scRNAseq_combined_02_HVG_Dimred
retogerber
2022-12-19
Last updated: 2022-12-19
Checks: 6 1
Knit directory: synovialscrnaseq/
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There are no past versions. Publish this analysis with wflow_publish() to start tracking its development.
Set up
suppressPackageStartupMessages({
library(dplyr)
library(ggplot2)
library(purrr)
library(stringr)
library(SummarizedExperiment)
library(SingleCellExperiment)
library(scater)
library(scran)
library(igraph)
library(SingleR)
library(scuttle)
library(celldex)
library(ggbeeswarm)
library(bluster)
library(BiocParallel)
})
n_workers <- 10
RhpcBLASctl::blas_set_num_threads(n_workers)
bpparam <- BiocParallel::MulticoreParam(workers=n_workers, RNGseed = 123)
analysis_version <- 7
here::here()[1] "/home/retger/Synovial/synovialscrnaseq"source(here::here("code","utilities_plots.R"))
set.seed(100)load data from preprocessing
syn_sce <- readRDS(file = here::here("output",paste0("combined_v",analysis_version,"_sce_filtered.rds"))) HVG subsetting
model per gene variance, get highly variable genes. Plot Mean vs. Variance of normalized log expression values.
all_gene_var <- modelGeneVar(syn_sce,
block=syn_sce$Sample)Loading required package: tidySingleCellExperiment
Attaching package: 'tidySingleCellExperiment'The following object is masked from 'package:IRanges':
sliceThe following object is masked from 'package:S4Vectors':
renameThe following object is masked from 'package:matrixStats':
countThe following objects are masked from 'package:dplyr':
bind_cols, bind_rows, countThe following object is masked from 'package:stats':
filterWarning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
collapsing to unique 'x' values
Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
collapsing to unique 'x' values
Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
collapsing to unique 'x' values
Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
collapsing to unique 'x' values
Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
collapsing to unique 'x' valueshvg <- getTopHVGs(all_gene_var, fdr.threshold=0.05)
length(hvg)[1] 1428mean_var_comb <- map(unique(syn_sce$Sample), ~ {
all_gene_var$per.block[[.x]] %>%
as_tibble %>%
mutate(row_names = rownames(all_gene_var$per.block[[.x]]),
is_hvg = row_names %in% hvg,
Sample = .x)
}) %>%
purrr::reduce(rbind)
mean_var_comb %>%
ggplot() +
geom_point(aes(x = mean, y = total, color= is_hvg)) +
geom_line(aes(x=mean, y= tech)) +
labs(y="Variance",x="Mean expression") +
facet_wrap(~Sample)
rowData(syn_sce)$is_hvg <- rownames(syn_sce) %in% hvgUpset plot
Plot set of detected genes (of HVG) for different conditions.
hvg_row_name <- "is_hvg"# Sample
tmpsce_nest_sample_unique <- syn_sce[rowData(syn_sce)[[hvg_row_name]],] %>%
nest(data=-Sample)
upsetdat_sample_unique <- purrr::map(seq_along(tmpsce_nest_sample_unique$data), ~{
ind <- rowSums(counts(tmpsce_nest_sample_unique$data[[.x]]) >0 ) >0
rownames(rowData(tmpsce_nest_sample_unique$data[[.x]]))[ind]
})
names(upsetdat_sample_unique) <- tmpsce_nest_sample_unique$Sample
ups_samp <- UpSetR::upset(UpSetR::fromList(upsetdat_sample_unique),nsets = length(upsetdat_sample_unique), nintersects = 20, order.by = "freq", mb.ratio = c(0.3,0.7))
# Protocol
tmpsce_nest_Protocol<- syn_sce[rowData(syn_sce)[[hvg_row_name]],] %>%
nest(data=-Protocol)
upsetdat_Protocol <- purrr::map(seq_along(tmpsce_nest_Protocol$data), ~{
ind <- rowSums(counts(tmpsce_nest_Protocol$data[[.x]]) >0 ) >0
rownames(rowData(tmpsce_nest_Protocol$data[[.x]]))[ind]
})
names(upsetdat_Protocol) <- tmpsce_nest_Protocol$Protocol
indmat <- UpSetR::fromList(upsetdat_Protocol)
rownames(indmat) <- unique(unlist(upsetdat_Protocol))
colnames(indmat)[1] "stephenson" "wei" combs <- expand.grid(purrr::map(seq_along(colnames(indmat)), ~c(0,1)))
colnames(combs) <- colnames(indmat)
rownames(combs) <- purrr::map_chr(seq_len(dim(combs)[1]), function(i)
purrr::map_chr(seq_len(dim(combs)[2]), ~ if_else(combs[i,.x]==0,"",colnames(combs)[.x])) %>%
stringr::str_c(collapse="_"))
combs stephenson wei
_ 0 0
stephenson_ 1 0
_wei 0 1
stephenson_wei 1 1gene_in_comb <- purrr::map(seq_len(dim(combs)[1]), function(i){
purrr::map(colnames(combs), ~(indmat[[.x]] == combs[[.x]][i])) %>%
purrr::reduce(`&`) %>%
tibble::as_tibble() %>%
dplyr::rename(!!rownames(combs)[i] := value)
}) %>%
purrr::reduce(cbind)
rownames(gene_in_comb) <- unique(unlist(upsetdat_Protocol))
colSums(gene_in_comb) _ stephenson_ _wei stephenson_wei
0 45 0 1382 upsetplot_genelists <- purrr::map(colnames(gene_in_comb), ~rownames(gene_in_comb)[gene_in_comb[[.x]]])
ups_prot <- UpSetR::upset(UpSetR::fromList(upsetdat_Protocol),nsets = length(upsetdat_Protocol), nintersects = 20, order.by = "freq", mb.ratio = c(0.3,0.7))cat("### Upsetplot detected HVG {.tabset}\n\n")Upsetplot detected HVG
cat("#### Unique Sample \n\n")Unique Sample
print(ups_samp)
cat("\n\n")cat("#### Protocol \n\n")Protocol
print(ups_prot)
cat("\n\n")cat("### {-}")saveRDS(upsetplot_genelists,here::here("output",paste0("combined_v",analysis_version,"_upsetplot_genelists.rds")))Dimensionality reduction
use intrinsicDimension to get number of PC’s to keep. Run UMAP on reduced PCA.
sce_tmp <- syn_sce[rowData(syn_sce)[[hvg_row_name]],] %>%
runPCA(name = "PCA")
ndims <- intrinsicDimension::maxLikGlobalDimEst(as.matrix(reducedDim(sce_tmp, "PCA")), k=20)
reducedDim(sce_tmp,"PCA_reduced") <- reducedDim(sce_tmp,"PCA")[,seq_len(ceiling(ndims$dim.est))]
reducedDimNames(sce_tmp)[1] "PCA" "PCA_reduced"ncol(reducedDim(sce_tmp,"PCA_reduced"))[1] 17set.seed(100)
sce_tmp <- sce_tmp %>%
runUMAP(name = "UMAP", dimred = "PCA_reduced")
reducedDim(syn_sce, "PCA") <- reducedDim(sce_tmp,"PCA")
reducedDim(syn_sce, "PCA_reduced") <- reducedDim(sce_tmp,"PCA_reduced")
reducedDim(syn_sce, "UMAP") <- reducedDim(sce_tmp,"UMAP")UMAP
Plot UMAP per sample.
set.seed(123)
shuffle <- sample(seq_len(dim(syn_sce)[2]))
plotReducedDim(syn_sce[,shuffle], "PCA", colour_by = "Sample") +
scale_color_manual(values=sample_cols(unique(syn_sce$Sample), n_split=5)) +
main_plot_theme()Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.
plotReducedDim(syn_sce[,shuffle], "PCA", colour_by = "Protocol") +
scale_color_manual(values=sample_cols(unique(syn_sce$Protocol), n_split=2, palette=viridis::viridis)) +
main_plot_theme()Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.
pca_pl_ls <- list()
colors_used <- sample_cols(unique(syn_sce$Sample), n_split=5)
ic <- 1
for(pa_id in unique(syn_sce$Sample)){
pat_filt <- syn_sce$Sample==pa_id
nr_sam_tmp <- length(unique(syn_sce[,pat_filt]$Sample))
colorid <- ic:(ic+nr_sam_tmp-1)
ic <- ic+nr_sam_tmp
ransam <- sample(seq_len(table(pat_filt)["TRUE"]))
pca_pl_ls[[pa_id]] <- #plotPCA(syn_sce %>% filter(pat_filt), colour_by="Sample", ncomponents=2) +
suppressMessages(suppressWarnings(
plotReducedDim(filter(syn_sce, pat_filt)[,ransam], "PCA", colour_by = "Sample") +
scale_color_manual(values=colors_used[colorid]) +
ggtitle(paste("Sample:",pa_id))
))
}
n_pat <- length(pca_pl_ls)
cat("### By Sample {.tabset}\n\n")By Sample
for(i in seq_len(ceiling(n_pat/4))){
pl_ind <- (((i-1)*4)+1):(((i)*4))
pl_ind <- pl_ind[pl_ind <= n_pat]
if(length(pl_ind) > 0){
cat("#### Samples: ",paste(names(pca_pl_ls)[pl_ind]),"\n\n")
print(ggpubr::ggarrange(plotlist=pca_pl_ls[pl_ind]))
cat("\n\n")
}
}Samples: S_RA1 S_RA2 S_RA3 S_RA4
Samples: S_RA5 W_RA1 W_RA2 W_RA3
Samples: W_RA4 W_RA5 W_RA6
cat("### {-}")pca_pl_ls <- list()
ic <- 1
for(pa_id in unique(syn_sce$Sample)){
pat_filt <- syn_sce$Sample==pa_id
nr_sam_tmp <- length(unique(syn_sce[,pat_filt]$Sample))
colorid <- ic:(ic+nr_sam_tmp-1)
ic <- ic+nr_sam_tmp
ransam <- sample(seq_len(table(pat_filt)["TRUE"]))
pca_pl_ls[[pa_id]] <- #plotPCA(syn_sce %>% filter(pat_filt), colour_by="Sample", ncomponents=2) +
suppressMessages(suppressWarnings(
plotReducedDim(filter(syn_sce, pat_filt)[,ransam], "UMAP", colour_by = "Sample") +
scale_color_manual(values=colors_used[colorid]) +
ggtitle(paste("Sample:",pa_id))
))
}
n_pat <- length(pca_pl_ls)
cat("### By Sample {.tabset}\n\n")By Sample
for(i in seq_len(ceiling(n_pat/4))){
pl_ind <- (((i-1)*4)+1):(((i)*4))
pl_ind <- pl_ind[pl_ind <= n_pat]
if(length(pl_ind) > 0){
cat("#### Samples: ",paste(names(pca_pl_ls)[pl_ind]),"\n\n")
print(ggpubr::ggarrange(plotlist=pca_pl_ls[pl_ind]))
cat("\n\n")
}
}Samples: S_RA1 S_RA2 S_RA3 S_RA4
Samples: S_RA5 W_RA1 W_RA2 W_RA3
Samples: W_RA4 W_RA5 W_RA6
cat("### {-}")saveRDS(syn_sce, file = here::here("output",paste0("combined_v",analysis_version,"_sce_hvg.rds")))
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04 LTS
Matrix products: default
BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so
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=C
[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
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] gdtools_0.2.3 tidySingleCellExperiment_1.0.0
[3] BiocParallel_1.24.1 bluster_1.0.0
[5] ggbeeswarm_0.6.0 celldex_1.0.0
[7] scuttle_1.0.4 SingleR_1.4.1
[9] igraph_1.2.6 scran_1.18.7
[11] scater_1.18.6 SingleCellExperiment_1.12.0
[13] SummarizedExperiment_1.20.0 Biobase_2.50.0
[15] GenomicRanges_1.42.0 GenomeInfoDb_1.26.7
[17] IRanges_2.24.1 S4Vectors_0.28.1
[19] BiocGenerics_0.36.1 MatrixGenerics_1.2.1
[21] matrixStats_0.58.0 stringr_1.4.0
[23] purrr_0.3.4 ggplot2_3.3.3
[25] dplyr_1.0.4 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.2.1
[3] AnnotationHub_2.22.1 BiocFileCache_1.14.0
[5] systemfonts_1.0.1 plyr_1.8.6
[7] lazyeval_0.2.2 digest_0.6.27
[9] htmltools_0.5.1.1 viridis_0.5.1
[11] fansi_0.4.2 magrittr_2.0.1
[13] memoise_2.0.0 openxlsx_4.2.3
[15] limma_3.46.0 svglite_1.2.3.2
[17] colorspace_2.0-0 blob_1.2.1
[19] rappdirs_0.3.3 haven_2.3.1
[21] xfun_0.21 crayon_1.4.1
[23] RCurl_1.98-1.2 jsonlite_1.7.2
[25] glue_1.4.2 gtable_0.3.0
[27] zlibbioc_1.36.0 XVector_0.30.0
[29] UpSetR_1.4.0 DelayedArray_0.16.3
[31] car_3.0-10 BiocSingular_1.6.0
[33] abind_1.4-5 scales_1.1.1
[35] DBI_1.1.1 edgeR_3.32.1
[37] rstatix_0.7.0 Rcpp_1.0.6
[39] viridisLite_0.3.0 xtable_1.8-4
[41] dqrng_0.2.1 foreign_0.8-81
[43] bit_4.0.4 rsvd_1.0.3
[45] htmlwidgets_1.5.3 httr_1.4.2
[47] yaImpute_1.0-32 ellipsis_0.3.1
[49] pkgconfig_2.0.3 farver_2.0.3
[51] uwot_0.1.10 dbplyr_2.1.0
[53] locfit_1.5-9.4 here_1.0.1
[55] tidyselect_1.1.0 labeling_0.4.2
[57] rlang_0.4.10 later_1.1.0.1
[59] AnnotationDbi_1.52.0 cellranger_1.1.0
[61] munsell_0.5.0 BiocVersion_3.12.0
[63] tools_4.0.3 cachem_1.0.4
[65] cli_2.3.0 generics_0.1.0
[67] RSQLite_2.2.3 ExperimentHub_1.16.1
[69] broom_0.7.4 evaluate_0.14
[71] fastmap_1.1.0 yaml_2.2.1
[73] RhpcBLASctl_0.20-137 knitr_1.31
[75] bit64_4.0.5 fs_1.5.0
[77] zip_2.1.1 sparseMatrixStats_1.2.1
[79] mime_0.10 compiler_4.0.3
[81] beeswarm_0.2.3 plotly_4.9.3
[83] curl_4.3 interactiveDisplayBase_1.28.0
[85] ggsignif_0.6.0 tibble_3.0.6
[87] statmod_1.4.35 stringi_1.5.3
[89] highr_0.8 RSpectra_0.16-0
[91] forcats_0.5.1 lattice_0.20-41
[93] Matrix_1.3-2 vctrs_0.3.6
[95] pillar_1.4.7 lifecycle_1.0.0
[97] BiocManager_1.30.12 RcppAnnoy_0.0.18
[99] BiocNeighbors_1.8.2 data.table_1.13.6
[101] cowplot_1.1.1 bitops_1.0-6
[103] irlba_2.3.3 httpuv_1.5.5
[105] R6_2.5.0 promises_1.2.0.1
[107] rio_0.5.16 gridExtra_2.3
[109] vipor_0.4.5 codetools_0.2-18
[111] assertthat_0.2.1 intrinsicDimension_1.2.0
[113] rprojroot_2.0.2 withr_2.4.1
[115] GenomeInfoDbData_1.2.4 hms_1.0.0
[117] grid_4.0.3 beachmat_2.6.4
[119] tidyr_1.1.2 rmarkdown_2.6
[121] DelayedMatrixStats_1.12.3 carData_3.0-4
[123] ggpubr_0.4.0 git2r_0.28.0
[125] shiny_1.6.0