Last updated: 2025-03-05
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 78c20ac | Dave Tang | 2025-03-05 | The SingleCellExperiment class |
From Introduction to Single-Cell Analysis with Bioconductor:
… the SingleCellExperiment class (from the SingleCellExperiment package) serves as the common currency for data exchange across 70+ single-cell-related Bioconductor packages. This class implements a data structure that stores all aspects of our single-cell data - gene-by-cell expression data, per-cell metadata and per-gene annotation (Figure 4.1) - and manipulate them in a synchronized manner.
Install Bioconductor packages using
BiocManager::install().
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("SingleCellExperiment")
BiocManager::install("TENxIO")
install.packages("hdf5r")
install.packages("Seurat")Load libraries.
suppressPackageStartupMessages(library(SingleCellExperiment))
suppressPackageStartupMessages(library(TENxIO))
suppressPackageStartupMessages(library(hdf5r))
suppressPackageStartupMessages(library(Seurat))Download data.
outdir <- 'data/'
pbmc5k_1_url <- "https://cf.10xgenomics.com/samples/cell-exp/9.0.0/5k_Human_Donor1_PBMC_3p_gem-x_5k_Human_Donor1_PBMC_3p_gem-x/5k_Human_Donor1_PBMC_3p_gem-x_5k_Human_Donor1_PBMC_3p_gem-x_count_sample_filtered_feature_bc_matrix.h5"
pbmc5k_2_url <- "https://cf.10xgenomics.com/samples/cell-exp/9.0.0/5k_Human_Donor2_PBMC_3p_gem-x_5k_Human_Donor2_PBMC_3p_gem-x/5k_Human_Donor2_PBMC_3p_gem-x_5k_Human_Donor2_PBMC_3p_gem-x_count_sample_filtered_feature_bc_matrix.h5"
pbmc5k_1_h5 <- paste0(outdir, basename(pbmc5k_1_url))
pbmc5k_2_h5 <- paste0(outdir, basename(pbmc5k_2_url))
download_file <- function(url, outfile){
fn <- basename(url)
if(file.exists(outfile) == FALSE){
download.file(url, destfile = outfile)
} else {
message(paste0(outfile, " already exists"))
}
}
download_file(pbmc5k_1_url, pbmc5k_1_h5)data/5k_Human_Donor1_PBMC_3p_gem-x_5k_Human_Donor1_PBMC_3p_gem-x_count_sample_filtered_feature_bc_matrix.h5 already existsdownload_file(pbmc5k_2_url, pbmc5k_2_h5)data/5k_Human_Donor2_PBMC_3p_gem-x_5k_Human_Donor2_PBMC_3p_gem-x_count_sample_filtered_feature_bc_matrix.h5 already existsCreate SingleCellExperiment files.
create_sce_obj <- function(h5){
con <- TENxH5(h5)
import(con)
}
pbmc5k_1 <- create_sce_obj(pbmc5k_1_h5)Warning in rhdf5::h5read(file, name = paste0(group, ranges), index = list(1L),
: Object 'matrix/features/interval' does not exist in this HDF5 file.pbmc5k_1class: SingleCellExperiment
dim: 38606 5710
metadata(1): TENxFile
assays(1): counts
rownames(38606): ENSG00000290825 ENSG00000243485 ... ENSG00000278817
ENSG00000277196
rowData names(3): ID Symbol Type
colnames(5710): AAACCAAAGGTGACGA-1 AAACCCTGTGACGAGT-1 ...
TGTGTTAGTCAATCGT-1 TGTGTTGAGGATCTCA-1
colData names(0):
reducedDimNames(0):
mainExpName: Gene Expression
altExpNames(0):Seurat object.
min_cells <- 10
min_features <- 500
pbmc5k_1_seurat <- Seurat::CreateSeuratObject(
counts = Seurat::Read10X_h5(pbmc5k_1_h5),
min.cells = min_cells,
min.features = min_features
)
pbmc5k_1_seuratAn object of class Seurat
20935 features across 5672 samples within 1 assay
Active assay: RNA (20935 features, 0 variable features)
1 layer present: countsFilter SingleCellExperiment object in the same way; the
order and manner of filtering is important or else you can not reproduce
Seurat’s behaviour.
filter_sce <- function(sce, min.features, min.cells){
detected_genes_in_bcs <- colSums(counts(sce) > 0)
sce <- sce[, detected_genes_in_bcs >= min.features]
detected_genes <- rowSums(counts(sce) > 0)
sce <- sce[detected_genes >= min.cells, ]
sce
}
pbmc5k_1_filtered <- filter_sce(pbmc5k_1, min_features, min_cells)
pbmc5k_1_filteredclass: SingleCellExperiment
dim: 20935 5672
metadata(1): TENxFile
assays(1): counts
rownames(20935): ENSG00000238009 ENSG00000241860 ... ENSG00000271254
ENSG00000278817
rowData names(3): ID Symbol Type
colnames(5672): AAACCAAAGGTGACGA-1 AAACCCTGTGACGAGT-1 ...
TGTGTTAGTCAATCGT-1 TGTGTTGAGGATCTCA-1
colData names(0):
reducedDimNames(0):
mainExpName: Gene Expression
altExpNames(0):Compare barcodes.
all(colnames(pbmc5k_1_seurat) == colnames(pbmc5k_1_filtered))[1] TRUE
sessionInfo()R version 4.4.1 (2024-06-14)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.5 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
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
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] Seurat_5.1.0 SeuratObject_5.0.2
[3] sp_2.1-4 hdf5r_1.3.11
[5] TENxIO_1.8.2 SingleCellExperiment_1.28.1
[7] SummarizedExperiment_1.36.0 Biobase_2.66.0
[9] GenomicRanges_1.58.0 GenomeInfoDb_1.42.3
[11] IRanges_2.40.1 S4Vectors_0.44.0
[13] BiocGenerics_0.52.0 MatrixGenerics_1.18.1
[15] matrixStats_1.4.1 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] RcppAnnoy_0.0.22 splines_4.4.1 later_1.3.2
[4] BiocIO_1.16.0 tibble_3.2.1 R.oo_1.26.0
[7] polyclip_1.10-7 fastDummies_1.7.4 lifecycle_1.0.4
[10] rprojroot_2.0.4 globals_0.16.3 processx_3.8.4
[13] lattice_0.22-6 MASS_7.3-60.2 magrittr_2.0.3
[16] plotly_4.10.4 sass_0.4.9 rmarkdown_2.28
[19] jquerylib_0.1.4 yaml_2.3.10 httpuv_1.6.15
[22] sctransform_0.4.1 spam_2.11-0 spatstat.sparse_3.1-0
[25] reticulate_1.39.0 cowplot_1.1.3 pbapply_1.7-2
[28] RColorBrewer_1.1-3 abind_1.4-8 zlibbioc_1.52.0
[31] Rtsne_0.17 purrr_1.0.2 R.utils_2.12.3
[34] git2r_0.35.0 GenomeInfoDbData_1.2.13 ggrepel_0.9.6
[37] irlba_2.3.5.1 listenv_0.9.1 spatstat.utils_3.1-0
[40] goftest_1.2-3 RSpectra_0.16-2 spatstat.random_3.3-2
[43] fitdistrplus_1.2-1 parallelly_1.38.0 leiden_0.4.3.1
[46] codetools_0.2-20 DelayedArray_0.32.0 tidyselect_1.2.1
[49] UCSC.utils_1.2.0 farver_2.1.2 spatstat.explore_3.3-3
[52] jsonlite_1.8.9 progressr_0.15.0 ggridges_0.5.6
[55] survival_3.6-4 tools_4.4.1 ica_1.0-3
[58] Rcpp_1.0.13 glue_1.8.0 gridExtra_2.3
[61] SparseArray_1.6.1 BiocBaseUtils_1.8.0 xfun_0.48
[64] HDF5Array_1.34.0 dplyr_1.1.4 fastmap_1.2.0
[67] rhdf5filters_1.18.0 fansi_1.0.6 callr_3.7.6
[70] digest_0.6.37 R6_2.5.1 mime_0.12
[73] colorspace_2.1-1 scattermore_1.2 tensor_1.5
[76] spatstat.data_3.1-2 R.methodsS3_1.8.2 utf8_1.2.4
[79] tidyr_1.3.1 generics_0.1.3 data.table_1.16.2
[82] httr_1.4.7 htmlwidgets_1.6.4 S4Arrays_1.6.0
[85] whisker_0.4.1 uwot_0.2.2 pkgconfig_2.0.3
[88] gtable_0.3.6 lmtest_0.9-40 XVector_0.46.0
[91] htmltools_0.5.8.1 dotCall64_1.2 scales_1.3.0
[94] png_0.1-8 spatstat.univar_3.0-1 knitr_1.48
[97] rstudioapi_0.17.1 tzdb_0.4.0 reshape2_1.4.4
[100] nlme_3.1-164 cachem_1.1.0 zoo_1.8-12
[103] rhdf5_2.50.2 stringr_1.5.1 KernSmooth_2.23-24
[106] parallel_4.4.1 miniUI_0.1.1.1 pillar_1.9.0
[109] grid_4.4.1 vctrs_0.6.5 RANN_2.6.2
[112] promises_1.3.0 xtable_1.8-4 cluster_2.1.6
[115] evaluate_1.0.1 readr_2.1.5 cli_3.6.3
[118] compiler_4.4.1 rlang_1.1.4 crayon_1.5.3
[121] future.apply_1.11.3 ps_1.8.1 getPass_0.2-4
[124] plyr_1.8.9 fs_1.6.4 stringi_1.8.4
[127] viridisLite_0.4.2 deldir_2.0-4 munsell_0.5.1
[130] lazyeval_0.2.2 spatstat.geom_3.3-3 Matrix_1.7-0
[133] RcppHNSW_0.6.0 hms_1.1.3 patchwork_1.3.0
[136] bit64_4.5.2 future_1.34.0 Rhdf5lib_1.28.0
[139] ggplot2_3.5.1 shiny_1.9.1 ROCR_1.0-11
[142] igraph_2.1.1 bslib_0.8.0 bit_4.5.0