Last updated: 2024-12-25
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| File | Version | Author | Date | Message |
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| Rmd | ecf10e9 | Dave Tang | 2024-12-25 | FindMarkers in parallel |
| html | 612e4f9 | Dave Tang | 2024-12-24 | Build site. |
| 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 1Find markers in parallel to speed up FindAllMarkers().
Use imap() to get the name of each list (.y);
.x is each element of the list.
library(future)
library(future.apply)
clusters <- levels(Idents(seurat_obj))
plan(multisession, workers = 4)
markers <- future_lapply(
clusters,
function(x){
FindMarkers(seurat_obj, ident.1 = x)
},
future.seed = TRUE
)
names(markers) <- clusters
purrr::map(
markers,
\(x) tibble::rownames_to_column(.data = x, var = "gene") |> tibble::remove_rownames()
) |>
purrr::imap(~ dplyr::mutate(.x, cluster = .y)) |>
purrr::list_rbind() |>
dplyr::filter(p_val < 0.01) |>
dplyr::mutate(cluster = factor(cluster, levels = clusters)) |>
dplyr::select(p_val, avg_log2FC, pct.1, pct.2, p_val_adj, cluster, gene) -> all_markers_parallel
all.equal(
all_markers_parallel,
tibble::remove_rownames(all_markers)
)[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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] future.apply_1.11.3 future_1.34.0 presto_1.0.0
[4] data.table_1.16.2 Rcpp_1.0.13 bench_1.1.3
[7] Seurat_5.1.0 SeuratObject_5.0.2 sp_2.1-4
[10] 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 shiny_1.9.1
[34] digest_0.6.37 colorspace_2.1-1 patchwork_1.3.0
[37] ps_1.8.1 rprojroot_2.0.4 tensor_1.5
[40] RSpectra_0.16-2 irlba_2.3.5.1 progressr_0.15.0
[43] fansi_1.0.6 spatstat.sparse_3.1-0 httr_1.4.7
[46] polyclip_1.10-7 abind_1.4-8 compiler_4.4.1
[49] withr_3.0.2 fastDummies_1.7.4 R.utils_2.12.3
[52] MASS_7.3-60.2 tools_4.4.1 lmtest_0.9-40
[55] httpuv_1.6.15 goftest_1.2-3 R.oo_1.27.0
[58] glue_1.8.0 callr_3.7.6 nlme_3.1-164
[61] promises_1.3.0 grid_4.4.1 Rtsne_0.17
[64] getPass_0.2-4 cluster_2.1.6 reshape2_1.4.4
[67] generics_0.1.3 gtable_0.3.6 spatstat.data_3.1-2
[70] R.methodsS3_1.8.2 tidyr_1.3.1 utf8_1.2.4
[73] spatstat.geom_3.3-3 RcppAnnoy_0.0.22 ggrepel_0.9.6
[76] RANN_2.6.2 pillar_1.9.0 stringr_1.5.1
[79] spam_2.11-0 RcppHNSW_0.6.0 later_1.3.2
[82] splines_4.4.1 dplyr_1.1.4 lattice_0.22-6
[85] survival_3.6-4 deldir_2.0-4 tidyselect_1.2.1
[88] miniUI_0.1.1.1 pbapply_1.7-2 knitr_1.48
[91] git2r_0.35.0 gridExtra_2.3 scattermore_1.2
[94] xfun_0.48 matrixStats_1.4.1 stringi_1.8.4
[97] lazyeval_0.2.2 yaml_2.3.10 evaluate_1.0.1
[100] codetools_0.2-20 tibble_3.2.1 cli_3.6.3
[103] uwot_0.2.2 xtable_1.8-4 reticulate_1.39.0
[106] munsell_0.5.1 processx_3.8.4 jquerylib_0.1.4
[109] globals_0.16.3 spatstat.random_3.3-2 png_0.1-8
[112] spatstat.univar_3.0-1 parallel_4.4.1 ggplot2_3.5.1
[115] dotCall64_1.2 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_1.1.4 cowplot_1.1.3