr <- function(so, dr, id)
DimPlot(so, cells = cs, group.by = id, reduction = dr, pt.size = 0.4) +
guides(col = guide_legend(nrow = 11,
override.aes = list(size = 3, alpha = 1))) +
theme_void() + theme(aspect.ratio = 1)
ids <- c("integration_group", "group_id", "cl_FullLineage_merged", "cluster_id",
"cluster_id_before")
for (id in ids) {
cat("## ", id, "\n")
p1 <- .plot_dr(so, "tsne", id)
lgd <- get_legend(p1)
p1 <- p1 + theme(legend.position = "none")
p2 <- .plot_dr(so, "umap", id) + theme(legend.position = "none")
ps <- plot_grid(plotlist = list(p1, p2), nrow = 1)
p <- plot_grid(ps, lgd, nrow = 1, rel_widths = c(1, 0.5))
print(p)
cat("\n\n")
}
Find markers using scran
We identify candidate marker genes for each cluster.
scran_markers <- findMarkers(sce,
groups = sce$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] "SAMD11" "STMN2" "TUBA1A" "RTN1" "MT-CO3" "MALAT1"
$`1`
[1] "SAMD11" "SOX4" "FABP7" "VGF" "PTN" "CLU" "VIM" "TUBA1A"
[9] "MALAT1"
$`2`
[1] "C1orf61" "FABP7" "PTN" "VIM" "TTYH1"
$`3`
[1] "SAMD11" "CDC20" "HMGB2" "PTTG1" "VIM" "TUBA1A" "MT-CO1"
$`4`
[1] "SOX4" "NEUROD6" "STMN2" "TUBA1A"
$`5`
[1] "C1orf61" "STMN2" "TUBA1A" "DCX" "DLX6-AS1" "MALAT1"
$`6`
[1] "SAMD11" "STMN2" "TUBA1A" "CRABP1" "MT-CO2" "MALAT1"
$`7`
[1] "SAMD11" "MT-CO1" "RP11-148B6" "POU5F1"
$`8`
[1] "SAMD11" "MT-ND1" "POU5F1"
$`9`
[1] "HMGA1" "MDK" "DLK1" "APOE" "MT-CO1"
$`10`
[1] "SAMD11" "VIM" "MDK" "CKB" "TTYH1" "MALAT1"
$`11`
[1] "C1orf61" "HMGB2" "HIST1H4C" "TUBA1A" "MALAT1"
$`12`
[1] "S100A11" "SPARC" "VIM" "IFITM3" "ANXA2" "MALAT1" "TTR"
$`13`
[1] "SFRP2" "MDK" "CRABP1" "MALAT1"
$`14`
[1] "C1orf61" "HES6" "SOX4" "TUBA1A" "MALAT1"
$`15`
[1] "C1orf61" "NEFL" "STMN2" "VIM" "TUBA1A" "HSP90AA1" "FTL"
[8] "MALAT1"
$`16`
[1] "MT2A" "UBE2C" "RP11-148B6" "POU5F1" sub <- sce[unique(unlist(gs)), ]
pbs <- aggregateData(sub, assay = "logcounts", by = "cluster_id", fun = "mean")
mat <- t(muscat:::.scale(assay(pbs)))
## remove the Ensembl ID from the gene names
colnames(mat) <- str_split(colnames(mat), pattern = "\\.", simplify = TRUE)[,2]
Heatmap(mat,
name = "scaled avg.\nexpression",
col = viridis(10),
cluster_rows = FALSE,
cluster_columns = FALSE,
row_names_side = "left",
row_title = "cluster_id",
rect_gp = gpar(col = "white"))
| Version | Author | Date |
|---|---|---|
| 48fb578 | khembach | 2020-09-07 |
We write tables with the top marker genes per cluster.
gs2 <- lapply(scran_markers, function(u) u[u$Top %in% 1:3,])
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("organoid_integration_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$Top %in% 1:3])
## candidate cluster markers
lapply(gs, function(x) str_split(x, pattern = "\\.", simplify = TRUE)[,2])$`0`
[1] "SAMD11" "STMN2" "TUBA1A" "RTN1" "MT-CO3" "MALAT1" "NOC2L" "TUBB2A"
[9] "SOX4" "DCX" "MT-CO2" "KLHL17" "CRABP1" "MT-ND4"
$`1`
[1] "SAMD11" "SOX4" "FABP7" "VGF" "PTN" "CLU" "VIM" "TUBA1A"
[9] "MALAT1" "NOC2L" "TUBB2A" "TUBB2B" "NEFL" "STMN2" "CKB" "KLHL17"
[17] "IGFBP2" "BASP1" "PTPRZ1" "LY6H" "METRN" "TTYH1" "MT-CO1"
$`2`
[1] "C1orf61" "FABP7" "PTN" "VIM" "TTYH1" "SOX2" "CKB"
[8] "SAMD11" "IGFBP2" "ATP1B2" "MALAT1"
$`3`
[1] "SAMD11" "CDC20" "HMGB2" "PTTG1" "VIM" "TUBA1A" "MT-CO1"
[8] "NOC2L" "HMGN2" "CENPF" "CKB" "UBE2S" "MALAT1" "KLHL17"
[15] "FABP7" "PTN" "ARL6IP1" "UBE2C"
$`4`
[1] "SOX4" "NEUROD6" "STMN2" "TUBA1A" "NFIB" "RTN1" "NEUROD2"
[8] "MALAT1" "SAMD11" "NSG1" "GPM6A" "DCX" "MT-CO3"
$`5`
[1] "C1orf61" "STMN2" "TUBA1A" "DCX" "DLX6-AS1" "MALAT1"
[7] "SOX4" "TCF4" "DLX5" "SAMD11" "DLX2"
$`6`
[1] "SAMD11" "STMN2" "TUBA1A" "CRABP1" "MT-CO2" "MALAT1" "NOC2L" "STMN1"
[9] "MLLT11" "SCG2" "HES6" "BASP1" "TUBB2B" "CD24" "RTN1" "MT-CO3"
[17] "KLHL17" "GAP43" "VIM" "SEZ6L2"
$`7`
[1] "SAMD11" "MT-CO1" "RP11-148B6" "POU5F1" "NOC2L"
[6] "MT-ND4" "KLHL17" "HMGA1" "TERF1" "MT-CO2"
$`8`
[1] "SAMD11" "MT-ND1" "POU5F1" "NOC2L" "HMGA1"
[6] "PMAIP1" "APOE" "MT-ND2" "KLHL17" "CD24"
[11] "TERF1" "LDHA" "MT-CO3" "RP11-148B6"
$`9`
[1] "HMGA1" "MDK" "DLK1" "APOE" "MT-CO1"
[6] "CD24" "GPC3" "MT-CO3" "RP11-148B6" "SAMD11"
[11] "MALAT1"
$`10`
[1] "SAMD11" "VIM" "MDK" "CKB" "TTYH1" "MALAT1" "NOC2L" "TUBB2B"
[9] "TUBA1A" "KLHL17" "HES4" "SOX2" "TUBA1B" "MT-CO1"
$`11`
[1] "C1orf61" "HMGB2" "HIST1H4C" "TUBA1A" "MALAT1" "FABP7"
[7] "VIM" "CKB" "TOP2A" "CENPF" "CDK1" "TUBA1B"
[13] "MT-CO1"
$`12`
[1] "S100A11" "SPARC" "VIM" "IFITM3" "ANXA2" "MALAT1" "TTR"
[8] "IGFBP5" "PTN" "CLU" "TAGLN" "LGALS1" "MT-CO1" "ID3"
[15] "IGFBP2" "TUBA1A" "TPM1" "MT-CYB"
$`13`
[1] "SFRP2" "MDK" "CRABP1" "MALAT1" "SAMD11" "VIM" "TUBA1A" "MT-CO1"
[9] "NOC2L" "DLK1" "MT-CO3"
$`14`
[1] "C1orf61" "HES6" "SOX4" "TUBA1A" "MALAT1" "HES5" "NFIA"
[8] "SOX11" "VIM" "NHLH1" "IGFBP5" "CKB" "TMSB4X" "MT-CO1"
$`15`
[1] "C1orf61" "NEFL" "STMN2" "VIM" "TUBA1A" "HSP90AA1"
[7] "FTL" "MALAT1" "SOX11" "IGFBP2" "SQSTM1" "VGF"
[13] "DDIT3" "MT-CO1" "SCG2" "TUBB2B" "STMN4" "RPL12"
[19] "FTH1" "SLC3A2" "HSPH1" "RPS27L" "MAP1LC3B"
$`16`
[1] "MT2A" "UBE2C" "RP11-148B6" "POU5F1" "HMGA1"
[6] "MT1G" "UGP2" "CD24" 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"))
| Version | Author | Date |
|---|---|---|
| 48fb578 | khembach | 2020-09-07 |
sessionInfo()R version 4.0.0 (2020-04-24)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.6 LTS
Matrix products: default
BLAS: /usr/local/R/R-4.0.0/lib/libRblas.so
LAPACK: /usr/local/R/R-4.0.0/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] viridis_0.5.1 viridisLite_0.3.0
[3] stringr_1.4.0 scran_1.16.0
[5] SingleCellExperiment_1.10.1 SummarizedExperiment_1.18.1
[7] DelayedArray_0.14.0 matrixStats_0.56.0
[9] Biobase_2.48.0 GenomicRanges_1.40.0
[11] GenomeInfoDb_1.24.2 IRanges_2.22.2
[13] S4Vectors_0.26.1 BiocGenerics_0.34.0
[15] Seurat_3.1.5 RColorBrewer_1.1-2
[17] muscat_1.2.1 dplyr_1.0.2
[19] ggplot2_3.3.2 cowplot_1.0.0
[21] ComplexHeatmap_2.4.2 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] backports_1.1.9 circlize_0.4.10
[3] blme_1.0-4 igraph_1.2.5
[5] plyr_1.8.6 lazyeval_0.2.2
[7] TMB_1.7.16 splines_4.0.0
[9] BiocParallel_1.22.0 listenv_0.8.0
[11] scater_1.16.2 digest_0.6.25
[13] foreach_1.5.0 htmltools_0.5.0
[15] gdata_2.18.0 lmerTest_3.1-2
[17] magrittr_1.5 memoise_1.1.0
[19] cluster_2.1.0 doParallel_1.0.15
[21] ROCR_1.0-11 limma_3.44.3
[23] globals_0.12.5 annotate_1.66.0
[25] prettyunits_1.1.1 colorspace_1.4-1
[27] rappdirs_0.3.1 ggrepel_0.8.2
[29] blob_1.2.1 xfun_0.15
[31] jsonlite_1.7.0 crayon_1.3.4
[33] RCurl_1.98-1.2 genefilter_1.70.0
[35] lme4_1.1-23 zoo_1.8-8
[37] ape_5.4 survival_3.2-3
[39] iterators_1.0.12 glue_1.4.2
[41] gtable_0.3.0 zlibbioc_1.34.0
[43] XVector_0.28.0 leiden_0.3.3
[45] GetoptLong_1.0.1 BiocSingular_1.4.0
[47] future.apply_1.6.0 shape_1.4.4
[49] scales_1.1.1 DBI_1.1.0
[51] edgeR_3.30.3 Rcpp_1.0.5
[53] xtable_1.8-4 progress_1.2.2
[55] clue_0.3-57 dqrng_0.2.1
[57] reticulate_1.16 bit_1.1-15.2
[59] rsvd_1.0.3 tsne_0.1-3
[61] htmlwidgets_1.5.1 httr_1.4.1
[63] gplots_3.0.4 ellipsis_0.3.1
[65] ica_1.0-2 farver_2.0.3
[67] pkgconfig_2.0.3 XML_3.99-0.4
[69] uwot_0.1.8 locfit_1.5-9.4
[71] labeling_0.3 tidyselect_1.1.0
[73] rlang_0.4.7 reshape2_1.4.4
[75] later_1.1.0.1 AnnotationDbi_1.50.1
[77] munsell_0.5.0 tools_4.0.0
[79] generics_0.0.2 RSQLite_2.2.0
[81] ggridges_0.5.2 evaluate_0.14
[83] yaml_2.2.1 knitr_1.29
[85] bit64_0.9-7 fs_1.4.2
[87] fitdistrplus_1.1-1 caTools_1.18.0
[89] RANN_2.6.1 purrr_0.3.4
[91] pbapply_1.4-2 future_1.17.0
[93] nlme_3.1-148 whisker_0.4
[95] pbkrtest_0.4-8.6 compiler_4.0.0
[97] plotly_4.9.2.1 beeswarm_0.2.3
[99] png_0.1-7 variancePartition_1.18.2
[101] tibble_3.0.3 statmod_1.4.34
[103] geneplotter_1.66.0 stringi_1.4.6
[105] lattice_0.20-41 Matrix_1.2-18
[107] nloptr_1.2.2.2 vctrs_0.3.4
[109] pillar_1.4.6 lifecycle_0.2.0
[111] lmtest_0.9-37 GlobalOptions_0.1.2
[113] RcppAnnoy_0.0.16 BiocNeighbors_1.6.0
[115] data.table_1.12.8 bitops_1.0-6
[117] irlba_2.3.3 patchwork_1.0.1
[119] httpuv_1.5.4 colorRamps_2.3
[121] R6_2.4.1 promises_1.1.1
[123] KernSmooth_2.23-17 gridExtra_2.3
[125] vipor_0.4.5 codetools_0.2-16
[127] boot_1.3-25 MASS_7.3-51.6
[129] gtools_3.8.2 DESeq2_1.28.1
[131] rprojroot_1.3-2 rjson_0.2.20
[133] withr_2.2.0 sctransform_0.2.1
[135] GenomeInfoDbData_1.2.3 hms_0.5.3
[137] tidyr_1.1.0 glmmTMB_1.0.2.1
[139] minqa_1.2.4 rmarkdown_2.3
[141] DelayedMatrixStats_1.10.1 Rtsne_0.15
[143] git2r_0.27.1 numDeriv_2016.8-1.1
[145] ggbeeswarm_0.6.0