QC and filtering
Last updated: 2023-01-17
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Multimodal-Plasmacell_manuscript/
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The single-cell multi-omics data contains single-cell transcriptomic and proteomic and phospho-proteomic data of in vitro generated antibody-secreting cells. The code below generates quality control plots, performs filtering, normalization and scaling of the counts
Count matrix to Seurat object
Import all count matrixes, combine plates and create unfiltered Seurat objects.
myfiles <- list.files(path="output/", pattern = ".rds$")
## only read all raw files and create raw combined table if not done yet. Speeds up generation of html file
if ("seu.RNA.rds" %in% myfiles) {
seu_RNA <- readRDS("output/seu.RNA.rds")
seu.PROT_live <- readRDS("output/seu.PROT_live.rds")
seu.PROT_fix <- readRDS("output/seu.PROT_fix.rds")
} else {
source("code/Import_and_create_seuratObj.R")
}QC plots and filters
plot_RNA_nCount <- plot_QC_paper(
seu_object = seu_RNA,
feature = "nCount_RNA",
ytext = "Total UMI counts per cell",
xtext = "Plate number",
paneltitle = "Fixed cells (1586 to 1589) show lower counts",
colorviolin = "dodgerblue2"
) +
geom_vline(xintercept = 6.5,
size = 0.3,
color = "red") +
annotate(
geom = "text",
x = 6.6,
y = 20000,
label = "Fixed cells",
hjust = 0,
size = 2.5
) +
theme(axis.title.x = element_blank())
plot_RNA_ngenes <- plot_QC_paper(
seu_object = seu_RNA,
feature = "nFeature_RNA",
ytext = "Total genes per cell",
xtext = "Plate number",
paneltitle = "Keep cells >300 genes",
colorviolin = "dodgerblue2"
) +
geom_hline(yintercept = 300,
size = 0.3,
color = "red") +
theme(axis.title.x = element_blank())
plot_percent.mt <- plot_QC_paper(
seu_object = seu_RNA,
feature = "percent.mt",
ytext = "% Mitochondrial counts",
xtext = "Plate number",
paneltitle = "Keep cells < 5 % mitochondrial genecounts",
colorviolin = "dodgerblue2"
) +
geom_hline(yintercept = 5,
color = "red",
size = 0.3) +
theme(axis.title.x = element_blank())
plot_percent.rb <- plot_QC_paper(
seu_object = seu_RNA,
feature = "percent.rb",
ytext = "% Ribosomal counts",
xtext = "Plate number",
paneltitle = "comparable % ribosomal counts in all plates",
colorviolin = "dodgerblue2"
) +
theme(axis.title.x = element_blank())
plot_PROT_nCount.live <- plot_QC_paper(
seu_object = seu.PROT_live,
feature = "nCount_PROT",
ytext = "Total UMI counts per cell",
xtext = "Plate number",
paneltitle = "Keep cells > 1500 & < 9000 PROT counts",
colorviolin = "deeppink3"
) +
geom_hline(yintercept = 1500, size = 0.3) +
geom_hline(yintercept = 9000, size = 0.3) +
theme(axis.title.x = element_blank())
plot_PROT_nCount.fix <- plot_QC_paper(
seu_object = seu.PROT_fix,
feature = "nCount_PROT",
ytext = "Total UMI counts per cell",
xtext = "Plate number",
paneltitle = "Keep cells > 2500 & < 20000 PROT counts",
colorviolin = "deeppink3"
) +
geom_hline(yintercept = 2500, size = 0.3) +
geom_hline(yintercept = 20000, size = 0.3) +
theme(axis.title.x = element_blank())
plot_PROT_nproteins.live <-
plot_QC_paper(
seu_object = seu.PROT_live,
feature = "nFeature_PROT",
ytext = "Total proteins per cell",
xtext = "Plate number",
paneltitle = "Keep cells >40 proteins",
colorviolin = "deeppink3"
) +
geom_hline(yintercept = 40,
size = 0.3,
color = "red") +
theme(axis.title.x = element_blank())
plot_PROT_nproteins.fix <- plot_QC_paper(
seu_object = seu.PROT_fix,
feature = "nFeature_PROT",
ytext = "Total proteins per cell",
xtext = "Plate number",
paneltitle = "Keep cells >65 proteins",
colorviolin = "deeppink3"
) +
geom_hline(yintercept = 65,
size = 0.3,
color = "red") +
theme(axis.title.x = element_blank())
plot.QC <- plot_grid(
plot_RNA_nCount,
plot_RNA_ngenes,
plot_percent.mt,
plot_percent.rb,
plot_PROT_nCount.live,
plot_PROT_nCount.fix,
plot_PROT_nproteins.live,
plot_PROT_nproteins.fix,
labels = c('a', 'b', 'c', 'd' , 'e', 'f', 'g', 'h'),
label_size = 10,
ncol = 2
)
ggsave(
plot.QC,
filename = "output/paper_figures/Suppl_QC_filters.pdf",
width = 177,
height = 200,
units = "mm",
dpi = 300
)
ggsave(
plot.QC,
filename = "output/paper_figures/Suppl_QC_filters.eps",
width = 177,
height = 200,
units = "mm",
dpi = 300
)
ggsave(
plot.QC,
filename = "output/paper_figures/Suppl_QC_filters.png",
width = 177,
height = 200,
units = "mm",
dpi = 300
)plot.QC
Supplementary Figure Thresholds for selection of high-quality
samples and cells.
- Based on the indicated cut-offs, high-quality cells are filtered for further analysis.
## Filter fixed protein dataset
seu.PROT.fix.subset <- subset(seu.PROT_fix, subset = nCount_PROT >= 2500 & nCount_PROT < 20000)
## Filter live-cell protein dataset
seu.PROT.live.subset <- subset(seu.PROT_live, subset = nCount_PROT >= 1500 & nCount_PROT <= 9000)
## RNA quality of fixed dataset is too low (very low gene numbers and counts). Therefore continue only with live-cell dataset.
seu.RNA_live <- subset(seu_RNA, idents = c(1586:1589), invert = TRUE)
seu.RNA_fix <- subset(seu_RNA, idents = c(1586:1589))
## Filter RNA live dataset
seu.RNA_live.subset <- subset(seu.RNA_live, subset = percent.mt <=5 & nFeature_RNA >= 300)
seu.RNA_fix.subset <- subset(seu.RNA_fix) #, subset = percent.mt <= 5 & nFeature_RNA >= 300 # Nofilter because RNA not taken along.- Filter low-detected genes: Keep genes that are >1% cells detected.
## Additional filter features (genes) detected in 1% of cells
seu.RNA_live.subset <- CreateSeuratObject(seu.RNA_live.subset[["RNA"]]@counts, min.cells = round(ncol(seu.RNA_live.subset)/100)) ## keep features detected in 1% of cells
seu.RNA_fix.subset <- CreateSeuratObject(seu.RNA_fix.subset[["RNA"]]@counts, min.cells = round(ncol(seu.RNA_fix.subset)/100)) ## keep features detected in min 1% cells- Merge Seurat objects from Protein & RNA modalities
## Merge Seurat objects live dataset
intersect <- colnames(seu.RNA_live.subset)[colnames(seu.RNA_live.subset) %in% colnames(seu.PROT.live.subset)]
intersect <- colnames(seu.PROT.live.subset)[colnames(seu.PROT.live.subset) %in% intersect]
seu.RNA_combined.live <- subset(seu.RNA_live.subset, cells = intersect )
Prot.live.intersect <- seu.PROT.live.subset@assays$PROT@counts[,colnames(seu.PROT.live.subset) %in% intersect]
seu.RNA_combined.live[["PROT"]] <- CreateAssayObject(counts = Prot.live.intersect)
seu.RNA_combined.liveAn object of class Seurat
10211 features across 1433 samples within 2 assays
Active assay: RNA (10158 features, 0 variable features)
1 other assay present: PROT## fix dataset
intersect <- colnames(seu.RNA_fix.subset)[colnames(seu.RNA_fix.subset) %in% colnames(seu.PROT.fix.subset)]
intersect <- colnames(seu.PROT.fix.subset)[colnames(seu.PROT.fix.subset) %in% intersect]
seu.RNA_combined.fix <- subset(seu.RNA_fix.subset, cells = intersect )
Prot.fix.intersect <- seu.PROT.fix.subset@assays$PROT@counts[,colnames(seu.PROT.fix.subset) %in% intersect]
seu.RNA_combined.fix[["PROT"]] <- CreateAssayObject(counts = Prot.fix.intersect)
seu.RNA_combined.fixAn object of class Seurat
5095 features across 1038 samples within 2 assays
Active assay: RNA (5019 features, 0 variable features)
1 other assay present: PROT- Antibody quality (Non-detected proteins) filter: remove proteins with median counts < 0.2 (fixed cells), or < 1 (live cells)
PROT_tbl_subset.fix <- as.data.frame(seu.PROT.fix.subset@assays$PROT@counts) %>%
mutate(protein = rownames(seu.PROT.fix.subset)) %>%
dplyr::select(protein, everything()) %>%
gather("cell", "count", 2:c(ncol(seu.PROT.fix.subset)+1)) %>%
mutate(sample = gsub('.{5}$', '', cell) )
prot.median.fix <- aggregate(PROT_tbl_subset.fix[, 3], list(protein =PROT_tbl_subset.fix$protein), mean)
prot.fix.toremove <- prot.median.fix$protein[prot.median.fix$x <=0.2]
filtered.prot.counts <- seu.PROT.fix.subset[["PROT"]]@counts[!c(rownames(seu.PROT.fix.subset[["PROT"]]@counts) %chin% prot.fix.toremove),]
seu.PROT.fix.subset <- CreateSeuratObject(filtered.prot.counts, assay = "PROT")
## Live cells
PROT_tbl_subset.live <- as.data.frame(seu.PROT_live@assays$PROT@counts) %>%
mutate(protein = rownames(seu.PROT_live[["PROT"]])) %>%
dplyr::select(protein, everything()) %>%
gather("cell", "count", 2:c(ncol(seu.PROT_live[["PROT"]])+1)) %>%
mutate(sample = gsub('.{9}$', '', cell) )
prot.median.live <- aggregate(PROT_tbl_subset.live[, 3], list(protein =PROT_tbl_subset.live$protein), mean)
prot.live.toremove <- prot.median.live$protein[prot.median.live$x <1]
filtered.prot.counts.live <- seu.RNA_combined.live[["PROT"]]@counts[!c(rownames(seu.RNA_combined.live[["PROT"]]@counts) %chin% prot.live.toremove),]
seu.RNA_combined.live[["PROT"]] <- CreateAssayObject(counts = filtered.prot.counts.live)- Add metadata to object.
## metadata import
metadata <- read_delim("data/metadata.txt", "\t", escape_double = FALSE, trim_ws = TRUE)
metadata$sample <- as.factor(metadata$sample)
## add metadata to fix dataset
meta.fix <- data.frame(seu.RNA_combined.fix@meta.data) %>%
mutate(sample = orig.ident ) %>%
left_join(metadata) %>%
mutate(group = sample)
meta.fix<-as.data.frame(meta.fix)
rownames(meta.fix) <- rownames(data.frame(seu.RNA_combined.fix@meta.data) )
seu.RNA_combined.fix <- AddMetaData(object = seu.RNA_combined.fix, metadata = meta.fix)
#meta.fix <- data.frame(seu.RNA_combined.fix@meta.data) %>%
# mutate(sample = rownames(seu.RNA_combined.fix@meta.data))
## add metadata to live dataset
meta.live <- data.frame(seu.RNA_combined.live@meta.data) %>%
mutate(sample = orig.ident ) %>%
left_join(metadata) %>%
mutate(group = sample)
meta.live<-as.data.frame(meta.live)
rownames(meta.live) <- rownames(data.frame(seu.RNA_combined.live@meta.data) )
seu.RNA_combined.live <- AddMetaData(object = seu.RNA_combined.live, metadata = meta.live)
#meta.live <- data.frame(seu.RNA_combined.live@meta.data) %>%
# mutate(sample = rownames(seu.RNA_combined.live@meta.data))
seu.RNA_combined.live[["percent.mt"]] <- PercentageFeatureSet(seu.RNA_combined.live, pattern = "^MT")
seu.RNA_combined.fix[["percent.mt"]] <- PercentageFeatureSet(seu.RNA_combined.fix, pattern = "^MT")Normalize and Scale
Finally, the datasets are normalized (SCT for RNA, CLR for (phospho-)protein), and scaled (regress out: nCount, percentage mitochondiral, and plate ID for RNA, and regress out: nCount and plate ID for protein).
## fix data normalize RNA
DefaultAssay(seu.RNA_combined.fix) <- 'RNA'
seu.RNA_combined.fix <- SCTransform(seu.RNA_combined.fix, assay = "RNA", new.assay.name = "SCT", vars.to.regress = c("nCount_RNA", "percent.mt", "plate"), return.only.var.genes = FALSE, verbose = FALSE)
# Add some metadata to normalized data (ncounts & percent mt)
seu.RNA_combined.fix <- AddMetaData(seu.RNA_combined.fix, as.data.frame(seu.RNA_combined.fix@assays$SCT@counts) %>% summarise_all(funs(sum)) %>% unlist(), col.name = "nCount_RNA_SCT")
seu.RNA_combined.fix <- PercentageFeatureSet(seu.RNA_combined.fix, pattern = "^MT\\.|^MTRN", col.name = "percent.mt.aftersct", assay = "SCT")
## Fixed dataset normalize protein
DefaultAssay(seu.RNA_combined.fix) <- 'PROT'
VariableFeatures(seu.RNA_combined.fix) <- rownames(seu.RNA_combined.fix[["PROT"]])
seu.RNA_combined.fix <- NormalizeData(seu.RNA_combined.fix, normalization.method = 'CLR', margin = 2, assay = "PROT") %>%
ScaleData(vars.to.regress = c("nCount_PROT", "plate"))
## live data normalize RNA
DefaultAssay(seu.RNA_combined.live) <- 'RNA'
seu.RNA_combined.live <- SCTransform(seu.RNA_combined.live, assay = "RNA", new.assay.name = "SCT", vars.to.regress = c("nCount_RNA", "percent.mt", "plate"), return.only.var.genes = FALSE, verbose = FALSE)
# Add some metadata to normalized data (ncounts & percent mt)
seu.RNA_combined.live <- AddMetaData(seu.RNA_combined.live, as.data.frame(seu.RNA_combined.live@assays$SCT@counts) %>% summarise_all(funs(sum)) %>% unlist(), col.name = "nCount_RNA_SCT")
seu.RNA_combined.live <- PercentageFeatureSet(seu.RNA_combined.live, pattern = "^MT\\.|^MTRN", col.name = "percent.mt.aftersct", assay = "SCT")
## live normalize & scale protein data
DefaultAssay(seu.RNA_combined.live) <- 'PROT'
VariableFeatures(seu.RNA_combined.live) <- rownames(seu.RNA_combined.live[["PROT"]])
seu.RNA_combined.live <- NormalizeData(seu.RNA_combined.live, normalization.method = 'CLR', margin = 2, assay = "PROT") %>%
ScaleData(vars.to.regress = c("nCount_PROT", "plate")) Filtered dataset properties
Overview of the number of cells and data properties of all plates.
Live-cells RNA & surface protein dataset
seu.RNA_combined.liveAn object of class Seurat
20366 features across 1433 samples within 3 assays
Active assay: PROT (50 features, 50 variable features)
2 other assays present: RNA, SCTTable Overview of per plate properties after filtering.
kable(seu.RNA_combined.live@meta.data %>%
group_by(donor,plate) %>%
summarise(`Number of cells` = round(n(),0),
`Median counts RNA` = round(median(nCount_RNA),0),
`Median Number genes` = round(median(nFeature_RNA),0),
`Median Mitochondrial counts (Median %)` = round(median(percent.mt),2),
`Median counts PROT` = round(median(nCount_PROT),0),
`Number proteins` = round(median(nFeature_PROT),0)
)) %>%
kable_styling(bootstrap_options = c("striped", "hover"))| donor | plate | Number of cells | Median counts RNA | Median Number genes | Median Mitochondrial counts (Median %) | Median counts PROT | Number proteins |
|---|---|---|---|---|---|---|---|
| D1 | P_1578 | 216 | 1624 | 556 | 1.14 | 3842 | 46 |
| D1 | P_1579 | 293 | 2333 | 861 | 1.21 | 3601 | 45 |
| D2 | P_1580 | 274 | 2888 | 1040 | 1.18 | 3908 | 47 |
| D2 | P_1584 | 184 | 3688 | 1220 | 1.21 | 4383 | 48 |
| D3 | P_1582 | 231 | 1150 | 524 | 1.11 | 3575 | 47 |
| D3 | P_1585 | 235 | 3706 | 1133 | 1.24 | 3831 | 47 |
Table Overview of per donor properties after filtering.
kable(seu.RNA_combined.live@meta.data %>%
group_by(donor) %>%
summarise(`Number of cells` = round(n(),0),
`Median counts RNA` = round(median(nCount_RNA),0),
`Median Number genes` = round(median(nFeature_RNA),0),
`Median Mitochondrial counts (Median %)` = round(median(percent.mt),2),
`Median counts PROT` = round(median(nCount_PROT),0),
`Number proteins` = round(median(nFeature_PROT),0)
)) %>%
kable_styling(bootstrap_options = c("striped", "hover"))| donor | Number of cells | Median counts RNA | Median Number genes | Median Mitochondrial counts (Median %) | Median counts PROT | Number proteins |
|---|---|---|---|---|---|---|
| D1 | 509 | 2008 | 732 | 1.18 | 3693 | 46 |
| D2 | 458 | 3168 | 1122 | 1.18 | 4098 | 47 |
| D3 | 466 | 1817 | 731 | 1.17 | 3694 | 47 |
Fixed cells intracellular proteins dataset
seu.RNA_combined.fixAn object of class Seurat
10114 features across 1038 samples within 3 assays
Active assay: PROT (76 features, 76 variable features)
2 other assays present: RNA, SCTTable Overview of per plate properties after filtering.
kable(seu.RNA_combined.fix@meta.data %>%
group_by(donor,plate) %>%
summarise(`Number of cells` = round(n(),0),
`Median counts RNA` = round(median(nCount_RNA),0),
`Median Number genes` = round(median(nFeature_RNA),0),
`Median Mitochondrial counts (Median %)` = round(median(percent.mt),2),
`Median counts PROT` = round(median(nCount_PROT),0),
`Number proteins` = round(median(nFeature_PROT),0)
)) %>%
kable_styling(bootstrap_options = c("striped", "hover"))| donor | plate | Number of cells | Median counts RNA | Median Number genes | Median Mitochondrial counts (Median %) | Median counts PROT | Number proteins |
|---|---|---|---|---|---|---|---|
| D2 | P_1586 | 290 | 280 | 106 | 0.52 | 7664 | 72 |
| D2 | P_1587 | 232 | 266 | 120 | 0.99 | 8492 | 72 |
| D3 | P_1588 | 254 | 322 | 140 | 0.57 | 7250 | 72 |
| D3 | P_1589 | 262 | 272 | 116 | 0.81 | 8704 | 72 |
Table Overview of per donor properties after filtering.
kable(seu.RNA_combined.fix@meta.data %>%
group_by(donor) %>%
summarise(`Number of cells` = round(n(),0),
`Median counts RNA` = round(median(nCount_RNA),0),
`Median Number genes` = round(median(nFeature_RNA),0),
`Median Mitochondrial counts (Median %)` = round(median(percent.mt),2),
`Median counts PROT` = round(median(nCount_PROT),0),
`Number proteins` = round(median(nFeature_PROT),0)
)) %>%
kable_styling(bootstrap_options = c("striped", "hover"))| donor | Number of cells | Median counts RNA | Median Number genes | Median Mitochondrial counts (Median %) | Median counts PROT | Number proteins |
|---|---|---|---|---|---|---|
| D2 | 522 | 272 | 112 | 0.73 | 7924 | 72 |
| D3 | 516 | 292 | 126 | 0.64 | 7942 | 72 |
Save dataset
Seurat object with filtered cells and normalized counts is stored in “output/seu.fix_norm.rds” (intracellular protein modality) and “output/seu.live_norm.rds”(RNA and surface protein modalities).
## Save Seurat objects
saveRDS(seu.RNA_combined.fix, file = "output/seu.fix_norm.rds")
saveRDS(seu.RNA_combined.live, file = "output/seu.live_norm.rds")
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19044)
Matrix products: default
locale:
[1] LC_COLLATE=English_Netherlands.1252 LC_CTYPE=English_Netherlands.1252
[3] LC_MONETARY=English_Netherlands.1252 LC_NUMERIC=C
[5] LC_TIME=English_Netherlands.1252
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] ggupset_0.3.0 RColorBrewer_1.1-2 clusterProfiler_3.18.1
[4] enrichplot_1.10.2 UCell_1.0.0 data.table_1.14.2
[7] scales_1.1.1 cowplot_1.1.1 ggthemes_4.2.4
[10] kableExtra_1.3.4 knitr_1.36 org.Hs.eg.db_3.12.0
[13] AnnotationDbi_1.52.0 IRanges_2.24.1 S4Vectors_0.28.1
[16] Biobase_2.50.0 BiocGenerics_0.36.1 forcats_0.5.1
[19] stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4
[22] readr_2.1.0 tidyr_1.1.4 tibble_3.1.5
[25] ggplot2_3.3.5 tidyverse_1.3.1 Matrix_1.3-4
[28] SeuratObject_4.0.2 Seurat_4.0.2 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] utf8_1.2.2 reticulate_1.22 tidyselect_1.1.1
[4] RSQLite_2.2.8 htmlwidgets_1.5.4 BiocParallel_1.24.1
[7] grid_4.0.3 Rtsne_0.15 scatterpie_0.1.7
[10] munsell_0.5.0 ragg_1.2.0 codetools_0.2-16
[13] ica_1.0-2 future_1.23.0 miniUI_0.1.1.1
[16] withr_2.5.0 colorspace_2.0-2 GOSemSim_2.16.1
[19] highr_0.9 rstudioapi_0.13 ROCR_1.0-11
[22] tensor_1.5 DOSE_3.16.0 listenv_0.8.0
[25] labeling_0.4.2 git2r_0.28.0 polyclip_1.10-0
[28] bit64_4.0.5 farver_2.1.0 downloader_0.4
[31] rprojroot_2.0.2 parallelly_1.29.0 vctrs_0.3.8
[34] generics_0.1.1 xfun_0.26 R6_2.5.1
[37] graphlayouts_0.7.2 fgsea_1.16.0 spatstat.utils_2.2-0
[40] cachem_1.0.6 assertthat_0.2.1 vroom_1.5.6
[43] promises_1.2.0.1 ggraph_2.0.5 gtable_0.3.0
[46] globals_0.14.0 goftest_1.2-2 tidygraph_1.2.0
[49] rlang_0.4.11 systemfonts_1.0.3 splines_4.0.3
[52] lazyeval_0.2.2 spatstat.geom_2.2-2 broom_0.7.10
[55] BiocManager_1.30.16 yaml_2.2.1 reshape2_1.4.4
[58] abind_1.4-5 modelr_0.1.8 backports_1.3.0
[61] httpuv_1.6.3 qvalue_2.22.0 tools_4.0.3
[64] ellipsis_0.3.2 spatstat.core_2.3-0 jquerylib_0.1.4
[67] ggridges_0.5.3 Rcpp_1.0.7 plyr_1.8.6
[70] rpart_4.1-15 deldir_1.0-2 pbapply_1.5-0
[73] viridis_0.6.2 zoo_1.8-9 haven_2.4.3
[76] ggrepel_0.9.1 cluster_2.1.0 fs_1.5.0
[79] magrittr_2.0.1 scattermore_0.7 DO.db_2.9
[82] lmtest_0.9-38 reprex_2.0.1 RANN_2.6.1
[85] whisker_0.4 fitdistrplus_1.1-6 matrixStats_0.61.0
[88] hms_1.1.1 patchwork_1.1.1 mime_0.12
[91] evaluate_0.14 xtable_1.8-4 readxl_1.3.1
[94] gridExtra_2.3 compiler_4.0.3 shadowtext_0.0.9
[97] KernSmooth_2.23-17 crayon_1.4.2 htmltools_0.5.2
[100] ggfun_0.0.4 mgcv_1.8-33 later_1.3.0
[103] tzdb_0.2.0 lubridate_1.8.0 DBI_1.1.1
[106] tweenr_1.0.2 dbplyr_2.1.1 MASS_7.3-53
[109] cli_3.6.0 igraph_1.2.6 pkgconfig_2.0.3
[112] rvcheck_0.2.1 plotly_4.10.0 spatstat.sparse_2.0-0
[115] xml2_1.3.2 svglite_2.0.0 bslib_0.3.1
[118] webshot_0.5.2 rvest_1.0.2 yulab.utils_0.0.4
[121] digest_0.6.28 sctransform_0.3.2 RcppAnnoy_0.0.19
[124] spatstat.data_2.1-0 fastmatch_1.1-3 rmarkdown_2.11
[127] cellranger_1.1.0 leiden_0.3.9 uwot_0.1.10
[130] shiny_1.7.1 lifecycle_1.0.1 nlme_3.1-149
[133] jsonlite_1.7.2 viridisLite_0.4.0 fansi_0.5.0
[136] pillar_1.6.4 lattice_0.20-41 fastmap_1.1.0
[139] httr_1.4.2 survival_3.2-7 GO.db_3.12.1
[142] glue_1.4.2 png_0.1-7 bit_4.0.4
[145] ggforce_0.3.3 stringi_1.7.5 sass_0.4.0
[148] blob_1.2.2 textshaping_0.3.6 memoise_2.0.1
[151] irlba_2.3.3 future.apply_1.8.1