Figure 3
Last updated: 2021-12-17
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Knit directory: sct2_revision/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
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| html | d736ec8 | Saket Choudhary | 2021-07-07 | Build site. |
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| html | 400797a | Saket Choudhary | 2021-07-06 | workflowr::wflow_git_commit(all = TRUE) |
Compare parameters
gene_attr <- list()
root_dir_sct <- here::here("output/snakemake_output/sct_v1_ncells_benchmarks/Fetal__sci-RNA-seq3")
root_dir_sct2 <- here::here("output/snakemake_output/sct_ncells_benchmarks/Fetal__sci-RNA-seq3")
for (ncell in list.dirs(root_dir_sct, full.names = F)) {
if (ncell == "") {
next
}
fp1 <- here::here(file.path(root_dir_sct, ncell), "gene_attr_sct.csv")
fp2 <- here::here(file.path(root_dir_sct2, ncell), "gene_attr_sct2.csv")
if (!file.exists(fp1)) next
if (!file.exists(fp2)) next
df1 <- read.csv(fp1)
if (ncell == "Full") {
# seu <- readRDS("data/rds/Fetal__sci-RNA-seq3.rds")
# dim(seu)
# 63561 377456
ncell <- 377456
}
df1$ncells <- as.numeric(ncell)
df1$type <- "Regularized parameters (v1)"
df2 <- read.csv(fp2)
df2$ncells <- as.numeric(ncell)
df2$type <- "Regularized parameters (v2)"
gene_attr[[ncell]] <- rbind(df1, df2)
}
gene_attr_df <- bind_rows(gene_attr)
gene_attr_df$ncells <- factor(gene_attr_df$ncells, levels = sort(unique(gene_attr_df$ncells)), labels = so_formatter(sort(unique(gene_attr_df$ncells))))
plot.theta <- ggplot(gene_attr_df, aes(x = log10(gmean), y = log10(theta), color = ncells)) +
scale_color_manual(values = brewer.pal(11, "Paired"), name = "") +
geom_scattermore(pointsize = 1, position = "jitter", alpha = 0.5) +
facet_wrap(~type, ncol=2) +
guides(colour = guide_legend(override.aes = list(size = 2))) +
xlab(expression(paste(log[10], "(Gene mean)"))) +
ylab(expression(paste(log[10], theta))) +
theme(legend.position = "right", legend.direction = "vertical",
legend.background = element_blank()
) +
guides(color=guide_legend(ncol=2)) +
theme(legend.position = c(0.88,0.1)) + theme(legend.key.size = unit(.75, 'lines'))
plot.theta
| Version | Author | Date |
|---|---|---|
| 400797a | Saket Choudhary | 2021-07-06 |
Compare runtimes
times <- list()
root_dir <- here::here("output/snakemake_output/vst2_time_benchmarks/Fetal__sci-RNA-seq3")
for (ncell in list.dirs(root_dir, full.names = F)) {
if (ncell == "") {
next
}
for (seed in list.dirs(file.path(root_dir, ncell), full.names = F)) {
if (seed == "") {
next
}
fp <- here::here(file.path(root_dir, ncell, seed), "times.csv")
if (!file.exists(fp)) next
df <- read.csv(fp)
df$ncell <- as.numeric(ncell)
df$seed <- as.numeric(seed)
times[[paste0(ncell, seed)]] <- df
}
}
times <- bind_rows(times)
times_melt <- melt(times, id.vars = c("ncells", "seed"), measure.vars = c("time_sct", "time_sct2"), variable.name = "type", value.name = "time")
times_melt$ncells <- as.numeric(times_melt$ncells)
times_melt <- summarySE(times_melt, measurevar = "time", groupvars = c("ncells", "type"))
times_melt$ncells <- factor(times_melt$ncells, levels = sort(unique(times_melt$ncells)), labels = so_formatter(sort(unique(times_melt$ncells))))
times_melt <- times_melt %>% arrange(ncells)
plot.time <- ggplot(times_melt, aes(ncells, time, color = type, group = type)) +
geom_errorbar(aes(ymin = time - se, ymax = time + se), width = .1) +
geom_line() +
geom_point() +
geom_line(show.legend = F) +
scale_color_manual(values = brewer.pal(3, "Dark2"), labels = c("all cells", "n_cells=2,000"), name = "") +
theme(legend.position = c(0.3, 0.9), legend.background = element_blank()) +
guides(col = guide_legend(ncol = 1)) +
xlab("Number of cells") +
ylab("Time (s)") +
#scale_y_log10() +
guides(x = guide_axis(angle = 40)) + theme(legend.key.size = unit(0.75, 'lines'))
plot.time
| Version | Author | Date |
|---|---|---|
| 400797a | Saket Choudhary | 2021-07-06 |
Correlations
gene_attr <- list()
common_genes <- NULL
ncells <- c()
root_dir <- here::here("output/snakemake_output/sct_ncells_benchmarks/Fetal__sci-RNA-seq3/")
for (ncell in list.dirs(root_dir, full.names = F)) {
if (ncell == "") {
next
}
fp1 <- here::here(file.path(root_dir, ncell), "gene_attr_sct2.csv")
if (!file.exists(fp1)) next
df1 <- read.csv(fp1, row.names = 1)
if (is.null(common_genes)) {
common_genes <- rownames(df1)
} else {
common_genes <- intersect(common_genes, rownames(df1))
}
df1 <- df1[common_genes, ]
df1$ncells <- ncell
gene_attr[[ncell]] <- df1
ncell <- as.integer(ncell)
if (!is.na(ncell)) ncells <- c(ncells, ncell)
}
ncells_level <- sort(ncells)
ncells <- c(sort(ncells), "Full")
mat <- sapply(gene_attr, function(x) x$residual_variance)
colnames(mat) <- names(gene_attr)
mat <- as.data.frame(mat)
rownames(mat) <- common_genes
mat <- mat[order(-mat$Full),]
matc <- mat
colnames(matc) <- paste0("C", colnames(matc))
#matc <- log1p(matc)#[1:3000, ]
plot.resvar <- ggplot(matc, aes_string("CFull", "C2000")) +
geom_point(alpha=0.5) +#scattermore(pointsize = 3) +
geom_abline(color = "red", linetype = "dashed") +
xlab(expression(sigma["all cells"]^2)) +
ylab(expression(sigma["n_cells=2,000"]^2))
plot.resvar
plot.thetax <- plot.theta +
theme(legend.position = c(0.86,0.28))
layout <- "
AABC
"
p <- plot.thetax | plot.resvar | plot.time
p + plot_layout(design = layout, tag_level = "new") & plot_annotation(tag_levels = "A") & theme(plot.tag = element_text(face = "bold"))
ggsave(here::here("output", "figures", "03_Figure3.pdf"), width = 9, height=3, dpi = "print")
ggsave(here::here("output", "figures", "03_Figure3.png"),)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] plyr_1.8.6 SeuratObject_4.0.4 Seurat_4.0.5 reshape2_1.4.4
[5] RColorBrewer_1.1-2 purrr_0.3.4 patchwork_1.1.1 here_1.0.1
[9] scattermore_0.7 ggpubr_0.4.0 ggridges_0.5.3 ggplot2_3.3.5
[13] forcats_0.5.1 dplyr_1.0.7 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rtsne_0.15 colorspace_2.0-2 deldir_1.0-6
[4] ggsignif_0.6.3 ellipsis_0.3.2 rprojroot_2.0.2
[7] fs_1.5.2 spatstat.data_2.1-0 farver_2.1.0
[10] leiden_0.3.9 listenv_0.8.0 ggrepel_0.9.1
[13] fansi_0.5.0 codetools_0.2-18 splines_4.1.2
[16] knitr_1.36 polyclip_1.10-0 jsonlite_1.7.2
[19] broom_0.7.10 ica_1.0-2 cluster_2.1.2
[22] png_0.1-7 uwot_0.1.11 shiny_1.7.1
[25] sctransform_0.3.2.9008 spatstat.sparse_2.0-0 compiler_4.1.2
[28] httr_1.4.2 backports_1.4.1 assertthat_0.2.1
[31] Matrix_1.4-0 fastmap_1.1.0 lazyeval_0.2.2
[34] later_1.3.0 htmltools_0.5.2 tools_4.1.2
[37] igraph_1.2.9 gtable_0.3.0 glue_1.5.1
[40] RANN_2.6.1 Rcpp_1.0.7 carData_3.0-4
[43] jquerylib_0.1.4 vctrs_0.3.8 nlme_3.1-152
[46] lmtest_0.9-39 xfun_0.28 stringr_1.4.0
[49] globals_0.14.0 mime_0.12 miniUI_0.1.1.1
[52] lifecycle_1.0.1 irlba_2.3.5 goftest_1.2-3
[55] rstatix_0.7.0 future_1.23.0 MASS_7.3-54
[58] zoo_1.8-9 scales_1.1.1 spatstat.core_2.3-2
[61] promises_1.2.0.1 spatstat.utils_2.3-0 parallel_4.1.2
[64] yaml_2.2.1 reticulate_1.22 pbapply_1.5-0
[67] gridExtra_2.3 sass_0.4.0 rpart_4.1-15
[70] stringi_1.7.6 highr_0.9 rlang_0.4.12
[73] pkgconfig_2.0.3 matrixStats_0.61.0 evaluate_0.14
[76] lattice_0.20-45 ROCR_1.0-11 tensor_1.5
[79] labeling_0.4.2 htmlwidgets_1.5.4 cowplot_1.1.1
[82] tidyselect_1.1.1 parallelly_1.29.0 RcppAnnoy_0.0.19
[85] magrittr_2.0.1 R6_2.5.1 generics_0.1.1
[88] DBI_1.1.1 mgcv_1.8-38 pillar_1.6.4
[91] whisker_0.4 withr_2.4.3 fitdistrplus_1.1-6
[94] survival_3.2-13 abind_1.4-5 tibble_3.1.6
[97] future.apply_1.8.1 crayon_1.4.2 car_3.0-12
[100] KernSmooth_2.23-20 utf8_1.2.2 spatstat.geom_2.3-1
[103] plotly_4.10.0 rmarkdown_2.11 grid_4.1.2
[106] data.table_1.14.2 git2r_0.29.0 digest_0.6.29
[109] xtable_1.8-4 tidyr_1.1.4 httpuv_1.6.3
[112] munsell_0.5.0 viridisLite_0.4.0 bslib_0.3.1
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] plyr_1.8.6 SeuratObject_4.0.4 Seurat_4.0.5 reshape2_1.4.4
[5] RColorBrewer_1.1-2 purrr_0.3.4 patchwork_1.1.1 here_1.0.1
[9] scattermore_0.7 ggpubr_0.4.0 ggridges_0.5.3 ggplot2_3.3.5
[13] forcats_0.5.1 dplyr_1.0.7 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rtsne_0.15 colorspace_2.0-2 deldir_1.0-6
[4] ggsignif_0.6.3 ellipsis_0.3.2 rprojroot_2.0.2
[7] fs_1.5.2 spatstat.data_2.1-0 farver_2.1.0
[10] leiden_0.3.9 listenv_0.8.0 ggrepel_0.9.1
[13] fansi_0.5.0 codetools_0.2-18 splines_4.1.2
[16] knitr_1.36 polyclip_1.10-0 jsonlite_1.7.2
[19] broom_0.7.10 ica_1.0-2 cluster_2.1.2
[22] png_0.1-7 uwot_0.1.11 shiny_1.7.1
[25] sctransform_0.3.2.9008 spatstat.sparse_2.0-0 compiler_4.1.2
[28] httr_1.4.2 backports_1.4.1 assertthat_0.2.1
[31] Matrix_1.4-0 fastmap_1.1.0 lazyeval_0.2.2
[34] later_1.3.0 htmltools_0.5.2 tools_4.1.2
[37] igraph_1.2.9 gtable_0.3.0 glue_1.5.1
[40] RANN_2.6.1 Rcpp_1.0.7 carData_3.0-4
[43] jquerylib_0.1.4 vctrs_0.3.8 nlme_3.1-152
[46] lmtest_0.9-39 xfun_0.28 stringr_1.4.0
[49] globals_0.14.0 mime_0.12 miniUI_0.1.1.1
[52] lifecycle_1.0.1 irlba_2.3.5 goftest_1.2-3
[55] rstatix_0.7.0 future_1.23.0 MASS_7.3-54
[58] zoo_1.8-9 scales_1.1.1 spatstat.core_2.3-2
[61] promises_1.2.0.1 spatstat.utils_2.3-0 parallel_4.1.2
[64] yaml_2.2.1 reticulate_1.22 pbapply_1.5-0
[67] gridExtra_2.3 sass_0.4.0 rpart_4.1-15
[70] stringi_1.7.6 highr_0.9 rlang_0.4.12
[73] pkgconfig_2.0.3 matrixStats_0.61.0 evaluate_0.14
[76] lattice_0.20-45 ROCR_1.0-11 tensor_1.5
[79] labeling_0.4.2 htmlwidgets_1.5.4 cowplot_1.1.1
[82] tidyselect_1.1.1 parallelly_1.29.0 RcppAnnoy_0.0.19
[85] magrittr_2.0.1 R6_2.5.1 generics_0.1.1
[88] DBI_1.1.1 mgcv_1.8-38 pillar_1.6.4
[91] whisker_0.4 withr_2.4.3 fitdistrplus_1.1-6
[94] survival_3.2-13 abind_1.4-5 tibble_3.1.6
[97] future.apply_1.8.1 crayon_1.4.2 car_3.0-12
[100] KernSmooth_2.23-20 utf8_1.2.2 spatstat.geom_2.3-1
[103] plotly_4.10.0 rmarkdown_2.11 grid_4.1.2
[106] data.table_1.14.2 git2r_0.29.0 digest_0.6.29
[109] xtable_1.8-4 tidyr_1.1.4 httpuv_1.6.3
[112] munsell_0.5.0 viridisLite_0.4.0 bslib_0.3.1