6: Cell type abundance analysis
Florian Wuenenmann
3/16/2022
Last updated: 2022-04-19
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Knit directory: Abatacept_scrnaseq_mi/
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|---|---|---|---|---|
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| html | f472db4 | Florian Wuennemann | 2022-04-14 | Build site. |
| html | 6e3bb2f | Florian Wuennemann | 2022-03-25 | Build site. |
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| Rmd | cb01216 | Florian Wuennemann | 2022-03-25 | wflow_publish("analysis/6_differential_cell_type_abundance.Rmd") |
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| Rmd | d5f28d4 | Florian Wuennemann | 2022-03-24 | wflow_publish("analysis/6_differential_cell_type_abundance.Rmd") |
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Aim
In this markdown, we will use the package DASeq to try and identify differential abundant cells between control and Abatacept treated samples.
Load data
First we load our processed Seurat object.
seurat_object_filt <- readRDS("../data/2_annotated.seurat_object.rds")Prepare data
Then we will extract some metadata information that we need to run DASeq from the seurat object.
## Set sample labels
sample_treat_labels <- seurat_object_filt@meta.data %>%
group_by(orig.ident,group) %>%
select(orig.ident,group) %>%
unique()
labels_control <- subset(sample_treat_labels,group == "control")$orig.ident
labels_treatment <- subset(sample_treat_labels,group == "treatment")$orig.identRun DASeq
No we will run DASeq to identify clusters in our data that show signs of differential abundance between control and treatment samples.
## Save DA object
da_object_name <- "../results/DA_object.rds"
if(file.exists(da_object_name)){
da_cells <- readRDS(da_object_name)
}else{
da_cells <- getDAcells(
X = seurat_object_filt@reductions$pca@cell.embeddings[,1:20],
cell.labels = seurat_object_filt@meta.data$orig.ident,
labels.1 = labels_control,
labels.2 = labels_treatment,
k.vector = seq(50, 500, 50),
plot.embedding = seurat_object_filt@reductions$umap@cell.embeddings)
saveRDS(da_cells,
file = da_object_name)
}
da_cells$da.cells.plot
| Version | Author | Date |
|---|---|---|
| 7eb9cc8 | Florian Wuennemann | 2022-03-25 |
We will take a look at the randplot that gives a measure of the distribution of DA cell scores created by permutation. The lightgrey curve here represents the distribution of DA measure scores from the permutation analysis. By default, cells with scores that fall outside that distribution are selected as DA cells.
##
da_cells$rand.plot
| Version | Author | Date |
|---|---|---|
| 7eb9cc8 | Florian Wuennemann | 2022-03-25 |
Since this is a pretty relaxed filtering threshold, let’s be a bit more strict and increase this DA measure threshold a bit.
da_cells <- updateDAcells(
X = da_cells,
pred.thres = c(-0.75,0.75),
plot.embedding = seurat_object_filt@reductions$umap@cell.embeddings
)
da_cells$da.cells.plot
Now we can use the DA cells to cluster them into DA regions, i.e. regions with differential abundance from the two treatment groups.
da_regions <- getDAregion(
X = seurat_object_filt@reductions$pca@cell.embeddings[,1:20],
da.cells = da_cells,
cell.labels = seurat_object_filt@meta.data$orig.ident,
labels.1 = labels_control,
labels.2 = labels_treatment,
resolution = 0.01,
plot.embedding = seurat_object_filt@reductions$umap@cell.embeddings,
min.cell = 20
)Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')
Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')
Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')Removing 10 DA regions with cells < 20.Warning in wilcox.test.default(x = idx.label.ratio[labels.2],
idx.label.ratio[labels.1]): cannot compute exact p-value with tiesNow let’s visualize a number of different features on top of these plots. First, let’s check cell types, then the treatment group, followed by the DA score and then the DA subclusters.
## Plot different measures on top of the UMAP
DimPlot(seurat_object_filt,reduction = "umap", label = TRUE) ## UMAP with cellt types
| Version | Author | Date |
|---|---|---|
| 7eb9cc8 | Florian Wuennemann | 2022-03-25 |
DimPlot(seurat_object_filt, group.by = "group") ## UMAP with treatment group
| Version | Author | Date |
|---|---|---|
| 7eb9cc8 | Florian Wuennemann | 2022-03-25 |
da_cells$da.cells.plot
| Version | Author | Date |
|---|---|---|
| 7eb9cc8 | Florian Wuennemann | 2022-03-25 |
da_regions$da.region.plot
| Version | Author | Date |
|---|---|---|
| 7eb9cc8 | Florian Wuennemann | 2022-03-25 |
Now we can also identify genes that classify these subclusters of differentially abundant cells.
## Lets use the SeuratMarkerFinder function from DASeq to find
## Add DA regions to seruat metadata
seurat_object_filt$da <- da_regions$da.region.label
da_scores <- da_regions$DA.stat
all_da_markers <- data.frame()
## Save STG gene set
all_marker_object_name <- "../results/DA_analysis.STG_genes.rds"
if(file.exists(all_marker_object_name)){
all_da_markers <- readRDS(all_marker_object_name)
}else{
for(this_celltype in unique(seurat_object_filt$cell_type)){
print(this_celltype)
seurat_subset <- subset(seurat_object_filt,cell_type == this_celltype)
n_da_region <- length(unique(seurat_subset$da))
if(n_da_region > 1 & nrow(subset(seurat_subset@meta.data,da !=0)) > 10){
da_numbers <- setdiff(unique(seurat_subset$da),c(0))
for(da_region in da_numbers){
print(da_region)
da_score <- da_scores[as.numeric(da_region),]
if(da_score[1] > 0){
direction <- "higher_in_treatment"
}else{
direction <- "higher_in_control"
}
da_markers <- FindMarkers(object = seurat_subset,
group.by = "da",
ident.1 = da_region,
ident.2 = 0)
da_markers$da_region <- da_region
da_markers$celltype_cluster <- this_celltype
da_markers$feature <- rownames(da_markers)
da_markers <- da_markers %>%
mutate("DA_direction" = direction)
all_da_markers <- rbind(all_da_markers,da_markers)
}
}
}
saveRDS(all_da_markers,
file = all_marker_object_name)
}
all_da_markers_sig <- all_da_markers %>%
subset(p_val_adj < 0.05) %>%
arrange(p_val_adj)Plot DA region markers
for(this_daregion in unique(all_da_markers$da_region)){
## Plot Dotplot similar to original DASeq publication
da_markers <- subset(all_da_markers,da_region == this_daregion)
this_celltype <- unique(da_markers$celltype_cluster)
seurat_subset <- subset(seurat_object_filt,cell_type == this_celltype)
top_markers <- da_markers %>%
top_n(4,wt = - p_val_adj)
dotplot <- DotPlot(seurat_subset,
features = top_markers$feature,
group.by = "da") +
labs(title = this_celltype)
print(dotplot)
}
| Version | Author | Date |
|---|---|---|
| 7eb9cc8 | Florian Wuennemann | 2022-03-25 |
| Version | Author | Date |
|---|---|---|
| 7eb9cc8 | Florian Wuennemann | 2022-03-25 |
reactable(all_da_markers_sig,
resizable = TRUE, showPageSizeOptions = TRUE,
searchable = TRUE,filterable = TRUE,
onClick = "expand", highlight = TRUE)Conclusion
Using DASeq, we were able to identify several different regions of differentiall abundant (DA) cells for different subtypes of cells. From a quick cross-check, it looks like this analysis is in line with the initial finding of higher immune cells in Abatacept treated samples and higher fibroblast cell content in control samples.
sessionInfo()R version 4.1.3 (2022-03-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.4 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=de_DE.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=de_DE.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=de_DE.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] reactable_0.2.3 data.table_1.14.2 DAseq_1.0.0 forcats_0.5.1
[5] stringr_1.4.0 dplyr_1.0.8 purrr_0.3.4 readr_2.1.2
[9] tidyr_1.2.0 tibble_3.1.6 ggplot2_3.3.5 tidyverse_1.3.1
[13] SeuratObject_4.0.4 Seurat_4.1.0 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] utf8_1.2.2 reticulate_1.24 R.utils_2.11.0
[4] tidyselect_1.1.2 htmlwidgets_1.5.4 grid_4.1.3
[7] Rtsne_0.15 pROC_1.18.0 munsell_0.5.0
[10] codetools_0.2-18 ica_1.0-2 future_1.24.0
[13] miniUI_0.1.1.1 withr_2.5.0 spatstat.random_2.2-0
[16] colorspace_2.0-3 highr_0.9 knitr_1.38
[19] rstudioapi_0.13 stats4_4.1.3 ROCR_1.0-11
[22] tensor_1.5 listenv_0.8.0 labeling_0.4.2
[25] git2r_0.30.1 polyclip_1.10-0 farver_2.1.0
[28] rprojroot_2.0.3 parallelly_1.31.0 vctrs_0.4.1
[31] generics_0.1.2 ipred_0.9-12 xfun_0.30
[34] R6_2.5.1 rsvd_1.0.5 flexmix_2.3-17
[37] spatstat.utils_2.3-0 assertthat_0.2.1 promises_1.2.0.1
[40] scales_1.2.0 nnet_7.3-17 gtable_0.3.0
[43] globals_0.14.0 processx_3.5.3 goftest_1.2-3
[46] timeDate_3043.102 rlang_1.0.2 splines_4.1.3
[49] lazyeval_0.2.2 ModelMetrics_1.2.2.2 spatstat.geom_2.4-0
[52] broom_0.8.0 BiocManager_1.30.16 yaml_2.3.5
[55] reshape2_1.4.4 abind_1.4-5 modelr_0.1.8
[58] crosstalk_1.2.0 backports_1.4.1 httpuv_1.6.5
[61] caret_6.0-91 tools_4.1.3 lava_1.6.10
[64] ellipsis_0.3.2 spatstat.core_2.4-2 jquerylib_0.1.4
[67] RColorBrewer_1.1-3 proxy_0.4-26 ggridges_0.5.3
[70] Rcpp_1.0.8.3 plyr_1.8.7 ps_1.6.0
[73] rpart_4.1.16 deldir_1.0-6 pbapply_1.5-0
[76] cowplot_1.1.1 zoo_1.8-9 reactR_0.4.4
[79] haven_2.4.3 ggrepel_0.9.1 cluster_2.1.3
[82] fs_1.5.2 magrittr_2.0.3 scattermore_0.8
[85] lmtest_0.9-40 reprex_2.0.1 RANN_2.6.1
[88] whisker_0.4 fitdistrplus_1.1-8 matrixStats_0.61.0
[91] hms_1.1.1 patchwork_1.1.1 mime_0.12
[94] evaluate_0.15 xtable_1.8-4 readxl_1.4.0
[97] gridExtra_2.3 shape_1.4.6 compiler_4.1.3
[100] KernSmooth_2.23-20 crayon_1.5.1 R.oo_1.24.0
[103] htmltools_0.5.2 mgcv_1.8-40 later_1.3.0
[106] tzdb_0.3.0 lubridate_1.8.0 DBI_1.1.2
[109] dbplyr_2.1.1 MASS_7.3-56 Matrix_1.4-1
[112] cli_3.2.0 R.methodsS3_1.8.1 parallel_4.1.3
[115] gower_1.0.0 igraph_1.3.0 pkgconfig_2.0.3
[118] getPass_0.2-2 plotly_4.10.0 spatstat.sparse_2.1-0
[121] recipes_0.2.0 xml2_1.3.3 foreach_1.5.2
[124] bslib_0.3.1 hardhat_0.2.0 prodlim_2019.11.13
[127] rvest_1.0.2 callr_3.7.0 digest_0.6.29
[130] sctransform_0.3.3 RcppAnnoy_0.0.19 spatstat.data_2.1-4
[133] rmarkdown_2.13 cellranger_1.1.0 leiden_0.3.9
[136] uwot_0.1.11 modeltools_0.2-23 shiny_1.7.1
[139] lifecycle_1.0.1 nlme_3.1-157 jsonlite_1.8.0
[142] SeuratWrappers_0.3.0 viridisLite_0.4.0 fansi_1.0.3
[145] pillar_1.7.0 lattice_0.20-45 fastmap_1.1.0
[148] httr_1.4.2 survival_3.2-13 glue_1.6.2
[151] remotes_2.4.2 png_0.1-7 iterators_1.0.14
[154] glmnet_4.1-3 class_7.3-20 stringi_1.7.6
[157] sass_0.4.1 irlba_2.3.5 e1071_1.7-9
[160] future.apply_1.8.1