Analysis of scRNA-seq & CITE-seq Data Combined
Post-process Macrophage Sub-clustering Results
Jovana Maksimovic
December 22, 2022
Last updated: 2022-12-22
Checks: 7 0
Knit directory:
paed-cf-cite-seq/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 4e2053e | Jovana Maksimovic | 2022-12-22 | wflow_publish("analysis/11_COMBO.postprocess_macrophages.Rmd") |
| html | fd0c5aa | Jovana Maksimovic | 2022-12-20 | Build site. |
| Rmd | 508dee7 | Jovana Maksimovic | 2022-12-20 | wflow_publish(c("analysis/postprocess_.Rmd")) |
| html | 2983a22 | Jovana Maksimovic | 2022-12-19 | Build site. |
| Rmd | 99078c4 | Jovana Maksimovic | 2022-12-19 | wflow_publish(c("analysis/11_COMBO.postprocess_macrophages.Rmd", |
| html | 16ace9e | Jovana Maksimovic | 2022-12-19 | Build site. |
| Rmd | e799f52 | Jovana Maksimovic | 2022-12-19 | wflow_publish(c("analysis/emptyDrops.Rmd", "analysis/postprocess*.Rmd", |
| html | 63f8ee8 | Jovana Maksimovic | 2022-12-15 | Build site. |
| Rmd | 916bafa | Jovana Maksimovic | 2022-12-15 | wflow_publish(c("analysis/.emptyDrops.Rmd", "analysis/postprocess_*.Rmd", |
| Rmd | f3b7b92 | Jovana Maksimovic | 2022-06-16 | Submission version |
| html | f3b7b92 | Jovana Maksimovic | 2022-06-16 | Submission version |
1 Load libraries
2 Load Data
out <- here("data/SCEs/07_COMBO.macrophages_clustered.SEU.rds")
seuInt <- readRDS(file = out)
seuIntAn object of class Seurat
33118 features across 30847 samples within 3 assays
Active assay: integrated (3000 features, 3000 variable features)
2 other assays present: RNA, SCT
2 dimensional reductions calculated: pca, umap3 Sub-cluster annotation
3.1 Load manual annotations
labels <- read_csv(here("data/macrophage_subcluster_annotations_21.12.22.csv"))
seuInt@meta.data %>%
dplyr::select(-Annotation, -Broad) %>%
left_join(labels %>%
mutate(Annotation = ifelse(is.na(Annotation),
"SUSPECT",
Annotation),
Broad = ifelse(is.na(Broad),
"SUSPECT",
Broad)) %>%
mutate(Cluster = as.factor(Cluster),
Annotation = as.factor(Annotation)),
by = c("integrated_snn_res.1" = "Cluster")) -> ann
ann %>% dplyr::pull(Annotation) -> seuInt$Annotation
ann %>% dplyr::pull(Broad) -> seuInt$Broad
seuInt$Annotation <- fct_drop(seuInt$Annotation)
seuInt$Broad <- fct_drop(seuInt$Broad)
seuIntAn object of class Seurat
33118 features across 30847 samples within 3 assays
Active assay: integrated (3000 features, 3000 variable features)
2 other assays present: RNA, SCT
2 dimensional reductions calculated: pca, umap3.2 Visualise annotations
options(ggrepel.max.overlaps = Inf)
DimPlot(seuInt, reduction = 'umap', label = TRUE, repel = TRUE,
label.size = 3, group.by = "integrated_snn_res.1") +
NoLegend() -> p1
DimPlot(seuInt, reduction = 'umap', label = TRUE, repel = TRUE,
label.size = 3, group.by = "Annotation") +
NoLegend() +
scale_color_paletteer_d("miscpalettes::pastel") -> p2
(p1 | p2) & theme(text = element_text(size = 8),
axis.text = element_text(size = 8))
f2a <- p23.2.1 No. cells per cluster
seuInt@meta.data %>%
ggplot(aes(x = Annotation, fill = Annotation)) +
geom_bar() +
geom_text(aes(label = ..count..), stat = "count",
vjust = -0.5, colour = "black", size = 2) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
NoLegend() +
scale_fill_paletteer_d("miscpalettes::pastel")
seuInt@meta.data %>%
count(Annotation) %>%
mutate(perc = round(n/sum(n)*100,1)) %>%
dplyr::rename(`Cell Label` = "Annotation",
`No. Cells` = n,
`% Cells` = perc) %>%
knitr::kable()| Cell Label | No. Cells | % Cells |
|---|---|---|
| alveolar macs | 13223 | 42.9 |
| macro-CCL | 867 | 2.8 |
| macro-cholesterol | 500 | 1.6 |
| macro-ifna/b | 4954 | 16.1 |
| macro-int | 3468 | 11.2 |
| macro-interstitial | 176 | 0.6 |
| macro-lipid | 2912 | 9.4 |
| macro-MT | 707 | 2.3 |
| macro-reg | 242 | 0.8 |
| macro-repair | 704 | 2.3 |
| macro-vesicle | 1012 | 3.3 |
| macro-viral | 756 | 2.5 |
| proliferating macrophages | 1326 | 4.3 |
3.3 Cepo cluster marker genes
cepoMarkers <- Cepo(seuInt[["RNA"]]@data,
seuInt$Annotation,
exprsPct = 0.1,
logfc = 1)
sapply(1:ncol(cepoMarkers$stats), function(i){
names(sort(cepoMarkers$stats[,i], decreasing = TRUE))[1:20]
}) -> dat
colnames(dat) <- colnames(cepoMarkers$stats)
dat %>% knitr::kable()| alveolar.macs | macro.CCL | macro.cholesterol | macro.ifna.b | macro.int | macro.interstitial | macro.lipid | macro.MT | macro.reg | macro.repair | macro.vesicle | macro.viral | proliferating.macrophages |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| GPD1 | CCL4 | MSMO1 | IFI27 | VCAN | CCL13 | MT2A | MT1X | ATF4 | CDKN1A | AZU1 | IFI44L | PCLAF |
| INHBA | TNFAIP6 | INSIG1 | IGF1 | FCN1 | MARCKS | RBP4 | MT2A | CXCR4 | MDM2 | HP | IFIT1 | TK1 |
| MME | SOD2 | IDI1 | GPD1 | CORO1A | FCGR2B | NUPR1 | MT1G | KLF6 | INHBA | PLAC8 | RSAD2 | MKI67 |
| ITIH5 | CCL20 | FDFT1 | INHBA | FPR3 | F13A1 | PLTP | MT1F | CYP27A1 | IL1RN | ITIH5 | MX1 | BIRC5 |
| AQP3 | CCL4L2 | INHBA | ITIH5 | TMEM176B | SLC40A1 | MT1E | MT1E | ARL4C | ABHD5 | IGF1 | IFIT2 | TYMS |
| MCEMP1 | MARCKS | CYP51A1 | MLPH | C15orf48 | STAB1 | CXCR4 | MT1M | MLPH | GPD1 | DEFB1 | NT5C3A | GGH |
| MLPH | TNIP3 | PCOLCE2 | PCOLCE2 | FGL2 | FOLR2 | CES1 | MT1H | RBP4 | AQP3 | ACKR3 | HERC5 | CENPM |
| PCOLCE2 | CCL3 | GPD1 | PHLDA3 | TMEM176A | RNASE1 | SCD | GPD1 | CES1 | MCEMP1 | MCEMP1 | IFIT3 | GTSE1 |
| LPL | MIR3945HG | RGCC | AQP3 | SOCS3 | TMEM176B | GCHFR | DEFB1 | NEAT1 | EVL | TCF7L2 | ISG15 | TOP2A |
| ACKR3 | TNFAIP2 | AQP3 | MCEMP1 | PMP22 | RNASE6 | A2M | AQP3 | CA2 | PHLDA3 | RETN | CXCL10 | CENPF |
| PHLDA3 | ICAM1 | ITIH5 | DEFB1 | ZFP36L1 | LGMN | MME | MLPH | EMB | FCN1 | INHBA | IFITM3 | ASPM |
| SVIL | CXCL8 | TUBA1A | FAM89A | PLA2G7 | FPR3 | ACO1 | CCND3 | PPP1R15A | TCF7L2 | MS4A6A | TNFSF10 | RRM2 |
| ABCG1 | TNFAIP3 | MCEMP1 | SVIL | PLEKHO1 | TMEM176A | MT1X | ITIH5 | MME | CA2 | FAM89A | IL1RN | CDK1 |
| FABP4 | CD83 | TCF7L2 | MME | EMP1 | GPR183 | MGST1 | HP | BCL2A1 | TREM2 | PCOLCE2 | GPD1 | UBE2C |
| CA2 | CCL23 | SVIL | CCND3 | FCGR2B | GAL3ST4 | CCL18 | MCEMP1 | ITIH5 | CDC42EP3 | FOLR3 | ITIH5 | PTTG1 |
| CCND3 | BCL2A1 | FAM89A | CES1 | IER3 | MAFB | ABCG1 | RETN | NAMPT | TGM2 | S100A13 | GBP1 | TPX2 |
| FAM89A | ACSL1 | PPARG | SERPING1 | RASSF2 | ZFP36L1 | CDC42EP3 | PCOLCE2 | FAM89A | CD9 | ACO1 | UBE2S | CCNB2 |
| EVL | C15orf48 | MLPH | QSOX1 | BASP1 | GAS6 | CCL23 | CES1 | PHLDA3 | PCNA | CCND3 | MIR3945HG | CDKN3 |
| TGM2 | CXCL5 | SCD | FABP4 | CD14 | CLEC10A | SERPING1 | RAC2 | ABHD5 | MME | SVIL | RETN | HMMR |
| QSOX1 | NAMPT | CKS1B | PPARG | CLEC10A | MAMDC2 | PDK4 | EVL | MAFB | RGCC | SPN | QSOX1 | NUCB2 |
3.3.1 Cepo marker gene dot plot
Genes duplicated between clusters are excluded.
DefaultAssay(seuInt) <- "RNA"
maxGenes <- 5
sigGenes <- lapply(1:ncol(dat), function(i){
dat[,i][1:maxGenes]
})
sig <- unlist(sigGenes)
geneCols <- c(rep(rep(c("blue","black"), each = maxGenes),
ceiling(ncol(dat)/2)))[1:length(sig)][!duplicated(sig)]
geneCols <- rep(paletteer_d("miscpalettes::pastel", ncol(dat)),
each = maxGenes)[1:length(sig)][!duplicated(sig)]
pal <- paletteer::paletteer_d("vapoRwave::cool")
DotPlot(seuInt,
features = sig[!duplicated(sig)],
group.by = "Annotation",
dot.scale = 2.5) +
FontSize(y.text = 10, x.text = 9) +
labs(y = element_blank(), x = element_blank()) +
theme(axis.text.x = element_text(color = geneCols,
angle = 90,
hjust = 1,
vjust = 0.5,
face = "bold"),
legend.text = element_text(size = 8),
legend.title = element_text(size = 10)) +
scale_color_gradient2(low = pal[1],
mid = pal[3],
high = pal[5]) -> f2c
f2c
3.4 Visualise cytokines of interest
markers <- read_csv(file = here("data",
"macrophage_subcluster_cytokines.csv"),
col_names = FALSE)
p <- DotPlot(seuInt,
features = markers$X1,
cols = c("grey", "red"),
dot.scale = 5,
assay = "RNA",
group.by = "Annotation") +
theme(axis.text.x = element_text(angle = 90,
hjust = 1,
vjust = 0.5,
size = 8),
axis.text.y = element_text(size = 8),
text = element_text(size = 8)) +
coord_flip() +
labs(y = "Label", x = "Cytokine")
p
| Version | Author | Date |
|---|---|---|
| fd0c5aa | Jovana Maksimovic | 2022-12-20 |
4 Load protein data
4.1 Add to Seurat object
seuAdt <- readRDS(here("data",
"SCEs",
"04_COMBO.clustered_annotated_adt_diet.SEU.rds"))
seuAdt <- subset(seuAdt, cells = colnames(seuInt))
all(colnames(seuAdt) == colnames(seuInt))[1] TRUEseuInt[["ADT.dsb"]] <- seuAdt[["ADT.dsb"]]
seuInt[["ADT.raw"]] <- seuAdt[["ADT.raw"]]
seuIntAn object of class Seurat
33440 features across 30847 samples within 5 assays
Active assay: RNA (15578 features, 0 variable features)
4 other assays present: SCT, integrated, ADT.dsb, ADT.raw
2 dimensional reductions calculated: pca, umaprm(seuAdt)
gc() used (Mb) gc trigger (Mb) max used (Mb)
Ncells 9805147 523.7 14699991 785.1 14699991 785.1
Vcells 1104347840 8425.6 2465556850 18810.8 2388884949 18225.84.2 Load protein annotations
prots <- read_csv(file = here("data",
"sample_sheets",
"TotalSeq-A_Universal_Cocktail_v1.0.csv")) %>%
dplyr::filter(grepl("^A0", id)) %>%
dplyr::filter(!grepl("[Ii]sotype", name)) 4.3 Visualise all ADTs
Normalised with DSB. C133_Neeland ADT data was transferred to CF_BAL_Pilot using reference mapping and transfer.
cbind(seuInt@meta.data,
as.data.frame(t(seuInt@assays$ADT.dsb@data))) %>%
dplyr::group_by(Annotation, experiment) %>%
dplyr::summarize_at(.vars = prots$id, .funs = median) %>%
pivot_longer(c(-Annotation, -experiment), names_to = "ADT",
values_to = "ADT Exp.") %>%
left_join(prots, by = c("ADT" = "id")) %>%
mutate(`Cell Label` = Annotation) %>%
dplyr::rename(Protein = name) |>
dplyr::filter(experiment == 2) |>
ungroup() -> dat
plot(density(dat$`ADT Exp.`))
topMax <- 8
abline(v = topMax, lty = 2, col = "grey")
dat |> heatmap(
.column = `Cell Label`,
.row = Protein,
.value = `ADT Exp.`,
scale = "none",
rect_gp = grid::gpar(col = "white", lwd = 1),
show_row_names = TRUE,
column_names_gp = grid::gpar(fontsize = 10),
column_title_gp = grid::gpar(fontsize = 12),
row_names_gp = grid::gpar(fontsize = 8),
row_title_gp = grid::gpar(fontsize = 12),
column_title_side = "top",
palette_value = circlize::colorRamp2(seq(-1, topMax, length.out = 256),
viridis::magma(256)),
heatmap_legend_param = list(direction = "vertical")) 
4.4 Visualise ADTs of interest
adt <- read_csv(file = here("data/Proteins_macs_22.04.22.csv"))
adt <- adt[!duplicated(adt$DNA_ID),]
dat |>
dplyr::inner_join(adt, by = c("ADT" = "DNA_ID")) |>
mutate(Protein = `Name for heatmap`) |>
heatmap(
.column = Protein,
.row = `Cell Label`,
.value = `ADT Exp.`,
scale = "none",
rect_gp = grid::gpar(col = "white", lwd = 1),
show_row_names = TRUE,
column_names_gp = grid::gpar(fontsize = 10),
column_title_gp = grid::gpar(fontsize = 12),
row_names_gp = grid::gpar(fontsize = 10),
row_title_gp = grid::gpar(fontsize = 12),
column_title_side = "bottom",
heatmap_legend_param = list(direction = "vertical"),
palette_value = circlize::colorRamp2(seq(-1, topMax, length.out = 256),
viridis::magma(256)),
column_title_side = "bottom") |>
add_tile(`Cell Label`, show_legend = FALSE,
show_annotation_name = FALSE,
palette = paletteer_d("miscpalettes::pastel",
length(levels(seuInt$Annotation)))) -> f2d
wrap_heatmap(f2d)
| Version | Author | Date |
|---|---|---|
| fd0c5aa | Jovana Maksimovic | 2022-12-20 |
5 Proportions analysis
5.1 Load clinical information
Import clinical characteristics and patient information and associate with genetic_donor IDs.
info <- read.csv(file = here("data/sample_sheets/Sample_information.csv"))
tab <- table(seuInt$HTO, seuInt$donor)
apply(tab, 2, function(x){
names(which(x == max(x)))
}) %>% data.frame %>%
dplyr::rename("HTO" = ".") %>%
rownames_to_column(var = "donor") %>%
inner_join(info, by = c("HTO" = "Sample")) %>%
mutate(Batch = factor(Batch)) -> info
info %>% knitr::kable()| donor | HTO | Participant | Sex | Age | Disease | Batch |
|---|---|---|---|---|---|---|
| A | A | B1_CF | M | 2.99 | CF | 1 |
| B | B | C1_CF | M | 2.99 | CF | 1 |
| C | C | A1_Ctrl | M | 3.00 | Ctrl | 1 |
| D | D | D1_CF | M | 3.03 | CF | 1 |
| donor_A | Human_HTO_8 | L2_CF | M | 5.95 | CF | 2 |
| donor_B | Human_HTO_1 | E2_CF | F | 5.99 | CF | 2 |
| donor_C | Human_HTO_4 | H2_CF | F | 5.89 | CF | 2 |
| donor_D | Human_HTO_6 | J2_CF | M | 5.05 | CF | 2 |
| donor_E | Human_HTO_3 | G2_CF | F | 4.91 | CF | 2 |
| donor_F | Human_HTO_5 | I2_CF | F | 5.93 | CF | 2 |
| donor_G | Human_HTO_2 | F2_CF | F | 6.02 | CF | 2 |
| donor_H | Human_HTO_7 | K2_CF | M | 4.92 | CF | 2 |
5.2 Sub-cluster proportions (Fine)
# Differences in cell type proportions
props <- getTransformedProps(clusters = seuInt$Annotation,
sample = seuInt$donor, transform="asin")
props$Proportions %>% knitr::kable()| A | B | C | D | donor_A | donor_B | donor_C | donor_D | donor_E | donor_F | donor_G | donor_H | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| alveolar macs | 0.5037393 | 0.4351317 | 0.4106037 | 0.4151553 | 0.3202811 | 0.3489796 | 0.3997243 | 0.5236613 | 0.4640499 | 0.5283702 | 0.2993445 | 0.3968416 |
| macro-CCL | 0.0320513 | 0.0937701 | 0.0179954 | 0.0141994 | 0.0210843 | 0.0448980 | 0.0310131 | 0.0174346 | 0.0135730 | 0.0233400 | 0.0480699 | 0.0112073 |
| macro-cholesterol | 0.0080128 | 0.0077071 | 0.0150929 | 0.0226346 | 0.0010040 | 0.0051020 | 0.0227429 | 0.0255293 | 0.0139398 | 0.0197183 | 0.0007283 | 0.0275089 |
| macro-ifna/b | 0.0667735 | 0.0751445 | 0.2380031 | 0.2006186 | 0.0933735 | 0.0969388 | 0.2074431 | 0.1270237 | 0.1221570 | 0.0949698 | 0.1798980 | 0.2185430 |
| macro-int | 0.1452991 | 0.0918433 | 0.0998452 | 0.0856179 | 0.3463855 | 0.1938776 | 0.0875258 | 0.0853051 | 0.0917095 | 0.0905433 | 0.2694829 | 0.0718288 |
| macro-interstitial | 0.0000000 | 0.0025690 | 0.0075464 | 0.0042176 | 0.0040161 | 0.0040816 | 0.0020675 | 0.0093400 | 0.0051357 | 0.0040241 | 0.0284050 | 0.0050942 |
| macro-lipid | 0.0625000 | 0.1361593 | 0.0381192 | 0.1054407 | 0.1194779 | 0.2051020 | 0.0654721 | 0.0653798 | 0.1338958 | 0.1078471 | 0.0691916 | 0.0896587 |
| macro-MT | 0.0192308 | 0.0147720 | 0.0317337 | 0.0336004 | 0.0130522 | 0.0040816 | 0.0151620 | 0.0080946 | 0.0179751 | 0.0185111 | 0.0058267 | 0.0341314 |
| macro-reg | 0.0085470 | 0.0077071 | 0.0075464 | 0.0059047 | 0.0130522 | 0.0071429 | 0.0027567 | 0.0043587 | 0.0066031 | 0.0020121 | 0.0029133 | 0.0320937 |
| macro-repair | 0.0523504 | 0.0324342 | 0.0241873 | 0.0140588 | 0.0060241 | 0.0265306 | 0.0282564 | 0.0236613 | 0.0256787 | 0.0148893 | 0.0233066 | 0.0152827 |
| macro-vesicle | 0.0528846 | 0.0163776 | 0.0501161 | 0.0477998 | 0.0100402 | 0.0142857 | 0.0268780 | 0.0354919 | 0.0212766 | 0.0221328 | 0.0058267 | 0.0112073 |
| macro-viral | 0.0373932 | 0.0327553 | 0.0178019 | 0.0195417 | 0.0170683 | 0.0224490 | 0.0461751 | 0.0298879 | 0.0234776 | 0.0233400 | 0.0320466 | 0.0168110 |
| proliferating macrophages | 0.0112179 | 0.0536288 | 0.0414087 | 0.0312105 | 0.0351406 | 0.0265306 | 0.0647829 | 0.0448319 | 0.0605282 | 0.0503018 | 0.0349599 | 0.0697911 |
5.2.1 Cell proportions by donor
props$Proportions %>%
data.frame %>%
inner_join(info, by = c("sample" = "donor")) %>%
ggplot(aes(x = Participant, y = Freq, fill = clusters)) +
geom_bar(stat = "identity") +
theme_classic() +
theme(axis.text.x = element_text(angle = 90,
vjust = 0.5,
hjust = 1),
legend.text = element_text(size = 8)) +
labs( y = "Proportion", fill = "Cell Label") +
scale_fill_paletteer_d("miscpalettes::pastel") -> f2b
f2b
5.2.2 Cell proportions by donor, stratified by cell type
props$Proportions %>%
data.frame %>%
inner_join(info, by = c("sample" = "donor")) %>%
ggplot(aes(x = Participant, y = Freq, fill = clusters)) +
geom_bar(stat = "identity") +
facet_wrap(~clusters, scales = "free_y") +
theme_classic() +
NoLegend() +
theme(axis.text.x = element_text(angle = 90,
vjust = 0.5,
hjust = 1,
size = 8),
strip.text = element_text(size = 10),
axis.text = element_text(size = 8)) +
labs( y = "Proportion", fill = "Cell label") +
scale_fill_paletteer_d("miscpalettes::pastel")
5.2.3 Cell proportions of control sample relative to CF samples
props$Proportions %>%
data.frame %>%
inner_join(info, by = c("sample" = "donor")) -> dat
ggplot(dat[dat$Participant != "A1_Ctrl",],
aes(x = clusters, y = Freq, fill = clusters)) +
geom_boxplot() +
geom_point(data = dat[dat$Participant == "A1_Ctrl", ],
aes(x = clusters, y = Freq),
color = "red") +
theme_classic() +
theme(axis.text.x = element_text(angle = 90,
vjust = 0.5,
hjust = 1),
legend.text = element_text(size = 8)) +
labs( y = "Proportion", x = "Cell Label") +
scale_fill_paletteer_d("miscpalettes::pastel") +
NoLegend() -> f
f
6 Save data
out <- here(glue("data/SCEs/07_COMBO.clean_macrophages_diet.SEU.rds"))
if(!file.exists(out)){
DefaultAssay(seuInt) <- "RNA"
saveRDS(DietSeurat(seuInt,
assays = c("RNA", "ADT.dsb", "ADT.raw"),
dimreducs = NULL,
graphs = NULL), out)
}7 Panel figures
layout = "AAAABB
AAAABB
AAAABB
CCCCCC
CCCCCC
DDDDDD
DDDDDD
DDDDDD"
((f2a + ggtitle("")) +
f2b +
f2c +
wrap_heatmap(f2d)) +
plot_layout(design = layout) +
plot_annotation(tag_levels = "A") &
theme(plot.tag = element_text(size = 14, face = "bold"))
| Version | Author | Date |
|---|---|---|
| fd0c5aa | Jovana Maksimovic | 2022-12-20 |
8 Session info
sessioninfo::session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.1.0 (2021-05-18)
os CentOS Linux 7 (Core)
system x86_64, linux-gnu
ui X11
language (EN)
collate en_AU.UTF-8
ctype en_AU.UTF-8
tz Australia/Melbourne
date 2022-12-22
pandoc 2.17.1.1 @ /usr/lib/rstudio-server/bin/quarto/bin/ (via rmarkdown)
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P zlibbioc 1.40.0 2021-10-26 [?] Bioconductor
P zoo 1.8-9 2021-03-09 [?] CRAN (R 4.1.0)
[1] /oshlack_lab/jovana.maksimovic/projects/MCRI/melanie.neeland/paed-cf-cite-seq/renv/library/R-4.1/x86_64-pc-linux-gnu
[2] /config/binaries/R/4.1.0/lib64/R/library
P ── Loaded and on-disk path mismatch.
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sessionInfo()R version 4.1.0 (2021-05-18)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS: /config/binaries/R/4.1.0/lib64/R/lib/libRblas.so
LAPACK: /config/binaries/R/4.1.0/lib64/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_AU.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_AU.UTF-8 LC_COLLATE=en_AU.UTF-8
[5] LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8
[7] LC_PAPER=en_AU.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats4 stats graphics grDevices datasets utils methods
[8] base
other attached packages:
[1] limma_3.50.0 Cepo_1.0.0 GSEABase_1.56.0
[4] graph_1.72.0 annotate_1.72.0 XML_3.99-0.8
[7] AnnotationDbi_1.56.2 IRanges_2.28.0 S4Vectors_0.32.3
[10] Biobase_2.54.0 BiocGenerics_0.40.0 speckle_0.0.3
[13] tidyHeatmap_1.7.0 paletteer_1.4.0 patchwork_1.1.1
[16] SeuratObject_4.0.4 Seurat_4.0.6 glue_1.6.0
[19] here_1.0.1 forcats_0.5.1 stringr_1.4.0
[22] dplyr_1.0.7 purrr_0.3.4 readr_2.1.1
[25] tidyr_1.1.4 tibble_3.1.6 ggplot2_3.3.5
[28] tidyverse_1.3.1 BiocStyle_2.22.0 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] scattermore_0.7 bit64_4.0.5
[3] knitr_1.37 irlba_2.3.5
[5] DelayedArray_0.20.0 data.table_1.14.2
[7] rpart_4.1-15 KEGGREST_1.34.0
[9] RCurl_1.98-1.6 doParallel_1.0.16
[11] generics_0.1.1 org.Mm.eg.db_3.14.0
[13] callr_3.7.0 cowplot_1.1.1
[15] RSQLite_2.2.9 RANN_2.6.1
[17] future_1.23.0 bit_4.0.4
[19] tzdb_0.2.0 spatstat.data_2.1-2
[21] xml2_1.3.3 lubridate_1.8.0
[23] httpuv_1.6.5 SummarizedExperiment_1.24.0
[25] assertthat_0.2.1 viridis_0.6.2
[27] xfun_0.29 hms_1.1.1
[29] jquerylib_0.1.4 evaluate_0.14
[31] promises_1.2.0.1 fansi_1.0.0
[33] dendextend_1.15.2 dbplyr_2.1.1
[35] readxl_1.3.1 igraph_1.2.11
[37] DBI_1.1.2 htmlwidgets_1.5.4
[39] spatstat.geom_2.3-1 ellipsis_0.3.2
[41] backports_1.4.1 bookdown_0.24
[43] prismatic_1.1.0 deldir_1.0-6
[45] sparseMatrixStats_1.6.0 MatrixGenerics_1.6.0
[47] vctrs_0.3.8 SingleCellExperiment_1.16.0
[49] ROCR_1.0-11 abind_1.4-5
[51] cachem_1.0.6 withr_2.4.3
[53] vroom_1.5.7 sctransform_0.3.3
[55] goftest_1.2-3 cluster_2.1.2
[57] lazyeval_0.2.2 crayon_1.4.2
[59] labeling_0.4.2 edgeR_3.36.0
[61] pkgconfig_2.0.3 GenomeInfoDb_1.30.1
[63] nlme_3.1-153 rlang_0.4.12
[65] globals_0.14.0 lifecycle_1.0.1
[67] miniUI_0.1.1.1 modelr_0.1.8
[69] cellranger_1.1.0 rprojroot_2.0.2
[71] polyclip_1.10-0 matrixStats_0.61.0
[73] lmtest_0.9-39 Matrix_1.4-0
[75] Rhdf5lib_1.16.0 zoo_1.8-9
[77] reprex_2.0.1 whisker_0.4
[79] ggridges_0.5.3 GlobalOptions_0.1.2
[81] processx_3.5.2 png_0.1-7
[83] viridisLite_0.4.0 rjson_0.2.21
[85] bitops_1.0-7 getPass_0.2-2
[87] KernSmooth_2.23-20 rhdf5filters_1.6.0
[89] Biostrings_2.62.0 blob_1.2.2
[91] DelayedMatrixStats_1.16.0 shape_1.4.6
[93] parallelly_1.30.0 beachmat_2.10.0
[95] scales_1.1.1 memoise_2.0.1
[97] magrittr_2.0.1 plyr_1.8.6
[99] ica_1.0-2 zlibbioc_1.40.0
[101] compiler_4.1.0 RColorBrewer_1.1-2
[103] clue_0.3-60 fitdistrplus_1.1-6
[105] cli_3.1.0 XVector_0.34.0
[107] listenv_0.8.0 pbapply_1.5-0
[109] ps_1.6.0 MASS_7.3-53.1
[111] mgcv_1.8-38 tidyselect_1.1.1
[113] stringi_1.7.6 highr_0.9
[115] yaml_2.2.1 locfit_1.5-9.4
[117] ggrepel_0.9.1 grid_4.1.0
[119] sass_0.4.0 tools_4.1.0
[121] future.apply_1.8.1 parallel_4.1.0
[123] circlize_0.4.13 rstudioapi_0.13
[125] foreach_1.5.1 git2r_0.29.0
[127] gridExtra_2.3 farver_2.1.0
[129] Rtsne_0.15 digest_0.6.29
[131] BiocManager_1.30.16 shiny_1.7.1
[133] Rcpp_1.0.7 GenomicRanges_1.46.1
[135] broom_0.7.11 scuttle_1.4.0
[137] later_1.3.0 RcppAnnoy_0.0.19
[139] org.Hs.eg.db_3.14.0 httr_1.4.2
[141] ComplexHeatmap_2.10.0 colorspace_2.0-2
[143] rvest_1.0.2 fs_1.5.2
[145] tensor_1.5 reticulate_1.22
[147] splines_4.1.0 uwot_0.1.11
[149] rematch2_2.1.2 spatstat.utils_2.3-0
[151] renv_0.15.0-14 sessioninfo_1.2.2
[153] plotly_4.10.0 xtable_1.8-4
[155] jsonlite_1.7.2 R6_2.5.1
[157] pillar_1.6.4 htmltools_0.5.2
[159] mime_0.12 fastmap_1.1.0
[161] BiocParallel_1.28.3 codetools_0.2-18
[163] utf8_1.2.2 lattice_0.20-45
[165] bslib_0.3.1 spatstat.sparse_2.1-0
[167] leiden_0.3.9 survival_3.2-13
[169] rmarkdown_2.11 munsell_0.5.0
[171] GetoptLong_1.0.5 rhdf5_2.38.0
[173] GenomeInfoDbData_1.2.7 iterators_1.0.13
[175] HDF5Array_1.22.1 haven_2.4.3
[177] reshape2_1.4.4 gtable_0.3.0
[179] spatstat.core_2.3-2