Last updated: 2024-10-30
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Knit directory:
single-cell-jamboree/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | a1d7a17 | Peter Carbonetto | 2024-10-30 | Added some background on the pancreas data set. |
| Rmd | 3fa28da | Peter Carbonetto | 2024-10-30 | Small edit to pancreas.Rmd. |
| Rmd | 370a336 | Peter Carbonetto | 2024-10-30 | Still working on pancreas.Rmd. |
| Rmd | fbe0b62 | Peter Carbonetto | 2024-10-30 | Made a few improvements to the pancreas analysis. |
| Rmd | c090d1d | Peter Carbonetto | 2024-10-30 | Working on initial examination of pancreas data. |
“Pancreas” data set featured in in the Luecken et al 2022 benchmarking paper. Used in several previous papers benchmark data integration methods (otherwise known as “batch correction” or “harmonization” methods) [the Supplementary Note gives references; e.g., the MNN paper]. See Supplementary Fig. 13, Supplementary Note 3 andSupplementary Data 7 for more info on this data set.
First, I downloaded the file
human_pancreas_norm_complexBatch.h5ad from figshare
and copied it to the “data” subdirectory of this git repository.
Load the packages needed for this analysis.
library(Matrix)
library(hdf5r)
library(uwot)
library(ggplot2)
library(cowplot)Load the counts and convert to a sparse matrix.
dat <- H5File$new("../data/human_pancreas_norm_complexBatch.h5ad",mode = "r")
counts <- dat[["layers"]][["counts"]][,]
counts <- as(counts,"CsparseMatrix")
counts <- t(counts)
sample_info <- data.frame(id = dat[["obs"]][["_index"]][],
tech = dat[["obs"]][["tech"]][],
celltype = dat[["obs"]][["celltype"]][],
size_factor = dat[["obs"]][["size_factors"]][],
stringsAsFactors = FALSE)
sample_info <- transform(sample_info,
tech = factor(tech),
celltype = factor(celltype))
levels(sample_info$tech) <- dat[["obs"]][["__categories"]][["tech"]][]
levels(sample_info$celltype) <- dat[["obs"]][["__categories"]][["celltype"]][]
genes <- dat[["var"]][["_index"]][]
rownames(counts) <- sample_info$id
colnames(counts) <- genesnrow(counts)
ncol(counts)
mean(counts > 0)
# [1] 16382
# [1] 19093
# [1] 0.1779012Notes:
Some entries are UMI counts (“UMI-count-like”), whereas others are not.
16,382 cells.
About 18% nonzeros.
13 cell types. Some are very rare.
9 “batches”
What to do about the very large counts? I think it is okay.
normalize_rows <- function (A)
A / rowSums(A)
X <- normalize_rows(counts)TO DO: Save the data in an .Rdata file for more convenient analysis with the matrix factorization methods.
sessionInfo()
# R version 4.3.3 (2024-02-29)
# Platform: aarch64-apple-darwin20 (64-bit)
# Running under: macOS Sonoma 14.6.1
#
# Matrix products: default
# BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
# LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
#
# locale:
# [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#
# time zone: America/Chicago
# tzcode source: internal
#
# attached base packages:
# [1] stats graphics grDevices utils datasets methods base
#
# other attached packages:
# [1] cowplot_1.1.3 ggplot2_3.5.0 uwot_0.1.16 hdf5r_1.3.11 Matrix_1.6-5
#
# loaded via a namespace (and not attached):
# [1] bit_4.0.5 gtable_0.3.4 jsonlite_1.8.8 dplyr_1.1.4
# [5] compiler_4.3.3 promises_1.2.1 tidyselect_1.2.1 Rcpp_1.0.12
# [9] stringr_1.5.1 git2r_0.33.0 later_1.3.2 jquerylib_0.1.4
# [13] scales_1.3.0 yaml_2.3.8 fastmap_1.1.1 lattice_0.22-5
# [17] R6_2.5.1 generics_0.1.3 workflowr_1.7.1 knitr_1.45
# [21] tibble_3.2.1 munsell_0.5.0 rprojroot_2.0.4 bslib_0.6.1
# [25] pillar_1.9.0 rlang_1.1.3 utf8_1.2.4 cachem_1.0.8
# [29] stringi_1.8.3 httpuv_1.6.14 xfun_0.42 fs_1.6.3
# [33] sass_0.4.8 bit64_4.0.5 cli_3.6.2 withr_3.0.0
# [37] magrittr_2.0.3 digest_0.6.34 grid_4.3.3 lifecycle_1.0.4
# [41] vctrs_0.6.5 evaluate_0.23 glue_1.7.0 whisker_0.4.1
# [45] colorspace_2.1-0 fansi_1.0.6 rmarkdown_2.26 tools_4.3.3
# [49] pkgconfig_2.0.3 htmltools_0.5.7