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File | Version | Author | Date | Message |
---|---|---|---|---|
Rmd | 380f7f8 | Dave Tang | 2025-01-16 | Remove unnecessary output |
html | e2a1a6b | Dave Tang | 2025-01-16 | Build site. |
Rmd | cd0f44f | Dave Tang | 2025-01-16 | Module scores |
html | 72b0d6d | Dave Tang | 2025-01-15 | Build site. |
Rmd | bcb5c7b | Dave Tang | 2025-01-15 | Manually use {presto} to calculate pvalues |
html | 8539125 | Dave Tang | 2025-01-15 | Build site. |
Rmd | 240ee62 | Dave Tang | 2025-01-15 | Compare p-value calculations |
html | 50fef6c | Dave Tang | 2025-01-15 | Build site. |
Rmd | cb2011a | Dave Tang | 2025-01-15 | FindMarkers with groups |
html | 522ba27 | Dave Tang | 2024-12-25 | Build site. |
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"))
suppressPackageStartupMessages(library("ggplot2"))
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.gz
Load 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 session
seurat_obj <- FindNeighbors(seurat_obj, dims = 1:30, verbose = debug_flag)
seurat_obj <- FindClusters(seurat_obj, resolution = 0.5, verbose = debug_flag)
seurat_obj
An 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, umap
FindAllMarkers()
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 7
FindMarkers()
finds markers (differentially expressed
genes) for identity classes. Things to note:
data
slot/layer; this contains
normalised values (after running NormalizeData()
)ident.1
- Identity class to define markers for; pass an
object of class phylo
or clustertree
to find
markers for a node in a cluster tree; passing clustertree
requires BuildClusterTree()
to have been runident.2
- A second identity class for comparison; if
NULL, use all other cells for comparison; if an object of class
phylo
or clustertree
is passed to
ident.1
, must pass a node to find markers forgroup.by
- Regroup cells into a different identity
class prior to performing differential expressionsubset.ident
- Subset a particular identity class prior
to regrouping. Only relevant if group.by is setpbmc_small
dataset.
data(pbmc_small)
pbmc_small
An object of class Seurat
230 features across 80 samples within 1 assay
Active assay: RNA (230 features, 20 variable features)
3 layers present: counts, data, scale.data
2 dimensional reductions calculated: pca, tsne
pbmc_small
metadata.
table(
pbmc_small@meta.data$RNA_snn_res.1,
pbmc_small@meta.data$groups
)
g1 g2
0 20 16
1 14 11
2 10 9
Take all cells in cluster 2, and find markers that separate cells in the ‘g1’ group (metadata variable ‘group’).
pbmc_small_markers <- FindMarkers(pbmc_small, ident.1 = "g1", group.by = 'groups', subset.ident = "2")
head(pbmc_small_markers)
p_val avg_log2FC pct.1 pct.2 p_val_adj
GSTP1 0.01601528 2.603521 0.7 0.111 1
LINC00936 0.02048683 7.182496 0.5 0.000 1
TPM4 0.02048683 7.488007 0.5 0.000 1
LGALS2 0.04515259 7.403075 0.4 0.000 1
IFI30 0.04515259 7.794332 0.4 0.000 1
RHOC 0.04515259 7.016294 0.4 0.000 1
Perform some sanity checks.
get_exp <- function(gene){
gene_exp <- pbmc_small[['RNA']]['data'][gene, ]
pbmc_small@meta.data |>
dplyr::filter(RNA_snn_res.1 == 2, groups == 'g1') |>
row.names() -> g1_c2
pbmc_small@meta.data |>
dplyr::filter(RNA_snn_res.1 == 2, groups == 'g2') |>
row.names() -> g2_c2
g1 <- gene_exp[g1_c2]
g2 <- gene_exp[g2_c2]
rbind(
data.frame(exp = g1, group = "g1"),
data.frame(exp = g2, group = "g2")
)
}
plot_gene <- function(gene){
my_df <- get_exp(gene)
boxplot(
exp~group,
data = my_df,
main = gene
)
}
head(pbmc_small_markers, 3) |>
row.names() -> genes_to_check
sapply(genes_to_check, plot_gene) -> dev_null
Version | Author | Date |
---|---|---|
50fef6c | Dave Tang | 2025-01-15 |
Version | Author | Date |
---|---|---|
50fef6c | Dave Tang | 2025-01-15 |
Version | Author | Date |
---|---|---|
50fef6c | Dave Tang | 2025-01-15 |
Perform Wilcoxon Rank Sum and Signed Rank Tests using
wilcox.test
and compare results.
purrr::map_dbl(row.names(pbmc_small_markers), \(x){
wilcox.test(exp~group, data = get_exp(x))$p.value
}) |>
suppressWarnings() -> manual_p_values
plot(pbmc_small_markers$p_val, manual_p_values, pch = 16)
abline(a = 0, b = 1, lty = 2, col = 2)
Fast Wilcoxon rank sum test and auROC using
presto::wilcoxauc()
.
run_presto_wilcox <- function(gene){
wanted <- pbmc_small@meta.data$RNA_snn_res.1 == "2"
seurat_obj <- pbmc_small[, wanted]
seurat_obj[['RNA']]$data |>
as.matrix() -> data_mat
my_exp <- data_mat[gene, ]
my_mat <- matrix(my_exp, nrow = 1)
colnames(my_mat) <- names(my_exp)
rownames(my_mat) <- gene
y <- factor(seurat_obj@meta.data$groups)
res <- presto::wilcoxauc(my_mat, y)
res <- res[1:(nrow(x = res)/2),]
res$pval
}
purrr::map_dbl(row.names(pbmc_small_markers), run_presto_wilcox) -> presto_p_values
plot(pbmc_small_markers$p_val, presto_p_values, pch = 16)
abline(a = 0, b = 1, lty = 2, col = 2)
p-value adjustment is performed using bonferroni correction based on the total number of genes in the dataset. Other correction methods are not recommended, as Seurat pre-filters genes using the arguments above, reducing the number of tests performed. Lastly, as Aaron Lun has pointed out, p-values should be interpreted cautiously, as the genes used for clustering are the same genes tested for differential expression.
all(p.adjust(manual_p_values, method = "bonferroni") == pbmc_small_markers$p_val_adj)
[1] TRUE
Find markers for cluster 0 in pbmc3k.
cluster_0_markers <- FindMarkers(seurat_obj, ident.1 = "0")
dim(cluster_0_markers)
[1] 8434 5
Cluster 0 markers from FindAllMarkers()
.
all_markers |>
dplyr::filter(cluster == 0) |>
dim()
[1] 3139 7
The 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-209
all_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-209
The 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 1
all_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 1
Trimming 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
Find 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
The function AddModuleScore():
Calculate the average expression levels of each program (cluster) on single cell level, subtracted by the aggregated expression of control feature sets. All analyzed features are binned based on averaged expression, and the control features are randomly selected from each bin.
ctrl
- Number of control features selected from the
same bin per analyzed featurepbmc_small_markers |>
head(10) |>
row.names() -> my_features
feature_list <- list(my_features)
AddModuleScore(
object = pbmc_small,
features = feature_list,
ctrl = 5,
name = 'cluster_2_markers'
) -> pbmc_small
Plot module scores; feature_list
contains genes that are
markers for g1
within cluster 2. The boxplot confirms the
results by showing higher module scores in cluster 2 of g1.
ggplot(pbmc_small@meta.data, aes(RNA_snn_res.1, cluster_2_markers1)) +
geom_boxplot() +
theme_minimal() +
facet_grid(~groups)
Version | Author | Date |
---|---|---|
e2a1a6b | Dave Tang | 2025-01-16 |
Visualise module scores on the UMAP.
pbmc_small <- RunUMAP(object = pbmc_small, dims = 1:19, verbose = FALSE)
cbind(
pbmc_small@meta.data,
pbmc_small@reductions$umap@cell.embeddings[, 1:2]
) |>
ggplot(aes(umap_1, umap_2, colour = cluster_2_markers1, shape = RNA_snn_res.1)) +
geom_point() +
theme_minimal() +
facet_grid(~groups)
Version | Author | Date |
---|---|---|
e2a1a6b | Dave Tang | 2025-01-16 |
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 ggplot2_3.5.1
[4] presto_1.0.0 data.table_1.16.2 Rcpp_1.0.13
[7] bench_1.1.3 Seurat_5.1.0 SeuratObject_5.0.2
[10] 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 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 labeling_0.4.3
[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] highr_0.11 R.utils_2.12.3 MASS_7.3-60.2
[55] tools_4.4.1 lmtest_0.9-40 httpuv_1.6.15
[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 limma_3.62.1
[82] spam_2.11-0 RcppHNSW_0.6.0 later_1.3.2
[85] splines_4.4.1 dplyr_1.1.4 lattice_0.22-6
[88] survival_3.6-4 deldir_2.0-4 tidyselect_1.2.1
[91] miniUI_0.1.1.1 pbapply_1.7-2 knitr_1.48
[94] git2r_0.35.0 gridExtra_2.3 scattermore_1.2
[97] xfun_0.48 statmod_1.5.0 matrixStats_1.4.1
[100] stringi_1.8.4 lazyeval_0.2.2 yaml_2.3.10
[103] evaluate_1.0.1 codetools_0.2-20 tibble_3.2.1
[106] cli_3.6.3 uwot_0.2.2 xtable_1.8-4
[109] reticulate_1.39.0 munsell_0.5.1 processx_3.8.4
[112] jquerylib_0.1.4 globals_0.16.3 spatstat.random_3.3-2
[115] png_0.1-8 spatstat.univar_3.0-1 parallel_4.4.1
[118] dotCall64_1.2 listenv_0.9.1 viridisLite_0.4.2
[121] scales_1.3.0 ggridges_0.5.6 leiden_0.4.3.1
[124] purrr_1.0.2 rlang_1.1.4 cowplot_1.1.3