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Last updated: 2021-12-17
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| File | Version | Author | Date | Message |
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| Rmd | 8afc486 | Saket Choudhary | 2021-12-17 | workflowr::wflow_publish("analysis/*") |
suppressMessages({
library(Seurat)
library(ggpubr)
library(ggridges)
library(patchwork)
library(dplyr)
library(ggupset)
library(tidyverse)
library(ggplot2)
library(xtable)
library(ComplexUpset)
library(SeuratData)
})Warning in if (is.na(desc)) {: the condition has length > 1 and only the first
element will be used
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element will be used
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element will be used
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element will be used
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element will be used
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element will be used
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element will be used
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element will be used
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element will be useddata("bmcite")
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
theme_set(theme_pubr())all_fdr_tpr_df_melt1 <- readRDS(here::here("output/muscat_simulation/results/all_fdr_tpr_df_melt.rds"))
all_fdr_tpr_df_melt2 <- readRDS(here::here("output/muscat_simulation/results/all_fdr_tpr_df_melt_DESeq2.rds"))
all_fdr_tpr_df_melt <- rbind(all_fdr_tpr_df_melt1, all_fdr_tpr_df_melt2)
all_fdr_tpr_df_melt$method <- as.character(all_fdr_tpr_df_melt$method)
# For DESeq2 - input is always raw counts
# For MAST - input is LogNorm or Scran or log(corrected counts) or pearson residuals
# For wilcoxon - input is either LogNorm or Scran or log(corrected counts) or pearson residual
all_fdr_tpr_df_melt <- all_fdr_tpr_df_melt %>% filter(test.use != "DESeq2" && method != "scran")
all_fdr_tpr_df_melt[all_fdr_tpr_df_melt$test.use == "negbinom", "test.use"] <- "NB"
all_fdr_tpr_df_melt[all_fdr_tpr_df_melt$test.use == "wilcox", "test.use"] <- "Wilcoxon"
all_fdr_tpr_df_melt[(all_fdr_tpr_df_melt$test.use == "DESeq2") & (all_fdr_tpr_df_melt$method == "lognorm"), "method"] <- "Raw counts"
all_fdr_tpr_df_melt[(all_fdr_tpr_df_melt$test.use == "NB") & (all_fdr_tpr_df_melt$method == "lognorm"), "method"] <- "Raw counts"
all_fdr_tpr_df_melt$method[all_fdr_tpr_df_melt$method == "SCT v1"] <- "Pearson residuals (SCT v1)"
all_fdr_tpr_df_melt$method[all_fdr_tpr_df_melt$method == "SCT v2"] <- "Corrected counts (SCT v2)"
all_fdr_tpr_df_melt$method[all_fdr_tpr_df_melt$method == "scran"] <- "Scran"
all_fdr_tpr_df_melt$method[all_fdr_tpr_df_melt$method == "lognorm"] <- "LogNorm"
all_fdr_tpr_df_melt <- all_fdr_tpr_df_melt[(all_fdr_tpr_df_melt$method != "Pearson residuals (SCT v1)") | (all_fdr_tpr_df_melt$test.use != "DESeq2"), ]
all_fdr_tpr_df_melt <- all_fdr_tpr_df_melt[(all_fdr_tpr_df_melt$method != "Pearson residuals (SCT v1)") | (all_fdr_tpr_df_melt$test.use != "NB"), ]
all_fdr_tpr_df_melt <- all_fdr_tpr_df_melt[all_fdr_tpr_df_melt$test.use!="NB",]
all_fdr_tpr_df_melt$method <- factor(all_fdr_tpr_df_melt$method, levels = c("LogNorm", "Scran", "Pearson residuals (SCT v1)", "Raw counts", "Corrected counts (SCT v2)"))
labels <- c("LogNorm", "Scran", "Pearson residuals (SCT v1)", "Raw counts", "Corrected counts (SCT v2)")
names(labels) <- c("LogNorm", "Scran", "Pearson residuals (SCT v1)", "Raw counts", "Corrected counts (SCT v2)")
p3 <- ggplot(all_fdr_tpr_df_melt[all_fdr_tpr_df_melt$de_percent > 0.01, ], aes(FDR, TPR, color = method)) +
geom_point() +
geom_line() +
geom_vline(xintercept = c(0.01, 0.05, 0.1), linetype = "dashed") +
xlim(0, 1) +
scale_x_continuous(trans = "log", breaks = c(0.01, 0.05, 0.1, 0.5, 1), guide = guide_axis(angle = 50)) +
facet_grid(test.use ~ de_percent) +
theme_pubr() +
theme(
panel.grid.minor = element_line(size = (0.2), colour = "grey")
) +
scale_color_brewer(type = "qual", palette = "Dark2", name = "", labels = labels) +
theme(axis.text.x = element_text(size = rel(0.7), angle = 45))
p3
ggsave(here::here("output/figures/SuppFigure_DE_benchmarking_muscat_count_methods.pdf"), width = 9, height = 5)hek_markers <- readRDS(here::here("output/simulation_HEK_QuartzSeq2_Dropseq_downsampling/HEK_downsampling_DE_sig.rds"))
hek_markers[hek_markers$test.use == "negbinom", "test"] <- "NB"
hek_markers[hek_markers$test.use == "wilcox", "test"] <- "Wilcoxon"
hek_markers[(hek_markers$test == "DESeq2") & (hek_markers$method == "lognorm"), "method"] <- "Raw counts"
hek_markers$method[hek_markers$method == "SCT v1"] <- "Pearson residuals (SCT v1)"
hek_markers$method[hek_markers$method == "SCT v2"] <- "Corrected counts (SCT v2)"
hek_markers$method[hek_markers$method == "scran"] <- "Scran"
hek_markers$method[hek_markers$method == "lognorm"] <- "LogNorm"
hek_markers$method <- factor(hek_markers$method, levels = c("LogNorm", "Scran", "Pearson residuals (SCT v1)", "Raw counts", "Corrected counts (SCT v2)"))
labels <- c("LogNorm", "Scran", "Pearson residuals (SCT v1)", "Raw counts", "Corrected counts (SCT v2)")
names(labels) <- c("LogNorm", "Scran", "Pearson residuals (SCT v1)", "Raw counts", "Corrected counts (SCT v2)")
hek_markers_summary <- hek_markers %>%
group_by(method, test) %>%
summarise(count = n()) %>%
ungroup() %>%
as.data.frame()
# remove PR on DESeq2
hek_markers_summary <- hek_markers_summary[(hek_markers_summary$method != "Pearson residuals (SCT v1)") | (hek_markers_summary$test != "DESeq2"), ]
hek_markers_summary <- hek_markers_summary[(hek_markers_summary$method != "Scran") | (hek_markers_summary$test != "DESeq2"), ]
hek_markers_summary <- hek_markers_summary[(hek_markers_summary$method != "Pearson residuals (SCT v1)") | (hek_markers_summary$test != "negbinom"), ]
hek_markers_summary[(hek_markers_summary$method == "LogNorm") & (hek_markers_summary$test == "DESeq2"), "method"] <- "Raw counts"
hek_markers_summary[(hek_markers_summary$method == "LogNorm") & (hek_markers_summary$test == "negbinom"), "method"] <- "Raw counts"
hek_markers_summary[(hek_markers_summary$test == "negbinom"), "test"] <- "NB"
hek_markers_summary[(hek_markers_summary$test == "wilcox"), "test"] <- "Wilcoxon"
# Rename negbinom and DEseq2 on log norm
hek_markers_summary <- hek_markers_summary[hek_markers_summary$test!="NB",]
hek_markers_summary$method <- factor(as.character(hek_markers_summary$method),
levels = c("LogNorm", "Scran", "Pearson residuals (SCT v1)", "Raw counts", "Corrected counts (SCT v2)")
)
p4 <- ggplot(hek_markers_summary, aes(method, y = count, fill = method)) +
geom_bar(width = 0.5, stat = "identity") +
facet_wrap(~test, scales = "free_x") +
# scale_fill_brewer(type = "qual", palette = "Dark2", name = "") +
scale_fill_brewer(type = "qual", palette = "Dark2", name = "", labels = labels) +
#scale_fill_manual(values = c("#D95F02", "#7570B3", "#E7298A", "#66A61E"), name = "") + #"#1B9E77",
ylab("Number of DE genes") +
xlab("") +
scale_x_discrete(guide = guide_axis(angle = 30)) +
scale_y_continuous(breaks = c(1000, 2500, 5000, 10000, 12500, 15000)) +
NoLegend()
p4
ggsave(here::here("output/figures/SuppFigure_DE_benchmarking_HEK_count_methods.pdf"), width = 9, height = 4)nk_markers <- readRDS(here::here("output/simulation_NK_downsampling/NK_downsampling_DE_multtest_sig.rds"))
nk_markers[nk_markers$test == "negbinom", "test"] <- "NB"
nk_markers[nk_markers$test == "wilcox", "test"] <- "Wilcoxon"
nk_markers[(nk_markers$test == "DESeq2") & (nk_markers$method == "lognorm"), "method"] <- "Raw counts"
nk_markers$method[nk_markers$method == "SCT v1"] <- "Pearson residuals (SCT v1)"
nk_markers$method[nk_markers$method == "SCT v2"] <- "Corrected counts (SCT v2)"
nk_markers$method[nk_markers$method == "scran"] <- "Scran"
nk_markers$method[nk_markers$method == "lognorm"] <- "LogNorm"
nk_markers$method <- factor(nk_markers$method, levels = c("LogNorm", "Scran", "Pearson residuals (SCT v1)", "Raw counts", "Corrected counts (SCT v2)"))
labels <- c("LogNorm", "Scran", "Pearson residuals (SCT v1)", "Raw counts", "Corrected counts (SCT v2)")
names(labels) <- c("LogNorm", "Scran", "Pearson residuals (SCT v1)", "Raw counts", "Corrected counts (SCT v2)")
nk_markers_summary <- nk_markers %>%
group_by(method, test) %>%
summarise(count = n()) %>%
ungroup() %>%
as.data.frame()
# remove PR on DESeq2
nk_markers_summary <- nk_markers_summary[(nk_markers_summary$method != "Pearson residuals (SCT v1)") | (nk_markers_summary$test != "DESeq2"), ]
nk_markers_summary <- nk_markers_summary[(nk_markers_summary$method != "Scran") | (nk_markers_summary$test != "DESeq2"), ]
nk_markers_summary <- nk_markers_summary[(nk_markers_summary$method != "Pearson residuals (SCT v1)") | (nk_markers_summary$test != "negbinom"), ]
nk_markers_summary <- nk_markers_summary[(nk_markers_summary$method != "Scran") | (nk_markers_summary$test != "negbinom"), ]
nk_markers_summary[(nk_markers_summary$method == "LogNorm") & (nk_markers_summary$test == "DESeq2"), "method"] <- "Raw counts"
nk_markers_summary[(nk_markers_summary$method == "LogNorm") & (nk_markers_summary$test == "negbinom"), "method"] <- "Raw counts"
nk_markers_summary[(nk_markers_summary$test == "negbinom"), "test"] <- "NB"
nk_markers_summary[(nk_markers_summary$test == "wilcox"), "test"] <- "Wilcoxon"
# Rename negbinom and DEseq2 on log norm
nk_markers_summary <- nk_markers_summary[nk_markers_summary$test!="NB",]
nk_markers_empty <- data.frame(expand.grid(labels, c("DESeq2", "MAST", "Wilcoxon")))
colnames(nk_markers_empty) <- c("method", "test")
nk_markers_empty$count <- 0
nk_markers_summary <- rbind(nk_markers_summary, nk_markers_empty)
nk_markers_summary <- nk_markers_summary %>% group_by(method, test) %>% summarise(count = sum(count))
nk_markers_summary <- nk_markers_summary[!((nk_markers_summary$method == "LogNorm") & (nk_markers_summary$test == "DESeq2")), ]
nk_markers_summary <- nk_markers_summary[!((nk_markers_summary$method == "Pearson residuals (SCT v1)") & (nk_markers_summary$test == "DESeq2")), ]
nk_markers_summary <- nk_markers_summary[!((nk_markers_summary$method == "Scran") & (nk_markers_summary$test == "DESeq2")), ]
nk_markers_summary <- nk_markers_summary[!((nk_markers_summary$method == "LogNorm") & (nk_markers_summary$test == "NB")), ]
nk_markers_summary <- nk_markers_summary[!((nk_markers_summary$method == "Pearson residuals (SCT v1)") & (nk_markers_summary$test == "NB")), ]
nk_markers_summary <- nk_markers_summary[!((nk_markers_summary$method == "Scran") & (nk_markers_summary$test == "NB")), ]
nk_markers_summary <- nk_markers_summary[!((nk_markers_summary$method == "Raw counts") & (nk_markers_summary$test == "MAST")), ]
nk_markers_summary <- nk_markers_summary[!((nk_markers_summary$method == "Raw counts") & (nk_markers_summary$test == "Wilcoxon")), ]
nk_markers_summary$method <- factor(as.character(nk_markers_summary$method),
levels = c("LogNorm", "Scran", "Pearson residuals (SCT v1)", "Raw counts", "Corrected counts (SCT v2)")
)
p4 <- ggplot(nk_markers_summary, aes(method, y = count, fill = method)) +
geom_bar(width = 0.5, stat = "identity") +
facet_wrap(~test, scales = "free") +
# scale_fill_brewer(type = "qual", palette = "Dark2", name = "") +
scale_fill_brewer(type = "qual", palette = "Dark2", name = "", labels = labels) +
#scale_fill_manual(values = c("#D95F02", "#7570B3", "#E7298A", "#66A61E"), name = "") + #"#1B9E77",
ylab("Number of DE genes") +
xlab("") +
scale_x_discrete(guide = guide_axis(angle = 30)) +
NoLegend()
p4
ggsave(here::here("output/figures/SuppFigure_DE_benchmarking_NK_count_methods.pdf"), width = 9, height = 4)
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.3 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] thp1.eccite.SeuratData_3.1.5 pbmcsca.SeuratData_3.0.0
[3] pbmc3k.SeuratData_3.1.4 panc8.SeuratData_3.0.2
[5] hcabm40k.SeuratData_3.0.0 bmcite.SeuratData_0.3.0
[7] SeuratData_0.2.1 ComplexUpset_1.3.3
[9] xtable_1.8-4 forcats_0.5.1
[11] stringr_1.4.0 purrr_0.3.4
[13] readr_2.1.1 tidyr_1.1.4
[15] tibble_3.1.6 tidyverse_1.3.1
[17] ggupset_0.3.0 dplyr_1.0.7
[19] patchwork_1.1.1 ggridges_0.5.3
[21] ggpubr_0.4.0 ggplot2_3.3.5
[23] SeuratObject_4.0.4 Seurat_4.0.5
[25] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.4.1 plyr_1.8.6
[4] igraph_1.2.9 lazyeval_0.2.2 splines_4.1.2
[7] listenv_0.8.0 scattermore_0.7 digest_0.6.29
[10] htmltools_0.5.2 fansi_0.5.0 magrittr_2.0.1
[13] tensor_1.5 cluster_2.1.2 ROCR_1.0-11
[16] tzdb_0.2.0 globals_0.14.0 modelr_0.1.8
[19] matrixStats_0.61.0 spatstat.sparse_2.0-0 colorspace_2.0-2
[22] rappdirs_0.3.3 rvest_1.0.2 ggrepel_0.9.1
[25] haven_2.4.3 xfun_0.28 crayon_1.4.2
[28] jsonlite_1.7.2 spatstat.data_2.1-0 survival_3.2-13
[31] zoo_1.8-9 glue_1.5.1 polyclip_1.10-0
[34] gtable_0.3.0 leiden_0.3.9 car_3.0-12
[37] future.apply_1.8.1 abind_1.4-5 scales_1.1.1
[40] DBI_1.1.1 rstatix_0.7.0 miniUI_0.1.1.1
[43] Rcpp_1.0.7 viridisLite_0.4.0 reticulate_1.22
[46] spatstat.core_2.3-2 htmlwidgets_1.5.4 httr_1.4.2
[49] RColorBrewer_1.1-2 ellipsis_0.3.2 ica_1.0-2
[52] farver_2.1.0 pkgconfig_2.0.3 sass_0.4.0
[55] uwot_0.1.11 dbplyr_2.1.1 deldir_1.0-6
[58] here_1.0.1 utf8_1.2.2 labeling_0.4.2
[61] tidyselect_1.1.1 rlang_0.4.12 reshape2_1.4.4
[64] later_1.3.0 cellranger_1.1.0 munsell_0.5.0
[67] tools_4.1.2 cli_3.1.0 generics_0.1.1
[70] broom_0.7.10 evaluate_0.14 fastmap_1.1.0
[73] yaml_2.2.1 goftest_1.2-3 knitr_1.36
[76] fs_1.5.2 fitdistrplus_1.1-6 RANN_2.6.1
[79] pbapply_1.5-0 future_1.23.0 nlme_3.1-152
[82] whisker_0.4 mime_0.12 xml2_1.3.3
[85] rstudioapi_0.13 compiler_4.1.2 plotly_4.10.0
[88] png_0.1-7 ggsignif_0.6.3 spatstat.utils_2.3-0
[91] reprex_2.0.1 bslib_0.3.1 stringi_1.7.6
[94] highr_0.9 lattice_0.20-45 Matrix_1.4-0
[97] vctrs_0.3.8 pillar_1.6.4 lifecycle_1.0.1
[100] spatstat.geom_2.3-1 lmtest_0.9-39 jquerylib_0.1.4
[103] RcppAnnoy_0.0.19 data.table_1.14.2 cowplot_1.1.1
[106] irlba_2.3.5 httpuv_1.6.3 R6_2.5.1
[109] promises_1.2.0.1 KernSmooth_2.23-20 gridExtra_2.3
[112] parallelly_1.29.0 codetools_0.2-18 MASS_7.3-54
[115] assertthat_0.2.1 rprojroot_2.0.2 withr_2.4.3
[118] sctransform_0.3.2.9008 mgcv_1.8-38 parallel_4.1.2
[121] hms_1.1.1 grid_4.1.2 rpart_4.1-15
[124] rmarkdown_2.11 carData_3.0-4 Rtsne_0.15
[127] git2r_0.29.0 shiny_1.7.1 lubridate_1.8.0