Biostatistical analysis part 2
AGC
2 2 2022
Last updated: 2022-06-27
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Knit directory: scSeq_Hefendehl/
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#get sample data
samples.integrated@meta.data %>% as.data.frame() -> samplemeta
# convert to correct data type
# define genotype as is
samplemeta$Genotype_corr = factor(samplemeta$Genotype=="wt", levels=c(F,T), labels = c("APPPS1+", "WT"))
samplemeta$Genotype_corr = relevel(samplemeta$Genotype_corr, ref="WT")
samplemeta$methoxy = factor(samplemeta$Genotype=="MX04+", levels=c(T,F), labels = c("MX04+", "MX04-"))
samplemeta$Treatment = as.factor(samplemeta$Treatment)
samplemeta$Treatment = relevel(samplemeta$Treatment, ref="Ctrl")
samplemeta$Mouse_ID = as.factor(samplemeta$Mouse_ID)
samplemeta$Sex = as.factor(samplemeta$Sex)
samplemeta$Brain_region = as.factor(samplemeta$Brain_region)
samplemeta$Celltype = as.factor(samplemeta$Celltype)
nCells=nrow(samplemeta)
nMice=nlevels(samplemeta$Mouse_ID)
nCelltypes=nlevels(samplemeta$Celltype)Sample descriptive:
Data contains a total of 649 Cells from 9. Q: Original raw datset containing only frankfurt data included 1149 cells. What were the filter criteria in the primary cell type analysis
Cells per Mouse
Cells per Strain
table(Mouse_ID=samplemeta$Mouse_ID) %>% as.data.frame() %>% display_tab()Cells per Genotype
table(Genotype=samplemeta$Genotype_corr, Treatment=samplemeta$Treatment) %>% as.data.frame() %>% display_tab()table(Genotype=samplemeta$Genotype_corr, Celltype=samplemeta$Celltype) %>%
as.data.frame() %>% display_tab()table(Genotype=samplemeta$Genotype_corr,
Celltype=samplemeta$Celltype, Treatment=samplemeta$Treatment
) %>%
as.data.frame() %>% display_tab()samplemeta$condition=paste0(samplemeta$Genotype_corr,"_", samplemeta$Treatment)
sce=as.SingleCellExperiment(samples.integrated)
sce <- slingshot(sce, clusterLabels = 'Celltype', reducedDim = 'UMAP')
samplemeta$Pseudotime <- sce$slingPseudotime_1
variables=c("Celltype","Sex", "Age","Genotype_corr", "Treatment", "condition","Phase", "Pseudotime","Brain_region","nCount_RNA","pseudoaligned_reads", "percent.mito", "percent.ribo", "Mouse_ID")Descriptive stats across Cell type
res = compareGroups(Celltype~., data = samplemeta[,variables], max.ylev = 10)
#summary(res)
export_table <- createTable(res)
options(width = 10000)
export2md(export_table)| T/NK | Microglia_0 | Microglia_1 | Microglia_2 | Microglia_3 | Microglia_4 | Microglia_5 | Granulocytes | p.overall | |
|---|---|---|---|---|---|---|---|---|---|
| N=22 | N=186 | N=164 | N=107 | N=99 | N=26 | N=23 | N=22 | ||
| Sex: | . | ||||||||
| f | 6 (27.3%) | 27 (14.5%) | 21 (12.8%) | 11 (10.3%) | 11 (11.1%) | 1 (3.85%) | 2 (8.70%) | 8 (36.4%) | |
| m | 16 (72.7%) | 159 (85.5%) | 143 (87.2%) | 96 (89.7%) | 88 (88.9%) | 25 (96.2%) | 21 (91.3%) | 14 (63.6%) | |
| Age: | <0.001 | ||||||||
| 10 | 13 (59.1%) | 65 (34.9%) | 35 (21.3%) | 26 (24.3%) | 32 (32.3%) | 6 (23.1%) | 12 (52.2%) | 9 (40.9%) | |
| 17 | 1 (4.55%) | 19 (10.2%) | 68 (41.5%) | 31 (29.0%) | 29 (29.3%) | 11 (42.3%) | 3 (13.0%) | 0 (0.00%) | |
| 9 | 8 (36.4%) | 102 (54.8%) | 61 (37.2%) | 50 (46.7%) | 38 (38.4%) | 9 (34.6%) | 8 (34.8%) | 13 (59.1%) | |
| Genotype_corr: | <0.001 | ||||||||
| WT | 3 (13.6%) | 66 (35.5%) | 93 (56.7%) | 51 (47.7%) | 44 (44.4%) | 15 (57.7%) | 11 (47.8%) | 6 (27.3%) | |
| APPPS1+ | 19 (86.4%) | 120 (64.5%) | 71 (43.3%) | 56 (52.3%) | 55 (55.6%) | 11 (42.3%) | 12 (52.2%) | 16 (72.7%) | |
| Treatment: | <0.001 | ||||||||
| Ctrl | 3 (13.6%) | 101 (54.3%) | 113 (68.9%) | 70 (65.4%) | 68 (68.7%) | 21 (80.8%) | 12 (52.2%) | 2 (9.09%) | |
| Stroke | 19 (86.4%) | 85 (45.7%) | 51 (31.1%) | 37 (34.6%) | 31 (31.3%) | 5 (19.2%) | 11 (47.8%) | 20 (90.9%) | |
| condition: | . | ||||||||
| APPPS1+_Ctrl | 2 (9.09%) | 82 (44.1%) | 45 (27.4%) | 39 (36.4%) | 39 (39.4%) | 10 (38.5%) | 9 (39.1%) | 2 (9.09%) | |
| APPPS1+_Stroke | 17 (77.3%) | 38 (20.4%) | 26 (15.9%) | 17 (15.9%) | 16 (16.2%) | 1 (3.85%) | 3 (13.0%) | 14 (63.6%) | |
| WT_Ctrl | 1 (4.55%) | 19 (10.2%) | 68 (41.5%) | 31 (29.0%) | 29 (29.3%) | 11 (42.3%) | 3 (13.0%) | 0 (0.00%) | |
| WT_Stroke | 2 (9.09%) | 47 (25.3%) | 25 (15.2%) | 20 (18.7%) | 15 (15.2%) | 4 (15.4%) | 8 (34.8%) | 6 (27.3%) | |
| Phase: | 0.002 | ||||||||
| G1 | 1 (4.55%) | 79 (42.5%) | 70 (42.7%) | 59 (55.1%) | 42 (42.4%) | 12 (46.2%) | 14 (60.9%) | 5 (22.7%) | |
| G2M | 11 (50.0%) | 50 (26.9%) | 38 (23.2%) | 17 (15.9%) | 21 (21.2%) | 6 (23.1%) | 4 (17.4%) | 11 (50.0%) | |
| S | 10 (45.5%) | 57 (30.6%) | 56 (34.1%) | 31 (29.0%) | 36 (36.4%) | 8 (30.8%) | 5 (21.7%) | 6 (27.3%) | |
| Pseudotime | . (.) | 12.9 (1.50) | 4.23 (1.73) | 8.66 (2.14) | 1.17 (1.76) | . (.) | 16.9 (0.61) | . (.) | <0.001 |
| Brain_region: | <0.001 | ||||||||
| Cortex | 3 (13.6%) | 101 (54.3%) | 113 (68.9%) | 70 (65.4%) | 68 (68.7%) | 21 (80.8%) | 12 (52.2%) | 2 (9.09%) | |
| Lesion | 19 (86.4%) | 85 (45.7%) | 51 (31.1%) | 37 (34.6%) | 31 (31.3%) | 5 (19.2%) | 11 (47.8%) | 20 (90.9%) | |
| nCount_RNA | 164049 (80916) | 150451 (78955) | 134224 (54802) | 170420 (76702) | 152640 (72678) | 172200 (70772) | 176087 (64299) | 144828 (65899) | 0.002 |
| pseudoaligned_reads | 165078 (81748) | 150942 (78850) | 134355 (54810) | 170750 (76639) | 152790 (72668) | 172433 (70717) | 176390 (64225) | 145316 (65972) | 0.002 |
| percent.mito | 2.23 (0.90) | 1.75 (1.17) | 1.62 (1.01) | 2.07 (1.07) | 1.72 (1.15) | 1.97 (1.02) | 2.11 (0.77) | 0.92 (1.00) | <0.001 |
| percent.ribo | 6.53 (2.72) | 2.68 (1.58) | 2.75 (1.81) | 2.44 (1.24) | 3.28 (1.67) | 2.11 (0.85) | 2.91 (1.35) | 2.01 (1.17) | <0.001 |
| Mouse_ID: | . | ||||||||
| 23#15773 | 0 (0.00%) | 7 (3.76%) | 25 (15.2%) | 12 (11.2%) | 17 (17.2%) | 4 (15.4%) | 1 (4.35%) | 0 (0.00%) | |
| 23#15774 | 0 (0.00%) | 1 (0.54%) | 30 (18.3%) | 10 (9.35%) | 6 (6.06%) | 1 (3.85%) | 1 (4.35%) | 0 (0.00%) | |
| 23#15792 | 1 (4.55%) | 11 (5.91%) | 13 (7.93%) | 9 (8.41%) | 6 (6.06%) | 6 (23.1%) | 1 (4.35%) | 0 (0.00%) | |
| 386 | 1 (4.55%) | 14 (7.53%) | 9 (5.49%) | 2 (1.87%) | 6 (6.06%) | 1 (3.85%) | 5 (21.7%) | 3 (13.6%) | |
| 387 | 11 (50.0%) | 10 (5.38%) | 5 (3.05%) | 6 (5.61%) | 5 (5.05%) | 0 (0.00%) | 1 (4.35%) | 6 (27.3%) | |
| 388 | 1 (4.55%) | 41 (22.0%) | 21 (12.8%) | 18 (16.8%) | 21 (21.2%) | 5 (19.2%) | 6 (26.1%) | 0 (0.00%) | |
| 409 | 1 (4.55%) | 33 (17.7%) | 16 (9.76%) | 18 (16.8%) | 9 (9.09%) | 3 (11.5%) | 3 (13.0%) | 3 (13.6%) | |
| 457 | 1 (4.55%) | 41 (22.0%) | 24 (14.6%) | 21 (19.6%) | 18 (18.2%) | 5 (19.2%) | 3 (13.0%) | 2 (9.09%) | |
| 461 | 6 (27.3%) | 28 (15.1%) | 21 (12.8%) | 11 (10.3%) | 11 (11.1%) | 1 (3.85%) | 2 (8.70%) | 8 (36.4%) |
export2xls(export_table,paste0(home,"/docs/Descriptives.xlsx"))download data as excel file here
Preprocessing
include only genes that are different across samples (5 974 excluded) include only genes with more than 10 reads in at least 10 cells in at least one Celltype (21 733 transcripts excluded) exclude all cells with less than 10000 reads (none excluded)
649 cells and 11 623 transcripts analyzed
#get normalized counts
# question to Desiree hat the Seurat object been initialized with normalized data?
counts <- samples.integrated@assays$RNA@counts %>% as.data.frame()
# drop no variance data and sort by samplemeta
counts <- counts[apply(counts,1, sd) > 0, rownames(samplemeta)]
# drop genes with low detection rate (more than 5 counts per cell)
counts_per_celltype=apply(counts, 1, function(x){tapply(x, samplemeta$Celltype, function(z){sum(z>10,na.rm=T)})}) %>% as.data.frame()
# keep RNAs with at least 10 cells with good expression
idxr=which(colSums(counts_per_celltype)>10)
idxc=which(colSums(counts)>10000)
counts = counts[idxr,idxc]
samplemeta=samplemeta[colnames(counts), ]Overview tables
samplemeta$corrGenotype_Treatment<-
paste0(samplemeta$Genotype_corr," ", samplemeta$Treatment)
sumstat = counts %>% t() %>%
aggregate(by=samplemeta["corrGenotype_Treatment"], median) %>%
t() %>% as.data.frame()
names(sumstat) <- paste0("median",sumstat[1,])
sumstat<-sumstat[-1,]
sumstatCelltype = counts %>% t() %>%
aggregate(by=samplemeta["Celltype"], median) %>%
t() %>% as.data.frame()
names(sumstatCelltype) <- paste0("median",sumstatCelltype[1,])
sumstatCelltype<-sumstatCelltype[-1,]
Geno.Treatment_kruskal=apply(counts,1, function(x){
res = kruskal.test(unlist(x)~samplemeta$corrGenotype_Treatment) %>% unlist()
return(as.numeric(res["p.value"]))
})
Geno.Treatment_kruskal_fdr = p.adjust(Geno.Treatment_kruskal, method = "fdr")
Celltype_kruskal=apply(counts,1, function(x){
res = kruskal.test(unlist(x)~samplemeta$Celltype) %>% unlist()
return(as.numeric(res["p.value"]))
})
Celltype_kruskal_fdr = p.adjust(Celltype_kruskal, method = "fdr")
summarytab=cbind(sumstat, Geno.Treatment_kruskal, Geno.Treatment_kruskal_fdr , sumstatCelltype, Celltype_kruskal, Celltype_kruskal_fdr)
summarytab %>% display_tab()Warning in instance$preRenderHook(instance): It seems your data is too big for client-side DataTables. You may consider server-side processing: https://rstudio.github.io/DT/server.htmlHierarchical clustering
to check where the variance in the data comes from
log2_cpm = log2(counts+1)
varsset=apply(log2_cpm, 1, var)
cpm.sel.trans = t(log2_cpm[order(varsset,decreasing = T)[1:1000],])
distance = dist(cpm.sel.trans)
sampleDistMatrix <- as.matrix(distance)
#colors for plotting heatmap
colors <- rev(colorRampPalette(brewer.pal(9, "Spectral"))(255))
colors=jetcolors(255)
colors=viridis(255)
cellcol = Dark8[1:nlevels(samplemeta$Celltype)]
names(cellcol) = levels(samplemeta$Celltype)
genotypecol = brewer.pal(4,"Accent")[c(1:nlevels(samplemeta$Genotype_corr))]
names(genotypecol) = levels(samplemeta$Genotype_corr)
strokecol = brewer.pal(5,"Set2")[1:nlevels(samplemeta$Treatment)+2]
names(strokecol) = levels(samplemeta$Treatment)
mousecol = brewer.pal(9,"Set1")[1:nlevels(samplemeta$Mouse_ID)]
names(mousecol) = levels(samplemeta$Mouse_ID)
braincol = brewer.pal(3,"Set2")[1:nlevels(samplemeta$Brain_region)]
names(braincol) = levels(samplemeta$Brain_region)
samplemeta$Age = as.factor(samplemeta$Age)
Agecol = colorRampPalette(c("dodgerblue",
"dodgerblue4"))(nlevels(samplemeta$Age))[1:nlevels(samplemeta$Age)]
names(Agecol) = as.character(sort(as.numeric(levels(samplemeta$Age))))
ann_colors = list(
Genotype_corr = genotypecol,
Mouse_ID = mousecol,
Brain_region = braincol,
Celltype=cellcol,
Treatment=strokecol,
Age=Agecol
)
labels = samplemeta[,c("Genotype_corr","Mouse_ID", "Age", "Brain_region", "Celltype", "Treatment")] %>%
mutate_all(as.character) %>% as.data.frame()
labels$Age = as.numeric(labels$Age)
rownames(labels)=rownames(samplemeta)
pheatmap(sampleDistMatrix,
clustering_distance_rows = distance,
clustering_distance_cols = distance,
clustering_method = "ward.D2",
scale ="none",
legend = F,
show_rownames=F, show_colnames = F,
border_color = NA,
annotation_row = labels,
annotation_col = labels,
annotation_colors = ann_colors,
col = colors,
main = "D62 top1000 Distances normalized log2 counts")
| Version | Author | Date |
|---|---|---|
| cfbfcf6 | achiocch | 2022-06-22 |
Genotype X Treatment Statistical modelling
the logFC should not be interpreted on its own without the specific post hoc tests (see app below to check the individual genes) In any case the value here would correspond to the APPPS1+ with Stroke against all others.
Heatmaps show standardized deviations from mean across all cells, trimmed to 2 standard deviations ( i.e. values above 2 SD are set to 2 SD). this allows to see more subtle changes better.
getres=function(Celltype="Specify",
Hypothesis="~1+Sex+Genotype_corr*Treatment",
Target="Genotype_corrAPPPS1+:TreatmentStroke",
Randomeffect=NULL){
res= comparison_rand(designform =Hypothesis,
randomeffect = Randomeffect,
Samples = samplemeta$Celltype==Celltype,
log_cpm = log2_cpm,
samplesdata = samplemeta,
target=Target)
labels = samplemeta[samplemeta$Celltype==Celltype,c("Treatment","Age", "Genotype_corr","Mouse_ID", "Brain_region", "Celltype")] %>%
mutate_all(as.character) %>% as.data.frame()
labels$Age = as.numeric(labels$Age)
labels=labels %>% arrange(Treatment,Genotype_corr)
plotdata=log2_cpm[res$adj.P.Val<0.05,rownames(labels)]
plotdata = apply(plotdata, 2, trimmed_scaled)
pheatmap(plotdata,
#clustering_method = "ward.D2",
cluster_cols = F,
cluster_rows = T,
scale ="row",
show_rownames=F, show_colnames = F,
legend=T,
border_color = NA,
#annotation_row = labels,
annotation_col = labels,
annotation_colors = ann_colors,
col = colors,
breaks = seq(-2,2, length.out=254),
main = "D62 Distances normalized log2 counts")
return(res)
}
generate_output=function(CT="Celltype", ...){
respath=paste0(home, "/docs/LMER_",CT,".xlsx")
analysis = getres(Celltype = CT)
analysis_sig = analysis[analysis$adj.P.Val<0.05,]
analysis_sig %>% display_tab()
write.xlsx2(analysis_sig, file =respath , sheetName = "significant genes")
ResGO = getGOresults(rownames(analysis_sig),
rownames(analysis),
"mmusculus")
if(length(ResGO)>0){
p=gostplot(ResGO)
} else{
ResGO=data.frame(result="no significant enrichment identified")
p="no significant enrichment identified"
}
write.xlsx2(ResGO$result, file = respath, sheetName = "GO_enrichment", append=T)
return(list(results=analysis,results_sig=analysis_sig, plot=p))
}Microglia_0
res_output=generate_output("Microglia_0")
res_output[["results_sig"]] %>% display_tab()res_output[["plot"]]Data sources and their abbreviations are: Gene Ontology (GO or by branch GO:MF, GO:BP, GO:CC)KEGG (KEGG) TRANSFAC (TF) miRTarBase (MIRNA) CORUM (CORUM) Human phenotype ontology (HP) Human Protein Atlas (HPA)
get results table here
Microglia_1
res_output=generate_output(CT = "Microglia_1")
res_output[["results_sig"]] %>% display_tab()res_output[["plot"]]Data sources and their abbreviations are: Gene Ontology (GO or by branch GO:MF, GO:BP, GO:CC)KEGG (KEGG) TRANSFAC (TF) miRTarBase (MIRNA) CORUM (CORUM) Human phenotype ontology (HP) Human Protein Atlas (HPA)
get results table here
Microglia_2
res_output=generate_output("Microglia_2")
res_output[["results_sig"]] %>% display_tab()res_output[["plot"]]Data sources and their abbreviations are: Gene Ontology (GO or by branch GO:MF, GO:BP, GO:CC)KEGG (KEGG) TRANSFAC (TF) miRTarBase (MIRNA) CORUM (CORUM) Human phenotype ontology (HP) Human Protein Atlas (HPA)
get results table here
Microglia_3
res_output=generate_output("Microglia_3")
[1] "no significant GO terms identified"res_output[["results_sig"]] %>% display_tab()res_output[["plot"]][1] "no significant enrichment identified"Data sources and their abbreviations are: Gene Ontology (GO or by branch GO:MF, GO:BP, GO:CC)KEGG (KEGG) TRANSFAC (TF) miRTarBase (MIRNA) CORUM (CORUM) Human phenotype ontology (HP) Human Protein Atlas (HPA)
get results table here
Microglia_4
calculation not converging as Celltype not identified in all conditions however also rare in other conditions, no significant difference
idx=samplemeta$Celltype=="Microglia_4"
table(samplemeta$Treatment[idx], samplemeta$Genotype_corr[idx])
WT APPPS1+
Ctrl 11 10
Stroke 4 1table(samplemeta$Treatment[idx], samplemeta$Genotype_corr[idx]) %>% fisher.test()
Fisher's Exact Test for Count Data
data: .
p-value = 0.3562
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.005090941 3.588141911
sample estimates:
odds ratio
0.2877466 #res_output=generate_output("Microglia_4")
#res_output[["results_sig"]] %>% display_tab()
#res_output[["plot"]]get results table here
Microglia_5
res_output=generate_output("Microglia_5")
[1] "no significant GO terms identified"res_output[["results_sig"]] %>% display_tab()res_output[["plot"]][1] "no significant enrichment identified"Data sources and their abbreviations are: Gene Ontology (GO or by branch GO:MF, GO:BP, GO:CC)KEGG (KEGG) TRANSFAC (TF) miRTarBase (MIRNA) CORUM (CORUM) Human phenotype ontology (HP) Human Protein Atlas (HPA) get results table here
Granulozytes
calculation not converging as Celltype not identified in all conditions however also rare in other conditions, no significant difference
idx=samplemeta$Celltype=="Granulocytes"
table(samplemeta$Treatment[idx], samplemeta$Genotype_corr[idx])
WT APPPS1+
Ctrl 0 2
Stroke 6 14table(samplemeta$Treatment[idx], samplemeta$Genotype_corr[idx]) %>% fisher.test()
Fisher's Exact Test for Count Data
data: .
p-value = 1
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.0000 14.7518
sample estimates:
odds ratio
0 # res_output=generate_output("Granulocytes ")
# res_output[["results_sig"]] %>% display_tab()
# res_output[["plot"]]get results table here
T_NK
idx=samplemeta$Celltype=="T/NK"
table(samplemeta$Treatment[idx], samplemeta$Genotype_corr[idx])
WT APPPS1+
Ctrl 1 2
Stroke 2 17table(samplemeta$Treatment[idx], samplemeta$Genotype_corr[idx]) %>% fisher.test()
Fisher's Exact Test for Count Data
data: .
p-value = 0.3708
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.0481749 117.2316789
sample estimates:
odds ratio
3.869104 # res_output=generate_output("T/NK")
# res_output[["results_sig"]] %>% display_tab()
# res_output[["plot"]]get results table here
Pseudotime analysis
colors <- viridis(100)
plotcol <- colors[cut(sce$slingPseudotime_1, breaks=100)]
plot(reducedDims(sce)$UMAP, col = "grey", pch=16, asp = 1)
points(reducedDims(sce)$UMAP, col = plotcol, pch=16, asp = 1)
lines(SlingshotDataSet(sce), lwd=2, col='black', type="l")
plot(samplemeta$Pseudotime[samplemeta$condition == "WT_Ctrl"] %>% density(na.rm=T), col=Dark8[1], lwd=2, xlab="Pseudotime", main="distribution of cells over pseudotime")
lines(samplemeta$Pseudotime[samplemeta$condition == "APPPS1+_Stroke"] %>% density(na.rm=T), col=Dark8[2], lwd=2)
lines(samplemeta$Pseudotime[samplemeta$condition == "WT_Stroke"] %>% density(na.rm=T), col=Dark8[3], lwd=2)
lines(samplemeta$Pseudotime[samplemeta$condition == "APPPS1+_Ctrl"] %>% density(na.rm=T), col=Dark8[4], lwd=2)
legend("topright", legend=c("WT_Ctrl","APPPS1+_Stroke","WT_Stroke","APPPS1+_Ctrl"), fill = Dark8[1:4])
| Version | Author | Date |
|---|---|---|
| 5bba014 | achiocch | 2022-06-27 |
Gene expression plot
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 base
other attached packages:
[1] gprofiler2_0.2.1 lm.beta_1.6-2 pheatmap_1.0.12 RColorBrewer_1.1-3 kableExtra_1.3.4 slingshot_2.4.0 TrajectoryUtils_1.4.0 SingleCellExperiment_1.18.0 princurve_2.1.6 DT_0.23 viridis_0.6.2 viridisLite_0.4.0 xlsx_0.6.5 brms_2.17.0 Rcpp_1.0.8.3 compareGroups_4.5.1 data.table_1.14.2 SingleR_1.10.0 sp_1.5-0 SeuratObject_4.1.0 Seurat_4.1.1 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.9 purrr_0.3.4 readr_2.1.2 tidyr_1.2.0 tibble_3.1.7 ggplot2_3.3.6 tidyverse_1.3.1 DESeq2_1.36.0 SummarizedExperiment_1.26.1 Biobase_2.56.0 MatrixGenerics_1.8.0 matrixStats_0.62.0 GenomicRanges_1.48.0 GenomeInfoDb_1.32.2 IRanges_2.30.0 S4Vectors_0.34.0 BiocGenerics_0.42.0 limma_3.52.2 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] rsvd_1.0.5 ica_1.0-2 svglite_2.1.0 ps_1.7.1 lmtest_0.9-40 rprojroot_2.0.3 crayon_1.5.1 spatstat.core_2.4-4 MASS_7.3-57 nlme_3.1-158 backports_1.4.1 posterior_1.2.2 reprex_2.0.1 colourpicker_1.1.1 rlang_1.0.2 XVector_0.36.0 ROCR_1.0-11 readxl_1.4.0 irlba_2.3.5 callr_3.7.0 flextable_0.7.2 BiocParallel_1.30.3 bit64_4.0.5 glue_1.6.2 loo_2.5.1 sctransform_0.3.3 rstan_2.21.5 parallel_4.2.0 processx_3.6.1 spatstat.sparse_2.1-1 AnnotationDbi_1.58.0 spatstat.geom_2.4-0 haven_2.5.0 tidyselect_1.1.2 fitdistrplus_1.1-8 XML_3.99-0.10 zoo_1.8-10 packrat_0.8.0 distributional_0.3.0 chron_2.3-57 xtable_1.8-4 magrittr_2.0.3 evaluate_0.15 gdtools_0.2.4 cli_3.3.0 zlibbioc_1.42.0 rstudioapi_0.13 miniUI_0.1.1.1 whisker_0.4 bslib_0.3.1 rpart_4.1.16 shinystan_2.6.0 shiny_1.7.1 BiocSingular_1.12.0 xfun_0.31 askpass_1.1 inline_0.3.19 pkgbuild_1.3.1 cluster_2.1.3 bridgesampling_1.1-2 KEGGREST_1.36.2 Brobdingnag_1.2-7 ggrepel_0.9.1 threejs_0.3.3 listenv_0.8.0 xlsxjars_0.6.1 Biostrings_2.64.0 png_0.1-7 future_1.26.1 withr_2.5.0 bitops_1.0-7 plyr_1.8.7 cellranger_1.1.0 coda_0.19-4 pillar_1.7.0 RcppParallel_5.1.5 cachem_1.0.6 fs_1.5.2 DelayedMatrixStats_1.18.0 xts_0.12.1 vctrs_0.4.1 ellipsis_0.3.2 generics_0.1.2 dygraphs_1.1.1.6 tools_4.2.0 munsell_0.5.0 DelayedArray_0.22.0 fastmap_1.1.0 compiler_4.2.0 abind_1.4-5 httpuv_1.6.5 plotly_4.10.0 rgeos_0.5-9 rJava_1.0-6 GenomeInfoDbData_1.2.8 gridExtra_2.3 lattice_0.20-45 deldir_1.0-6 utf8_1.2.2 later_1.3.0 jsonlite_1.8.0 scales_1.2.0 ScaledMatrix_1.4.0 pbapply_1.5-0 sparseMatrixStats_1.8.0 genefilter_1.78.0 lazyeval_0.2.2 promises_1.2.0.1 goftest_1.2-3 spatstat.utils_2.3-1 reticulate_1.25 checkmate_2.1.0 rmarkdown_2.14 cowplot_1.1.1 webshot_0.5.3 Rtsne_0.16 uwot_0.1.11 igraph_1.3.2 survival_3.3-1 rsconnect_0.8.26 yaml_2.3.5 systemfonts_1.0.4 bayesplot_1.9.0 htmltools_0.5.2 rstantools_2.2.0 memoise_2.0.1 locfit_1.5-9.5 digest_0.6.29 assertthat_0.2.1 mime_0.12 RSQLite_2.2.14 future.apply_1.9.0 blob_1.2.3 shinythemes_1.2.0 splines_4.2.0 RCurl_1.98-1.7 broom_0.8.0 hms_1.1.1 modelr_0.1.8 colorspace_2.0-3 base64enc_0.1-3 BiocManager_1.30.18 nnet_7.3-17 sass_0.4.1 RANN_2.6.1 mvtnorm_1.1-3 fansi_1.0.3 tzdb_0.3.0 truncnorm_1.0-8 parallelly_1.32.0 R6_2.5.1 grid_4.2.0 ggridges_0.5.3 lifecycle_1.0.1 StanHeaders_2.21.0-7 zip_2.2.0 writexl_1.4.0 curl_4.3.2 leiden_0.4.2 jquerylib_0.1.4 Matrix_1.4-1 RcppAnnoy_0.0.19 htmlwidgets_1.5.4 officer_0.4.3 beachmat_2.12.0 polyclip_1.10-0 markdown_1.1 crosstalk_1.2.0 rvest_1.0.2 mgcv_1.8-40 globals_0.15.0 openssl_2.0.2 patchwork_1.1.1 spatstat.random_2.2-0 tensorA_0.36.2 progressr_0.10.1 codetools_0.2-18 lubridate_1.8.0 gtools_3.9.2.2 getPass_0.2-2 prettyunits_1.1.1 dbplyr_2.2.0 gtable_0.3.0 DBI_1.1.3 git2r_0.30.1 tensor_1.5 httr_1.4.3 highr_0.9 KernSmooth_2.23-20 stringi_1.7.6 reshape2_1.4.4 farver_2.1.0 uuid_1.1-0 annotate_1.74.0 mice_3.14.0 xml2_1.3.3 shinyjs_2.1.0 BiocNeighbors_1.14.0 geneplotter_1.74.0 scattermore_0.8 bit_4.0.4 spatstat.data_2.2-0 pkgconfig_2.0.3 HardyWeinberg_1.7.5 Rsolnp_1.16 knitr_1.39