Getting started with Seurat
2024-03-19
Last updated: 2024-03-19
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Knit directory: muse/
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| Rmd | ae0ea42 | Dave Tang | 2024-03-19 | Getting started with Seurat |
This post follows the Peripheral Blood Mononuclear Cells (PBMCs) tutorial for 2,700 single cells. It was written while I was going through the tutorial and contains my notes. The dataset for this tutorial can be downloaded from the 10X Genomics dataset page but it is also hosted on Amazon (see below). The PBMCs, which are primary cells with relatively small amounts of RNA (around 1pg RNA/cell), come from a healthy donor. There were 2,700 cells detected and sequencing was performed on an Illumina NextSeq 500 with around 69,000 reads per cell. To get started install Seurat by using install.packages().
install.packages("Seurat")If you get the warning:
‘SeuratObject’ was built under R 4.3.0 but the current version is 4.3.2; it is recomended that you reinstall ‘SeuratObject’ as the ABI for R may have changed
re-install the SeuratObject package using a repository
that has an updated copy. The same goes for the htmltools
package.
install.packages("SeuratObject", repos = "https://cran.ism.ac.jp/")
install.packages("htmltools", repos = "https://cran.ism.ac.jp/")
packageVersion("SeuratObject")
packageVersion("htmltools")Load Seurat.
library("Seurat")Loading required package: SeuratObjectLoading required package: sp
Attaching package: 'SeuratObject'The following object is masked from 'package:base':
intersectpackageVersion("Seurat")[1] '5.0.3'Data
To follow the tutorial, you need the 10X data.
mkdir -p data/pbmc3k && cd data/pbmc3k
wget -c https://s3-us-west-2.amazonaws.com/10x.files/samples/cell/pbmc3k/pbmc3k_filtered_gene_bc_matrices.tar.gz
tar -xzf pbmc3k_filtered_gene_bc_matrices.tar.gzThe extracted files.
ls -1 data/pbmc3k/filtered_gene_bc_matrices/hg19barcodes.tsv
genes.tsv
matrix.mtxmatrix.mtx is a MatrixMarket
file. It has the following properties:
- Only non-zero entries are stored in the file
- Comments start with a
%, like LaTeX - The first line indicates the total number of rows, columns, and entries
- The following lines after the first provide a row and column number and the value at that coordinate
head data/pbmc3k/filtered_gene_bc_matrices/hg19/matrix.mtx%%MatrixMarket matrix coordinate real general
%
32738 2700 2286884
32709 1 4
32707 1 1
32706 1 10
32704 1 1
32703 1 5
32702 1 6
32700 1 10Seurat
Load 10x data into a matrix.
pbmc.data <- Read10X(data.dir = "data/pbmc3k/filtered_gene_bc_matrices/hg19/")class(pbmc.data)[1] "dgCMatrix"
attr(,"package")
[1] "Matrix"32,738 genes and 2,700 cells.
dim(pbmc.data)[1] 32738 2700Check out the first six genes and cells
pbmc.data[1:6, 1:6]6 x 6 sparse Matrix of class "dgCMatrix"
AAACATACAACCAC-1 AAACATTGAGCTAC-1 AAACATTGATCAGC-1
MIR1302-10 . . .
FAM138A . . .
OR4F5 . . .
RP11-34P13.7 . . .
RP11-34P13.8 . . .
AL627309.1 . . .
AAACCGTGCTTCCG-1 AAACCGTGTATGCG-1 AAACGCACTGGTAC-1
MIR1302-10 . . .
FAM138A . . .
OR4F5 . . .
RP11-34P13.7 . . .
RP11-34P13.8 . . .
AL627309.1 . . .Summary of total expression per single cell.
summary(colSums(pbmc.data)) Min. 1st Qu. Median Mean 3rd Qu. Max.
548 1758 2197 2367 2763 15844 Check how many genes have at least one transcript in each cell.
at_least_one <- apply(pbmc.data, 2, function(x) sum(x>0))
hist(
at_least_one,
breaks = 100,
main = "Distribution of detected genes",
xlab = "Genes with at least one tag"
)
Total expression per cell.
hist(
colSums(pbmc.data),
breaks = 100,
main = "Expression sum per cell",
xlab = "Sum expression"
)
sessionInfo()R version 4.3.2 (2023-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] Seurat_5.0.3 SeuratObject_5.0.1 sp_2.1-3 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] RColorBrewer_1.1-3 rstudioapi_0.15.0 jsonlite_1.8.8
[4] magrittr_2.0.3 spatstat.utils_3.0-4 rmarkdown_2.26
[7] fs_1.6.3 vctrs_0.6.5 ROCR_1.0-11
[10] spatstat.explore_3.2-6 htmltools_0.5.7 sass_0.4.9
[13] sctransform_0.4.1 parallelly_1.37.1 KernSmooth_2.23-22
[16] bslib_0.6.1 htmlwidgets_1.6.4 ica_1.0-3
[19] plyr_1.8.9 plotly_4.10.4 zoo_1.8-12
[22] cachem_1.0.8 whisker_0.4.1 igraph_2.0.3
[25] mime_0.12 lifecycle_1.0.4 pkgconfig_2.0.3
[28] Matrix_1.6-5 R6_2.5.1 fastmap_1.1.1
[31] fitdistrplus_1.1-11 future_1.33.1 shiny_1.8.0
[34] digest_0.6.35 colorspace_2.1-0 patchwork_1.2.0
[37] ps_1.7.6 rprojroot_2.0.4 tensor_1.5
[40] RSpectra_0.16-1 irlba_2.3.5.1 progressr_0.14.0
[43] fansi_1.0.6 spatstat.sparse_3.0-3 httr_1.4.7
[46] polyclip_1.10-6 abind_1.4-5 compiler_4.3.2
[49] fastDummies_1.7.3 highr_0.10 MASS_7.3-60
[52] tools_4.3.2 lmtest_0.9-40 httpuv_1.6.14
[55] future.apply_1.11.1 goftest_1.2-3 glue_1.7.0
[58] callr_3.7.5 nlme_3.1-163 promises_1.2.1
[61] grid_4.3.2 Rtsne_0.17 getPass_0.2-4
[64] cluster_2.1.4 reshape2_1.4.4 generics_0.1.3
[67] gtable_0.3.4 spatstat.data_3.0-4 tidyr_1.3.1
[70] data.table_1.15.2 utf8_1.2.4 spatstat.geom_3.2-9
[73] RcppAnnoy_0.0.22 ggrepel_0.9.5 RANN_2.6.1
[76] pillar_1.9.0 stringr_1.5.1 spam_2.10-0
[79] RcppHNSW_0.6.0 later_1.3.2 splines_4.3.2
[82] dplyr_1.1.4 lattice_0.21-9 survival_3.5-7
[85] deldir_2.0-4 tidyselect_1.2.1 miniUI_0.1.1.1
[88] pbapply_1.7-2 knitr_1.45 git2r_0.33.0
[91] gridExtra_2.3 scattermore_1.2 xfun_0.42
[94] matrixStats_1.2.0 stringi_1.8.3 lazyeval_0.2.2
[97] yaml_2.3.8 evaluate_0.23 codetools_0.2-19
[100] tibble_3.2.1 cli_3.6.2 uwot_0.1.16
[103] xtable_1.8-4 reticulate_1.35.0 munsell_0.5.0
[106] processx_3.8.4 jquerylib_0.1.4 Rcpp_1.0.12
[109] globals_0.16.3 spatstat.random_3.2-3 png_0.1-8
[112] parallel_4.3.2 ellipsis_0.3.2 ggplot2_3.5.0
[115] dotCall64_1.1-1 listenv_0.9.1 viridisLite_0.4.2
[118] scales_1.3.0 ggridges_0.5.6 leiden_0.4.3.1
[121] purrr_1.0.2 rlang_1.1.3 cowplot_1.1.3