cell freq stat
plot cell frequency of unsorted CITEseq data
Generate Figure 5A; Figure S2A, S2B, S2C
Take interested populations from statistical model test and plot together
set1 as days_since_onset, set2 as PC1class, set3 for heatmap
main_set1 = c("B_Mem_IgMneg", "B_CD71_gated",
"CD8_Mem_Activated","NK","Mono_Classical",
"Mono_Classical_CD163hi", "Mono_NonClassical")
sup_set1 = c("Plasmablast_gated", "PB_Plasmablasts_MHCIhi",
"CD4_Mem_CM", "CD4_Mem_CD69pos", "Tfh_gated", "Treg_gated",
"CD8_Mem_NKT", "CD8_Mem_EM.TE", "CD8_Naive_KLRG1pos ", "gammadeltaT",
"NK_CD56loCD16lo", "Mono", "cDC", "pDC", "Platelets")
main_set2 = c("CD4_Mem_CM", "CD8_Mem_CM.TM",
"gammadeltaT","MAIT","Mono_NonClassical", "pDC", "cDC", "Platelets")
sup_set2 = c( "B_Mem_CD11cpos", "B_CD71_gated", "Plasmablast_gated",
"PB_Plasmablasts_MHCIhi","CD4_Mem", "CD4_Naive",
"CD4_Mem_Activated.CD38.CD278hi", "Tfh_gated", "Treg_gated",
"CD8_Mem", "CD8_Mem_Activated", "gammadeltaT_CD158e1hi",
"Mono_Classical_IgPos", "Mono_Classical_CD163hi")
# read final list data in
final_cell_freq_UnSort_mtx <- read.csv("input/final_full_cell_freq_UnSort_mtx.20200818.csv",
header = TRUE, row.names = 1, check.names = FALSE)
colnames(final_cell_freq_UnSort_mtx) <- str_replace(colnames(final_cell_freq_UnSort_mtx),
pattern = "_to_parent", replacement = "")
final_cell_freq_UnSort_mtx$PC1class <- factor(final_cell_freq_UnSort_mtx$PC1class, levels = c("HC","PC1_low","PC1_high"))
# remove technical controls
final_cell_freq_UnSort_mtx <- final_cell_freq_UnSort_mtx %>% filter(Subject != "CHI014")set1 Fig5
final_cell_freq_UnSort_mtx_main_set1 <- select(final_cell_freq_UnSort_mtx, all_of(main_set1),
colnames(final_cell_freq_UnSort_mtx)[1:10])
final_cell_freq_UnSort_main_set1 <- final_cell_freq_UnSort_mtx_main_set1 %>%
melt(id = c("Var1", "Timepoint", "sample_id", "Batch", "severity",
"days_since_symptoms_onset","PC1","PC1class","Subject","severity_outcome"))
p <- plot5cat(final_cell_freq_UnSort_main_set1, "Fig5.main", "all.set1", width = 7.5, height = 5)
# plot timecourse here
p[[3]]
set2 Fig2S
final_cell_freq_UnSort_mtx_main_set2 <- select(final_cell_freq_UnSort_mtx, all_of(main_set2),
colnames(final_cell_freq_UnSort_mtx)[1:10])
final_cell_freq_UnSort_main_set2 <- final_cell_freq_UnSort_mtx_main_set2 %>%
melt(id = c("Var1", "Timepoint", "sample_id", "Batch", "severity",
"days_since_symptoms_onset","PC1","PC1class","Subject","severity_outcome"))
p <- plot5cat(final_cell_freq_UnSort_main_set2, "Fig2.main", "all.set2", width = 7, height = 5)
# plot PC1/DSM group here
p[[4]]
heatmap of cell subsets
# removed 3-"CD4_Mem_Activated.CD38.CD278hi", 34-"Tcell, 31-"DPT", 33 - "Granulocytes", 26 - "RBC" which are too small or batch specific
main_set3 = unique(c(main_set1, main_set2, "Mono_NonClassical", "B_Naive", "Mono_Classical",
"NK_CD16hi","B_Mem","gammadeltaT","NK_CD56loCD16lo","Treg",
"CD4_Mem","CD8_Mem","CD8_Naive","CD4_Naive","MAIT","pDC","cDC",
"TissueResMemT","Platelets","NK_CD56hiCD16lo","DNT",
"CD4", "CD8","CD19","NK","Mono","TCRVbeta13.1pos","Mono_Intermediate",
"B_CD71_gated", "Plasmablast_gated", "Tfh_gated",
"Treg_gated", "B_Mem_CD11cpos"))
fig2_cells <- select(final_cell_freq_UnSort_mtx, colnames(final_cell_freq_UnSort_mtx)[1:10],
intersect(main_set3, colnames(final_cell_freq_UnSort_mtx)))
fig2_cells_PC1 <- fig2_cells %>% filter(!is.na(PC1class), Timepoint %in% c("HC","T0"))
fig2_cells_PC1_mtx <- fig2_cells_PC1 %>%
mutate(PC1class = factor(PC1class, levels = c("HC", "PC1_low", "PC1_high"))) %>%
arrange(PC1class, PC1) %>%
select(-c("TCRVbeta13.1pos", "Treg"))
# change column names -- set HC
fig2_cells_PC1_mtx_hm <- t(fig2_cells_PC1_mtx[,c(11:ncol(fig2_cells_PC1_mtx))])
colnames(fig2_cells_PC1_mtx_hm) <- replace(as.character(fig2_cells_PC1_mtx$Subject),
fig2_cells_PC1_mtx$Timepoint == "HC" & fig2_cells_PC1_mtx$Subject != "CHI014", paste("HC", seq_along(1:13), sep = ""))
fig2_cells_PC1_mtx$colgroup = as.character(fig2_cells_PC1_mtx$PC1class)
fig2_cells_PC1_mtx$colgroup = factor(fig2_cells_PC1_mtx$colgroup, levels = c("HC", "PC1_low", "PC1_high"))
# use complexheatmap
severity.color <- c("Critical_Alive"="#374E55FF","Critical_Deceased"="#DF8F44FF","Moderate_Alive"="#00A1D5FF","Severe_Alive"="#B24745FF","HC_HC" = "#79AF97FF")
PC1.color <- colorRamp2(c(-3, 3.6), c("white", "#e97171"))
PC1class.color <- c("HC"="#00BA38", "PC1_low"="#619CFF", "PC1_high"="#F8766D")
ha = HeatmapAnnotation(
PC1class = fig2_cells_PC1_mtx$PC1class,
PC1 = fig2_cells_PC1_mtx$PC1,
severity_outcome = factor(fig2_cells_PC1_mtx$severity_outcome,
levels = c("HC_HC", "Moderate_Alive", "Severe_Alive", "Critical_Alive", "Critical_Deceased")),
col = list(PC1 = PC1.color,
PC1class = PC1class.color,
severity_outcome = severity.color)
)
# pdf("output/Fig2.celltypecor.cluster.pdf", width = 10, height = 6.5)
Heatmap(pheatmap:::scale_rows(fig2_cells_PC1_mtx_hm), name = "cell_freq_zscore",
show_column_names = TRUE, column_names_rot = 45,
top_annotation = ha, cluster_columns = FALSE,
column_split = fig2_cells_PC1_mtx$colgroup,
column_gap = unit(1.5, "mm"),
row_names_gp = gpar(fontsize = 10.5),
column_names_gp = gpar(fontsize = 8)
)
# dev.off()sI <- sessionInfo()
utils:::print.sessionInfo(sI[-c(10,11)])## R version 3.6.2 (2019-12-12)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Mojave 10.14.6
##
## Matrix products: default
##
## 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] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] circlize_0.4.8 ComplexHeatmap_2.2.0 reshape2_1.4.3
## [4] openxlsx_4.1.4 forcats_0.4.0 stringr_1.4.0
## [7] dplyr_0.8.4 purrr_0.3.3 readr_1.3.1
## [10] tidyr_1.0.2 tibble_3.0.3 ggplot2_3.3.2
## [13] tidyverse_1.3.0 plyr_1.8.5 matrixStats_0.55.0
## [16] Seurat_3.1.4
##
## loaded via a namespace (and not attached):
## [1] readxl_1.3.1 backports_1.1.5 sn_1.5-5
## [4] igraph_1.2.4.2 lazyeval_0.2.2 splines_3.6.2
## [7] listenv_0.8.0 TH.data_1.0-10 digest_0.6.25
## [10] htmltools_0.4.0 gdata_2.18.0 fansi_0.4.1
## [13] magrittr_1.5 cluster_2.1.0 ROCR_1.0-7
## [16] globals_0.12.5 modelr_0.1.6 RcppParallel_4.4.4
## [19] sandwich_2.5-1 colorspace_1.4-1 rvest_0.3.5
## [22] ggrepel_0.8.1 haven_2.2.0 xfun_0.12
## [25] crayon_1.3.4 jsonlite_1.6.1 survival_3.1-8
## [28] zoo_1.8-7 ape_5.3 glue_1.3.1
## [31] gtable_0.3.0 leiden_0.3.3 GetoptLong_0.1.8
## [34] shape_1.4.4 future.apply_1.4.0 BiocGenerics_0.32.0
## [37] scales_1.1.0 pheatmap_1.0.12 mvtnorm_1.1-0
## [40] DBI_1.1.0 bibtex_0.4.2.2 Rcpp_1.0.3
## [43] metap_1.3 plotrix_3.7-7 viridisLite_0.3.0
## [46] clue_0.3-57 reticulate_1.14 rsvd_1.0.3
## [49] stats4_3.6.2 tsne_0.1-3 htmlwidgets_1.5.1
## [52] httr_1.4.1 gplots_3.0.3 RColorBrewer_1.1-2
## [55] TFisher_0.2.0 ellipsis_0.3.0 ica_1.0-2
## [58] farver_2.0.3 pkgconfig_2.0.3 uwot_0.1.5
## [61] dbplyr_1.4.2 labeling_0.3 tidyselect_1.0.0
## [64] rlang_0.4.7 munsell_0.5.0 cellranger_1.1.0
## [67] tools_3.6.2 cli_2.0.2 generics_0.0.2
## [70] broom_0.7.0 ggridges_0.5.2 evaluate_0.14
## [73] yaml_2.2.1 npsurv_0.4-0 knitr_1.28
## [76] fs_1.3.1 fitdistrplus_1.0-14 zip_2.0.4
## [79] caTools_1.18.0 RANN_2.6.1 pbapply_1.4-2
## [82] future_1.16.0 nlme_3.1-144 xml2_1.2.2
## [85] compiler_3.6.2 rstudioapi_0.11 plotly_4.9.2
## [88] png_0.1-7 lsei_1.2-0 reprex_0.3.0
## [91] stringi_1.4.6 lattice_0.20-40 Matrix_1.2-18
## [94] multtest_2.42.0 vctrs_0.3.4 mutoss_0.1-12
## [97] pillar_1.4.3 lifecycle_0.2.0 Rdpack_0.11-1
## [100] lmtest_0.9-37 GlobalOptions_0.1.1 RcppAnnoy_0.0.15
## [103] data.table_1.12.8 cowplot_1.0.0 bitops_1.0-6
## [106] irlba_2.3.3 gbRd_0.4-11 patchwork_1.0.0
## [109] R6_2.4.1 KernSmooth_2.23-16 gridExtra_2.3
## [112] codetools_0.2-16 MASS_7.3-51.5 gtools_3.8.1
## [115] assertthat_0.2.1 rjson_0.2.20 withr_2.1.2
## [118] sctransform_0.2.1 mnormt_1.5-6 multcomp_1.4-12
## [121] mgcv_1.8-31 parallel_3.6.2 hms_0.5.3
## [124] rmarkdown_2.1 Rtsne_0.15 numDeriv_2016.8-1.1
## [127] Biobase_2.46.0 lubridate_1.7.4