Last updated: 2021-12-17
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suppressPackageStartupMessages({
library(Seurat)
library(ggplot2)
library(patchwork)
library(stringr)
library(httr)
library(XML)
})
set.seed(42)
theme_set(theme_classic())
FetchGEOFiles <- function(geo, download.dir = getwd(), download.files = FALSE, ...) {
geo <- trimws(toupper(geo))
geo_type <- substr(geo, 1, 3)
url.prefix <- "https://ftp.ncbi.nlm.nih.gov/geo/"
if (geo_type == "GSE") {
url.prefix <- paste0(url.prefix, "series/")
} else if (geo_type == "GSM") {
url.prefix <- paste0(url.prefix, "samples/")
} else if (geotype == "GPL") {
url.prefix <- paste0(url.prefix, "platform/")
}
geo_prefix <- paste0(substr(x = geo, start = 1, stop = nchar(geo) - 3), "nnn")
url <- paste0(url.prefix, geo_prefix, "/", geo, "/", "suppl", "/")
response <- GET(url = url)
html_parsed <- htmlParse(file = response)
links <- xpathSApply(doc = html_parsed, path = "//a/@href")
suppl_files <- as.character(grep(pattern = "^G", x = links, value = TRUE))
if (length(suppl_files) == 0) {
return(NULL)
}
file.url <- paste0(url, suppl_files)
file_list <- data.frame(filename = suppl_files, url = file.url)
if (download.files) {
names(file.url) <- suppl_files
download_file <- function(url, filename, ...) {
message(paste0("Downloading ", filename, " to ", download.dir))
download.file(url = url, destfile = file.path(download.dir, filename), mode = "wb", ...)
message("Done!")
}
lapply(seq_along(file.url), function(y, n, i) {
download_file(y[[i]], n[[i]], ...)
},
y = file.url, n = names(file.url)
)
}
return(file_list)
}download_dir <- here::here("data/raw_data/Ding")
dir.create(download_dir, showWarnings = F, recursive = T)
dir.create(here::here("data/rds_raw"), showWarnings = F, recursive = T)
geo_files <- FetchGEOFiles("GSE132044", download_dir, download.files = T)
geo_files filename
1 GSE132044_HEK293_PBMC_TPM_bulk.tsv.gz
2 GSE132044_NIH3T3_cortex_TPM_bulk.tsv.gz
3 GSE132044_cortex_mm10_cell.tsv.gz
4 GSE132044_cortex_mm10_count_matrix.mtx.gz
5 GSE132044_cortex_mm10_gene.tsv.gz
6 GSE132044_mixture_hg19_mm10_cell.tsv.gz
7 GSE132044_mixture_hg19_mm10_count_matrix.mtx.gz
8 GSE132044_mixture_hg19_mm10_gene.tsv.gz
9 GSE132044_pbmc_hg38_cell.tsv.gz
10 GSE132044_pbmc_hg38_count_matrix.mtx.gz
11 GSE132044_pbmc_hg38_gene.tsv.gz
url
1 https://ftp.ncbi.nlm.nih.gov/geo/series/GSE132nnn/GSE132044/suppl/GSE132044_HEK293_PBMC_TPM_bulk.tsv.gz
2 https://ftp.ncbi.nlm.nih.gov/geo/series/GSE132nnn/GSE132044/suppl/GSE132044_NIH3T3_cortex_TPM_bulk.tsv.gz
3 https://ftp.ncbi.nlm.nih.gov/geo/series/GSE132nnn/GSE132044/suppl/GSE132044_cortex_mm10_cell.tsv.gz
4 https://ftp.ncbi.nlm.nih.gov/geo/series/GSE132nnn/GSE132044/suppl/GSE132044_cortex_mm10_count_matrix.mtx.gz
5 https://ftp.ncbi.nlm.nih.gov/geo/series/GSE132nnn/GSE132044/suppl/GSE132044_cortex_mm10_gene.tsv.gz
6 https://ftp.ncbi.nlm.nih.gov/geo/series/GSE132nnn/GSE132044/suppl/GSE132044_mixture_hg19_mm10_cell.tsv.gz
7 https://ftp.ncbi.nlm.nih.gov/geo/series/GSE132nnn/GSE132044/suppl/GSE132044_mixture_hg19_mm10_count_matrix.mtx.gz
8 https://ftp.ncbi.nlm.nih.gov/geo/series/GSE132nnn/GSE132044/suppl/GSE132044_mixture_hg19_mm10_gene.tsv.gz
9 https://ftp.ncbi.nlm.nih.gov/geo/series/GSE132nnn/GSE132044/suppl/GSE132044_pbmc_hg38_cell.tsv.gz
10 https://ftp.ncbi.nlm.nih.gov/geo/series/GSE132nnn/GSE132044/suppl/GSE132044_pbmc_hg38_count_matrix.mtx.gz
11 https://ftp.ncbi.nlm.nih.gov/geo/series/GSE132nnn/GSE132044/suppl/GSE132044_pbmc_hg38_gene.tsv.gzcortex <- ReadMtx(
mtx = "~/github/scRNA_NB_comparison/data/raw_data/Ding/GSE132044_cortex_mm10_count_matrix.mtx.gz",
cells = "~/github/scRNA_NB_comparison/data/raw_data/Ding/GSE132044_cortex_mm10_cell.tsv.gz",
features = "~/github/scRNA_NB_comparison/data/raw_data/Ding/GSE132044_cortex_mm10_gene.tsv.gz", feature.column = 1
)
cortex[1:5, 1:5]5 x 5 sparse Matrix of class "dgCMatrix"
Cortex1.Smart-seq2.p1_A1 Cortex1.Smart-seq2.p2_A12
ENSMUSG00000000001_Gnai3 . .
ENSMUSG00000000003_Pbsn . .
ENSMUSG00000000028_Cdc45 . .
ENSMUSG00000000031_H19 . .
ENSMUSG00000000037_Scml2 . .
Cortex1.Smart-seq2.p2_A2 Cortex1.Smart-seq2.p2_A4
ENSMUSG00000000001_Gnai3 . 80
ENSMUSG00000000003_Pbsn . .
ENSMUSG00000000028_Cdc45 . .
ENSMUSG00000000031_H19 . .
ENSMUSG00000000037_Scml2 . .
Cortex1.Smart-seq2.p2_A5
ENSMUSG00000000001_Gnai3 .
ENSMUSG00000000003_Pbsn .
ENSMUSG00000000028_Cdc45 .
ENSMUSG00000000031_H19 .
ENSMUSG00000000037_Scml2 .replicate <- str_split_fixed(colnames(cortex), pattern = "\\.", n = 3)[, 1]
technology <- str_split_fixed(colnames(cortex), pattern = "\\.", n = 3)[, 2]
metadata <- data.frame(technology = technology, replicate = replicate)
rownames(metadata) <- colnames(cortex)
head(metadata) technology replicate
Cortex1.Smart-seq2.p1_A1 Smart-seq2 Cortex1
Cortex1.Smart-seq2.p2_A12 Smart-seq2 Cortex1
Cortex1.Smart-seq2.p2_A2 Smart-seq2 Cortex1
Cortex1.Smart-seq2.p2_A4 Smart-seq2 Cortex1
Cortex1.Smart-seq2.p2_A5 Smart-seq2 Cortex1
Cortex1.Smart-seq2.p2_A7 Smart-seq2 Cortex1genes <- rownames(cortex)
gene_names <- make.unique(str_split_fixed(genes, pattern = "_", n = 2)[, 2])
rownames(cortex) <- gene_names
cortex <- CreateSeuratObject(counts = cortex, project = "Ding_scBenchmark_Cortex", meta.data = metadata, min.cells = 1, min.features = 1)all_technologies <- sort(unique(metadata$technology))
all_technologies[1] "10x-Chromium-v2" "DroNc-seq" "sci-RNA-seq" "Smart-seq2" umi_techs <- c("10x-Chromium-v2", "DroNc-seq", "sci-RNA-seq")
clean_named_techs <- c("ChromiumV2", "DroNc-seq", "sci-RNA-seq")
names(clean_named_techs) <- umi_techs
clean_named_techs10x-Chromium-v2 DroNc-seq sci-RNA-seq
"ChromiumV2" "DroNc-seq" "sci-RNA-seq" `%notin%` <- Negate(`%in%`)
split_cortex <- SplitObject(object = cortex, split.by = "technology")
names(split_cortex)[1] "Smart-seq2" "10x-Chromium-v2" "DroNc-seq" "sci-RNA-seq" for (technology in names(split_cortex)) {
if (technology %notin% umi_techs) next
obj <- split_cortex[[technology]]
obj_split <- SplitObject(object = obj, split.by = "replicate")
clean_tech <- clean_named_techs[[technology]]
for (sampletype in names(obj_split)) {
seu <- obj_split[[sampletype]]
seu[["percent.mt"]] <- PercentageFeatureSet(seu, pattern = "^mt-")
saveRDS(
seu,
here::here("data/rds_raw/", paste0("Ding-", sampletype, "__", clean_tech, ".rds"))
)
}
}
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] XML_3.99-0.8 httr_1.4.2 stringr_1.4.0 patchwork_1.1.1
[5] ggplot2_3.3.5 SeuratObject_4.0.4 Seurat_4.0.5 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rtsne_0.15 colorspace_2.0-2 deldir_1.0-6
[4] ellipsis_0.3.2 ggridges_0.5.3 rprojroot_2.0.2
[7] fs_1.5.2 spatstat.data_2.1-0 leiden_0.3.9
[10] listenv_0.8.0 ggrepel_0.9.1 fansi_0.5.0
[13] codetools_0.2-18 splines_4.1.2 knitr_1.36
[16] polyclip_1.10-0 jsonlite_1.7.2 ica_1.0-2
[19] cluster_2.1.2 png_0.1-7 uwot_0.1.11
[22] shiny_1.7.1 sctransform_0.3.2.9008 spatstat.sparse_2.0-0
[25] compiler_4.1.2 assertthat_0.2.1 Matrix_1.4-0
[28] fastmap_1.1.0 lazyeval_0.2.2 later_1.3.0
[31] htmltools_0.5.2 tools_4.1.2 igraph_1.2.9
[34] gtable_0.3.0 glue_1.5.1 RANN_2.6.1
[37] reshape2_1.4.4 dplyr_1.0.7 Rcpp_1.0.7
[40] scattermore_0.7 jquerylib_0.1.4 vctrs_0.3.8
[43] nlme_3.1-152 lmtest_0.9-39 xfun_0.28
[46] globals_0.14.0 mime_0.12 miniUI_0.1.1.1
[49] lifecycle_1.0.1 irlba_2.3.5 goftest_1.2-3
[52] future_1.23.0 MASS_7.3-54 zoo_1.8-9
[55] scales_1.1.1 spatstat.core_2.3-2 promises_1.2.0.1
[58] spatstat.utils_2.3-0 parallel_4.1.2 RColorBrewer_1.1-2
[61] curl_4.3.2 yaml_2.2.1 reticulate_1.22
[64] pbapply_1.5-0 gridExtra_2.3 sass_0.4.0
[67] rpart_4.1-15 stringi_1.7.6 rlang_0.4.12
[70] pkgconfig_2.0.3 matrixStats_0.61.0 evaluate_0.14
[73] lattice_0.20-45 ROCR_1.0-11 purrr_0.3.4
[76] tensor_1.5 htmlwidgets_1.5.4 cowplot_1.1.1
[79] tidyselect_1.1.1 here_1.0.1 parallelly_1.29.0
[82] RcppAnnoy_0.0.19 plyr_1.8.6 magrittr_2.0.1
[85] R6_2.5.1 generics_0.1.1 DBI_1.1.1
[88] withr_2.4.3 mgcv_1.8-38 pillar_1.6.4
[91] whisker_0.4 fitdistrplus_1.1-6 survival_3.2-13
[94] abind_1.4-5 tibble_3.1.6 future.apply_1.8.1
[97] crayon_1.4.2 KernSmooth_2.23-20 utf8_1.2.2
[100] spatstat.geom_2.3-1 plotly_4.10.0 rmarkdown_2.11
[103] grid_4.1.2 data.table_1.14.2 git2r_0.29.0
[106] digest_0.6.29 xtable_1.8-4 tidyr_1.1.4
[109] httpuv_1.6.3 munsell_0.5.0 viridisLite_0.4.0
[112] bslib_0.3.1