Supplementary Figure 1
Last updated: 2021-12-17
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Knit directory: sct2_revision/
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suppressPackageStartupMessages({
library(dplyr)
library(ggplot2)
library(kableExtra)
library(ggpubr)
library(ggridges)
library(here)
library(patchwork)
library(RColorBrewer)
library(readr)
library(reshape2)
library(scattermore)
library(Seurat)
library(sparseMatrixStats)
library(xtable)
})
`%notin%` <- Negate(`%in%`)
theme_set(theme_pubr(base_size = 9))
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
clean_keys <- function(key) {
gsub(
pattern = "|\\)", replacement = "",
x = gsub(pattern = " |\\(", replacement = "_", x = key)
)
}CellSummary <- function(cm) {
total_umi_per_cell <- colSums(cm)
expressed_features_per_cell <- colSums(x = cm > 0)
n_features <- dim(cm)[1]
nonexpressed_features_per_cell <- n_features - expressed_features_per_cell
median_umi_per_cell <- median(total_umi_per_cell)
avg_umi_per_cell <- total_umi_per_cell / n_features
avg_umi_per_cell_expressedgenes <- total_umi_per_cell / expressed_features_per_cell
cell_amean <- colMeans2(cm)
cell_variance <- colVars(cm)
cell_attr <- data.frame(
total_umi = total_umi_per_cell, n_expressed_genes = expressed_features_per_cell, n_nonexpressed_cells = nonexpressed_features_per_cell, prop_expressed_genes = expressed_features_per_cell / n_features,
prop_nonexpressed_genes = nonexpressed_features_per_cell / n_features,
avg_umi = avg_umi_per_cell, avg_umi_expressedgenes = avg_umi_per_cell_expressedgenes, cell_amean = cell_amean,
cell_variance = cell_variance
)
return(cell_attr)
}
GeneSummary <- function(cm) {
# remove genes and cells with zero counts
cm <- cm[rowSums(cm) > 0, colSums(cm) > 0]
total_umi_per_gene <- rowSums(cm)
expressed_cells_per_gene <- rowSums(cm > 0)
n_cells <- dim(cm)[2]
nonexpressed_cells_per_gene <- n_cells - expressed_cells_per_gene
median_umi_per_gene <- median(total_umi_per_gene)
avg_umi_per_gene <- total_umi_per_gene / n_cells
avg_umi_per_gene_expressedcells <- total_umi_per_gene / expressed_cells_per_gene
gene_amean <- rowMeans(cm)
gene_var <- rowVars(cm)
gene_gmean <- sctransform:::row_gmean(cm)
gene_attr <- data.frame(
total_umi = total_umi_per_gene, n_expressed_cells = expressed_cells_per_gene, n_nonexpressed_cells = nonexpressed_cells_per_gene, prop_expressed_cells = expressed_cells_per_gene / n_cells,
prop_nonexpressed_cells = nonexpressed_cells_per_gene / n_cells,
avg_umi = avg_umi_per_gene, avg_umi_expressedcells = avg_umi_per_gene_expressedcells, gene_amean = gene_amean, gene_gmean = gene_gmean, gene_variance = gene_var
)
return(gene_attr)
}
GetGeneCellSummary <- function(dataset_name, mode = "gene") {
cm <- GetAssayData(
readRDS(here::here("data", "rds_filtered", paste0(clean_keys(dataset_name), ".rds"))),
assay = "RNA", slot = "counts"
)
if (mode == "gene") {
gc_attr <- GeneSummary(cm)
} else {
gc_attr <- CellSummary(cm)
}
cm <- NULL
gc()
return(gc_attr)
}datasets <- readr::read_csv(here::here("data", "datasets.csv"), col_types = readr::cols())
dataset_keys <- datasets$key
counts <- sapply(dataset_keys,
FUN = function(x) {
GetAssayData(
readRDS(here::here("data", "rds_filtered", paste0(clean_keys(x), ".rds"))),
assay = "RNA", slot = "counts"
)
},
simplify = FALSE, USE.NAMES = TRUE
)
cell_attrs <- sapply(dataset_keys,
FUN = function(x) {
message(x)
GetGeneCellSummary(x, "cell")
},
simplify = FALSE, USE.NAMES = TRUE
)
cell_attrs_df <- bind_rows(cell_attrs, .id = "key")
cell_attrs_df <- left_join(cell_attrs_df, datasets)
gene_attrs <- sapply(dataset_keys,
FUN = function(x) {
GetGeneCellSummary(x, "gene")
},
simplify = FALSE, USE.NAMES = TRUE
)
gene_attrs_df <- bind_rows(gene_attrs, .id = "key")
gene_attrs_df <- left_join(gene_attrs_df, datasets)UMI statistics
gene_attrs_df$datatype <- factor(gene_attrs_df$datatype, levels = c("technical-control", "cell line", "heterogeneous"))
cell_attrs_df$datatype <- factor(cell_attrs_df$datatype, levels = c("technical-control", "cell line", "heterogeneous"))
gene_attrs_df_summary <- gene_attrs_df %>%
group_by(sample_name) %>%
summarize(median.zero_prop = round(median(prop_nonexpressed_cells), 4), median.detection_rate = round(median(prop_expressed_cells), 4))
gene_attrs_df_summary <- left_join(gene_attrs_df_summary, datasets)
pgeneavg <- ggplot(gene_attrs_df, aes(
x = avg_umi,
y = reorder(sample_name, avg_umi, FUN = median),
avg_umi, fill = datatype
)) +
scale_x_continuous(trans = "log10", breaks = c(0.0001, 0.01, 1, 100, 10000), labels = c("0.0001", 0.01, 1, 100, 10000)) +
stat_density_ridges(quantile_lines = TRUE, quantiles = 2) +
scale_fill_manual(values = brewer.pal(3, "Set2"), name = "") +
labs(title = "") +
ylab("") +
theme(
legend.position = "bottom",
legend.direction = "horizontal",
legend.background = element_blank()
) +
guides(col = guide_legend(ncol = 3)) +
xlab("Mean UMI per gene")
cell_attrs_df_summary <- cell_attrs_df %>%
group_by(sample_name, datatype) %>%
summarize(median_umi = median(total_umi), median_detection_rate = round(median(prop_expressed_genes), 3))
# cell_attrs_df_summary
pcelltot <- ggplot(cell_attrs_df, aes(
x = total_umi,
y = reorder(sample_name, total_umi, FUN = median),
total_umi, fill = datatype
)) +
scale_x_continuous(trans = "log10", breaks = c(0.0001, 0.01, 0.1, 1, 10, 100, 1000, 10000, 100000), labels = c("0.0001", 0.01, 0.1, 1, 10, 100, 1000, 10000, "100000")) +
stat_density_ridges(quantile_lines = TRUE, quantiles = 2) +
scale_fill_manual(values = brewer.pal(3, "Set2"), name = "") +
labs(title = "") +
ylab("") +
theme(
legend.position = "bottom",
legend.direction = "horizontal",
legend.background = element_blank()
) +
guides(col = guide_legend(ncol = 3)) +
xlab("Total UMI per cell")
wrap_plots(pcelltot,pgeneavg, ncol = 2) + plot_annotation(tag_levels = "A") + plot_layout(guides = "collect", tag_level = "new") & theme(legend.position = "bottom") & theme(plot.tag = element_text(face = "bold"))
| Version | Author | Date |
|---|---|---|
| 400797a | Saket Choudhary | 2021-07-06 |
dir.create(here::here("output", "figures"), showWarnings = F)
ggsave(here::here("output", "figures", "data_stats.pdf"), width = 12, height = 12, dpi = "print")cell_attrs_df_summary <- cell_attrs_df_summary %>% arrange(median_umi)
kbl(cell_attrs_df_summary, booktabs = T) %>%
kable_styling(latex_options = "striped")| sample_name | datatype | median_umi | median_detection_rate |
|---|---|---|---|
| PBMC-r1 (inDrops) | heterogeneous | 375.0 | 0.009 |
| Fetal (sci-RNA-seq3) | heterogeneous | 499.0 | 0.008 |
| PBMC-r2 (Seq-Well) | heterogeneous | 521.0 | 0.011 |
| PBMC-r1 (Seq-Well) | heterogeneous | 846.0 | 0.017 |
| PBMC-r1 (Drop-seq) | heterogeneous | 1199.0 | 0.022 |
| PBMC-r2 (inDrops) | heterogeneous | 1247.0 | 0.020 |
| PBMC68k (ChromiumV1) | heterogeneous | 1292.0 | 0.026 |
| PBMC-r2 (Drop-seq) | heterogeneous | 1850.5 | 0.030 |
| Cortex-r2 (sci-RNA-seq) | heterogeneous | 1899.0 | 0.044 |
| HEK-m (Drop-seq) | cell line | 1907.5 | 0.051 |
| PBMC-r1 (ChromiumV2A) | heterogeneous | 2032.0 | 0.027 |
| Cortex-r1 (DroNc-seq) | heterogeneous | 2092.0 | 0.053 |
| 3T3-r1 (inDrops) | cell line | 2213.0 | 0.061 |
| Bone Marrow (CITE-seq) | heterogeneous | 2294.0 | 0.045 |
| TechCtrl1 (ChromiumV1) | technical-control | 2308.5 | 0.031 |
| TechCtrl2 (ChromiumV1) | technical-control | 2566.0 | 0.031 |
| PBMC-r2 (ChromiumV2) | heterogeneous | 2626.0 | 0.036 |
| HEK-m (inDrops) | cell line | 2943.0 | 0.073 |
| HEK-r1 (inDrops) | cell line | 3019.0 | 0.057 |
| PBMC-r1 (ChromiumV2B) | heterogeneous | 3050.0 | 0.037 |
| 3T3-r1 (Drop-seq) | cell line | 3072.0 | 0.082 |
| Cortex-r2 (DroNc-seq) | heterogeneous | 3094.0 | 0.071 |
| HEK-m (mcSCRB-seq) | cell line | 3266.5 | 0.063 |
| 3T3-r2 (Drop-seq) | cell line | 3345.0 | 0.091 |
| Cortex-r1 (sci-RNA-seq) | heterogeneous | 3524.0 | 0.060 |
| Cortex-r2 (ChromiumV2) | heterogeneous | 3527.0 | 0.073 |
| HEK-r2 (inDrops) | cell line | 3904.0 | 0.073 |
| 3T3-r2 (inDrops) | cell line | 4666.5 | 0.107 |
| HEK-m (ChromiumV2_sn) | cell line | 4967.0 | 0.128 |
| HEK-r2 (Drop-seq) | cell line | 4968.5 | 0.085 |
| PBMC-r1 (ChromiumV3) | heterogeneous | 5066.0 | 0.054 |
| HEK-m (ddSeq) | cell line | 5304.5 | 0.108 |
| HEK-r1 (Drop-seq) | cell line | 5328.0 | 0.092 |
| PBMC-r2 (CEL-seq2) | heterogeneous | 5917.0 | 0.087 |
| 3T3-r1 (sci-RNA-seq) | cell line | 6609.5 | 0.136 |
| PBMC-r1 (CEL-seq2) | heterogeneous | 6848.0 | 0.096 |
| PBMC (ChromiumV3) | heterogeneous | 6992.0 | 0.107 |
| Cortex-r1 (ChromiumV2) | heterogeneous | 6993.5 | 0.122 |
| 3T3-r2 (sci-RNA-seq) | cell line | 8256.0 | 0.160 |
| PBMC (Smart-seq3) | heterogeneous | 8288.0 | 0.058 |
| 3T3-r1 (ChromiumV2) | cell line | 9548.0 | 0.140 |
| HEK-r2 (sci-RNA-seq) | cell line | 11045.0 | 0.160 |
| HEK-m (MARS-seq) | cell line | 11207.5 | 0.180 |
| HEK-r1 (sci-RNA-seq) | cell line | 11490.0 | 0.158 |
| 3T3-r2 (ChromiumV2) | cell line | 13776.5 | 0.185 |
| 3T3 (ChromiumV3) | cell line | 15577.0 | 0.180 |
| HEK-r2 (ChromiumV2) | cell line | 22986.5 | 0.171 |
| HEK-r1 (ChromiumV2) | cell line | 23388.0 | 0.169 |
| TechCtrl (inDrops) | technical-control | 32905.0 | 0.391 |
| 3T3-r1 (CEL-seq2) | cell line | 34291.0 | 0.321 |
| HEK (ChromiumV3) | cell line | 40547.0 | 0.246 |
| HEK-r2 (CEL-seq2) | cell line | 43670.0 | 0.287 |
| HEK-r1 (CEL-seq2) | cell line | 52973.0 | 0.308 |
| 3T3-r2 (CEL-seq2) | cell line | 53036.0 | 0.367 |
| HEK-m (CEL-seq2) | cell line | 60592.5 | 0.479 |
| HEK-m (ChromiumV2) | cell line | 73333.5 | 0.434 |
| HEK (Smart-seq3) | cell line | 106994.0 | 0.381 |
| HEK-m (Quartz-Seq2) | cell line | 167199.0 | 0.548 |
| Fibroblasts (Smart-seq3) | heterogeneous | 196626.0 | 0.380 |
dir.create(here::here("output", "tables"), showWarnings = F)
print(xtable(cell_attrs_df_summary, type = "latex", digits=3), include.rownames = FALSE, file = here::here("output", "tables", "datasets_umi_stats.tex"))
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] xtable_1.8-4 sparseMatrixStats_1.4.2 MatrixGenerics_1.4.3
[4] matrixStats_0.61.0 SeuratObject_4.0.4 Seurat_4.0.5
[7] scattermore_0.7 reshape2_1.4.4 readr_2.1.1
[10] RColorBrewer_1.1-2 patchwork_1.1.1 here_1.0.1
[13] ggridges_0.5.3 ggpubr_0.4.0 kableExtra_1.3.4
[16] ggplot2_3.3.5 dplyr_1.0.7 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] backports_1.4.1 systemfonts_1.0.3 plyr_1.8.6
[4] igraph_1.2.9 lazyeval_0.2.2 splines_4.1.2
[7] listenv_0.8.0 digest_0.6.29 htmltools_0.5.2
[10] fansi_0.5.0 magrittr_2.0.1 tensor_1.5
[13] cluster_2.1.2 ROCR_1.0-11 tzdb_0.2.0
[16] globals_0.14.0 vroom_1.5.7 svglite_2.0.0
[19] spatstat.sparse_2.0-0 colorspace_2.0-2 rvest_1.0.2
[22] ggrepel_0.9.1 xfun_0.28 crayon_1.4.2
[25] jsonlite_1.7.2 spatstat.data_2.1-0 survival_3.2-13
[28] zoo_1.8-9 glue_1.5.1 polyclip_1.10-0
[31] gtable_0.3.0 webshot_0.5.2 leiden_0.3.9
[34] car_3.0-12 future.apply_1.8.1 abind_1.4-5
[37] scales_1.1.1 DBI_1.1.1 rstatix_0.7.0
[40] miniUI_0.1.1.1 Rcpp_1.0.7 viridisLite_0.4.0
[43] reticulate_1.22 spatstat.core_2.3-2 bit_4.0.4
[46] htmlwidgets_1.5.4 httr_1.4.2 ellipsis_0.3.2
[49] ica_1.0-2 farver_2.1.0 pkgconfig_2.0.3
[52] sass_0.4.0 uwot_0.1.11 deldir_1.0-6
[55] utf8_1.2.2 tidyselect_1.1.1 rlang_0.4.12
[58] later_1.3.0 munsell_0.5.0 tools_4.1.2
[61] generics_0.1.1 broom_0.7.10 evaluate_0.14
[64] stringr_1.4.0 fastmap_1.1.0 yaml_2.2.1
[67] goftest_1.2-3 knitr_1.36 bit64_4.0.5
[70] fs_1.5.2 fitdistrplus_1.1-6 purrr_0.3.4
[73] RANN_2.6.1 pbapply_1.5-0 future_1.23.0
[76] nlme_3.1-152 whisker_0.4 mime_0.12
[79] xml2_1.3.3 compiler_4.1.2 rstudioapi_0.13
[82] plotly_4.10.0 png_0.1-7 ggsignif_0.6.3
[85] spatstat.utils_2.3-0 tibble_3.1.6 bslib_0.3.1
[88] stringi_1.7.6 highr_0.9 lattice_0.20-45
[91] Matrix_1.4-0 vctrs_0.3.8 pillar_1.6.4
[94] lifecycle_1.0.1 spatstat.geom_2.3-1 lmtest_0.9-39
[97] jquerylib_0.1.4 RcppAnnoy_0.0.19 data.table_1.14.2
[100] cowplot_1.1.1 irlba_2.3.5 httpuv_1.6.3
[103] R6_2.5.1 promises_1.2.0.1 KernSmooth_2.23-20
[106] gridExtra_2.3 parallelly_1.29.0 codetools_0.2-18
[109] MASS_7.3-54 assertthat_0.2.1 rprojroot_2.0.2
[112] withr_2.4.3 sctransform_0.3.2.9008 mgcv_1.8-38
[115] parallel_4.1.2 hms_1.1.1 grid_4.1.2
[118] rpart_4.1-15 tidyr_1.1.4 rmarkdown_2.11
[121] carData_3.0-4 Rtsne_0.15 git2r_0.29.0
[124] shiny_1.7.1