Last updated: 2024-12-24
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | f0f7a57 | Dave Tang | 2024-12-24 | Finding Markers with Seurat |
Use the Peripheral Blood Mononuclear Cells (PBMCs) 2,700 cells dataset to test finding markers with Seurat.
Install the following packages, if necessary.
install.packages("remotes")
remotes::install_github("immunogenomics/presto")
install.packages("Seurat")
install.packages("bench")Load Seurat and bench for some
benchmarking.
suppressPackageStartupMessages(library("Seurat"))
suppressPackageStartupMessages(library("bench"))
suppressPackageStartupMessages(library("presto"))To follow the tutorial, you’ll need the 10X data, which can be download from AWS.
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.gzLoad 10x data into a matrix using Read10X().
pbmc.data <- Read10X(
data.dir = "data/pbmc3k/filtered_gene_bc_matrices/hg19/"
)Create the Seurat object using CreateSeuratObject; see
?SeuratObject for more information on the class.
seurat_obj <- CreateSeuratObject(
counts = pbmc.data,
min.cells = 3,
min.features = 200,
project = "pbmc3k"
)Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')class(seurat_obj)[1] "Seurat"
attr(,"package")
[1] "SeuratObject"Run the workflow as separate steps; they can be piped together but sometimes errors occur, so it is useful to split up the steps.
debug_flag <- FALSE
seurat_obj <- NormalizeData(seurat_obj, normalization.method = "LogNormalize", scale.factor = 1e4, verbose = debug_flag)
seurat_obj <- FindVariableFeatures(seurat_obj, selection.method = 'vst', nfeatures = 2000, verbose = debug_flag)
seurat_obj <- ScaleData(seurat_obj, verbose = debug_flag)
seurat_obj <- RunPCA(seurat_obj, verbose = debug_flag)
seurat_obj <- RunUMAP(seurat_obj, dims = 1:30, verbose = debug_flag)Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per sessionseurat_obj <- FindNeighbors(seurat_obj, dims = 1:30, verbose = debug_flag)
seurat_obj <- FindClusters(seurat_obj, resolution = 0.5, verbose = debug_flag)
seurat_objAn object of class Seurat
13714 features across 2700 samples within 1 assay
Active assay: RNA (13714 features, 2000 variable features)
3 layers present: counts, data, scale.data
2 dimensional reductions calculated: pca, umapFindAllMarkers() will find markers (differentially
expressed genes) for each of the identity classes in a dataset.
levels(Idents(seurat_obj))[1] "0" "1" "2" "3" "4" "5" "6" "7"Find all markers.
all_markers <- FindAllMarkers(seurat_obj, verbose = debug_flag)
dim(all_markers)[1] 17899 7FindMarkers() finds markers (differentially expressed
genes) for identity classes.
cluster_0_markers <- FindMarkers(seurat_obj, ident.1 = "0")
dim(cluster_0_markers)[1] 8434 5Cluster 0 markers from FindAllMarkers().
all_markers |>
dplyr::filter(cluster == 0) |>
dim()[1] 3139 7The start of the results are the same.
head(cluster_0_markers) p_val avg_log2FC pct.1 pct.2 p_val_adj
LDHB 1.547138e-240 1.9351689 0.922 0.473 2.121746e-236
RPS12 3.595829e-228 0.8665851 1.000 0.987 4.931320e-224
CD74 2.127919e-225 -3.1636831 0.735 0.925 2.918227e-221
HLA-DRB1 3.113535e-225 -4.3722870 0.129 0.715 4.269901e-221
CYBA 2.054958e-213 -1.8108145 0.730 0.933 2.818169e-209
HLA-DRA 7.109002e-213 -4.6393725 0.291 0.765 9.749286e-209all_markers |>
dplyr::filter(cluster == 0) |>
dplyr::select(-cluster, -gene) |>
head() p_val avg_log2FC pct.1 pct.2 p_val_adj
LDHB 1.547138e-240 1.9351689 0.922 0.473 2.121746e-236
RPS12 3.595829e-228 0.8665851 1.000 0.987 4.931320e-224
CD74 2.127919e-225 -3.1636831 0.735 0.925 2.918227e-221
HLA-DRB1 3.113535e-225 -4.3722870 0.129 0.715 4.269901e-221
CYBA 2.054958e-213 -1.8108145 0.730 0.933 2.818169e-209
HLA-DRA 7.109002e-213 -4.6393725 0.291 0.765 9.749286e-209The tail of the results are the same too, except that in
FindAllMarkers() results have been trimmed.
cluster_0_markers[3134:3139, ] p_val avg_log2FC pct.1 pct.2 p_val_adj
SCML1 0.009913768 1.2125839 0.028 0.014 1
CGGBP1 0.009914211 0.3048076 0.152 0.117 1
CCT3 0.009950407 0.2610577 0.231 0.190 1
ZNF32 0.009955859 0.1339321 0.108 0.079 1
RNF214 0.009977100 0.8208791 0.043 0.025 1
P2RX7 0.009979523 -1.7709166 0.003 0.013 1all_markers |>
dplyr::filter(cluster == 0) |>
dplyr::select(-cluster, -gene) |>
tail() p_val avg_log2FC pct.1 pct.2 p_val_adj
SCML1 0.009913768 1.2125839 0.028 0.014 1
CGGBP1 0.009914211 0.3048076 0.152 0.117 1
CCT3 0.009950407 0.2610577 0.231 0.190 1
ZNF32 0.009955859 0.1339321 0.108 0.079 1
RNF214 0.009977100 0.8208791 0.043 0.025 1
P2RX7 0.009979523 -1.7709166 0.003 0.013 1Trimming seems to be from p_val < 0.01
cluster_0_markers[3139:3142, ] p_val avg_log2FC pct.1 pct.2 p_val_adj
P2RX7 0.009979523 -1.7709166 0.003 0.013 1
CBFB 0.010029322 0.6492086 0.068 0.046 1
ATF6B 0.010045052 -0.4047457 0.130 0.165 1
PCNT 0.010051913 -1.8088730 0.003 0.013 1
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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] presto_1.0.0 data.table_1.16.2 Rcpp_1.0.13 bench_1.1.3
[5] Seurat_5.1.0 SeuratObject_5.0.2 sp_2.1-4 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] RColorBrewer_1.1-3 rstudioapi_0.17.1 jsonlite_1.8.9
[4] magrittr_2.0.3 spatstat.utils_3.1-0 farver_2.1.2
[7] rmarkdown_2.28 fs_1.6.4 vctrs_0.6.5
[10] ROCR_1.0-11 spatstat.explore_3.3-3 htmltools_0.5.8.1
[13] sass_0.4.9 sctransform_0.4.1 parallelly_1.38.0
[16] KernSmooth_2.23-24 bslib_0.8.0 htmlwidgets_1.6.4
[19] ica_1.0-3 plyr_1.8.9 plotly_4.10.4
[22] zoo_1.8-12 cachem_1.1.0 whisker_0.4.1
[25] igraph_2.1.2 mime_0.12 lifecycle_1.0.4
[28] pkgconfig_2.0.3 Matrix_1.7-0 R6_2.5.1
[31] fastmap_1.2.0 fitdistrplus_1.2-1 future_1.34.0
[34] shiny_1.9.1 digest_0.6.37 colorspace_2.1-1
[37] patchwork_1.3.0 ps_1.8.1 rprojroot_2.0.4
[40] tensor_1.5 RSpectra_0.16-2 irlba_2.3.5.1
[43] progressr_0.15.0 fansi_1.0.6 spatstat.sparse_3.1-0
[46] httr_1.4.7 polyclip_1.10-7 abind_1.4-8
[49] compiler_4.4.1 withr_3.0.2 fastDummies_1.7.4
[52] R.utils_2.12.3 MASS_7.3-60.2 tools_4.4.1
[55] lmtest_0.9-40 httpuv_1.6.15 future.apply_1.11.3
[58] goftest_1.2-3 R.oo_1.27.0 glue_1.8.0
[61] callr_3.7.6 nlme_3.1-164 promises_1.3.0
[64] grid_4.4.1 Rtsne_0.17 getPass_0.2-4
[67] cluster_2.1.6 reshape2_1.4.4 generics_0.1.3
[70] gtable_0.3.6 spatstat.data_3.1-2 R.methodsS3_1.8.2
[73] tidyr_1.3.1 utf8_1.2.4 spatstat.geom_3.3-3
[76] RcppAnnoy_0.0.22 ggrepel_0.9.6 RANN_2.6.2
[79] pillar_1.9.0 stringr_1.5.1 spam_2.11-0
[82] RcppHNSW_0.6.0 later_1.3.2 splines_4.4.1
[85] dplyr_1.1.4 lattice_0.22-6 survival_3.6-4
[88] deldir_2.0-4 tidyselect_1.2.1 miniUI_0.1.1.1
[91] pbapply_1.7-2 knitr_1.48 git2r_0.35.0
[94] gridExtra_2.3 scattermore_1.2 xfun_0.48
[97] matrixStats_1.4.1 stringi_1.8.4 lazyeval_0.2.2
[100] yaml_2.3.10 evaluate_1.0.1 codetools_0.2-20
[103] tibble_3.2.1 cli_3.6.3 uwot_0.2.2
[106] xtable_1.8-4 reticulate_1.39.0 munsell_0.5.1
[109] processx_3.8.4 jquerylib_0.1.4 globals_0.16.3
[112] spatstat.random_3.3-2 png_0.1-8 spatstat.univar_3.0-1
[115] parallel_4.4.1 ggplot2_3.5.1 dotCall64_1.2
[118] listenv_0.9.1 viridisLite_0.4.2 scales_1.3.0
[121] ggridges_0.5.6 leiden_0.4.3.1 purrr_1.0.2
[124] rlang_1.1.4 cowplot_1.1.3