Merge samples
Mechthild Lütge
14 May 2020
Last updated: 2022-06-23
Checks: 6 1
Knit directory: humanCardiacFibroblasts/
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load packages
suppressPackageStartupMessages({
library(SingleCellExperiment)
library(tidyverse)
library(Seurat)
library(magrittr)
library(dplyr)
library(purrr)
library(ggplot2)
library(here)
library(runSeurat3)
library(ggsci)
library(ggpubr)
library(pheatmap)
})set dir
basedir <- here()
metaDat <- read_tsv(paste0(basedir, "/metadata2.txt"), col_names = T)load and assign samples
assignSamples <- function(smpNam, basedirSmp, smpTec, smpID, smpCond, smpOri,
smpIso, smpProc){
smpNamFull <- list.files(path = paste0(basedirSmp, "/data/humanFibroblast/"),
pattern = paste0(smpNam, ".*_seurat.rds"))
seuratSmp <- readRDS(paste0(basedirSmp, "/data/humanFibroblast/", smpNamFull))
seuratSmp$technique <- smpTec
seuratSmp$ID <- smpID
seuratSmp$cond <- smpCond
seuratSmp$origin <- smpOri
seuratSmp$isolation <- smpIso
seuratSmp$processing <- smpProc
return(seuratSmp)
}
####################################################################
for(i in 1:length(metaDat$Sample)){
seuratX <- assignSamples(smpNam = metaDat$Sample[i],
basedirSmp = basedir,
smpTec = metaDat$technique[i],
smpID = metaDat$ID[i],
smpCond = metaDat$cond[i],
smpOri = metaDat$origin[i],
smpProc = metaDat$processing[i],
smpIso = metaDat$isolation[i])
if(exists("seurat")){
seurat <- merge(x = seurat, y = seuratX, project = "humanCardiacFibro")
}else{
seurat <- seuratX
}
}
remove(seuratX)run clustering and DR and remove contaminating cells
seurat <- rerunSeurat3(seurat)Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 44788
Number of edges: 1552846
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9624
Number of communities: 17
Elapsed time: 11 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 44788
Number of edges: 1552846
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9290
Number of communities: 26
Elapsed time: 10 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 44788
Number of edges: 1552846
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9160
Number of communities: 29
Elapsed time: 9 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 44788
Number of edges: 1552846
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9469
Number of communities: 20
Elapsed time: 10 seconds#seuratSub <- subset(seurat1, subset = `MGP-BALBcJ-G0026271.Grem1` >0) ## 5 cells
#seuratSub <- subset(seurat1, subset = `MGP-BALBcJ-G0026527.Bmp2` >0) ## 14104 cells
dat <- data.frame(table(seurat$dataset))
colnames(dat) <- c("dataset", "all")
knitr::kable(dat)| dataset | all |
|---|---|
| 1_20210930_Hu_nucseq_HTrans_Heart22_24_GEM | 2381 |
| 1_20211018_Hu_nucseq_Myocar_Heart27_GEM | 238 |
| 10_20210907_Hu_nucseq_iDCM_Heart01_GEM | 826 |
| 2_20210930_Hu_nucseq_HTrans_Heart23_25_GEM | 3131 |
| 9_20210817_Hu_nucleoi_seq_cardiac_transplant_Heart17_biopsy_NG | 1321 |
| 9_20210907_Hu_nucseq_HTrans_Heart21_GEM | 1536 |
| o27533_1_09-9_20220203_Hu_nucseq_EMB28_GEM | 1545 |
| o27533_1_10-10_20220203_Hu_nucseq_EMB29_GEM | 1254 |
| o27533_1_11-11_20220203_Hu_nucseq_EMB30_GEM | 1242 |
| o27533_1_12-12_20220203_Hu_nucseq_EMB31_GEM | 236 |
| o27936_1_7-7_20220309_Hu_nucseq_EMB32_GEM | 1192 |
| o28576_1_01-1_20220525_Hu_nucseq_Graz_1_EMB_GEM | 2740 |
| o28576_1_02-2_20220525_Hu_nucseq_Graz_2_EMB_GEM | 1684 |
| o28576_1_03-3_20220525_Hu_nucseq_Graz_3_EMB_GEM | 2396 |
| o28576_1_04-4_20220525_Hu_nucseq_Graz_4_EMB_GEM | 545 |
| o28576_1_05-5_20220525_Hu_nucseq_Graz_5_EMB_GEM | 781 |
| o28576_1_06-6_20220525_Hu_nucseq_Graz_6_EMB_GEM | 491 |
| o28576_1_07-7_20220525_Hu_nucseq_Graz_7_EMB_GEM | 653 |
| o28576_1_08-8_20220525_Hu_nucseq_Graz_8_HH_GEM | 3921 |
| o28576_1_09-9_20220525_Hu_nucseq_Graz_9_HH_GEM | 3707 |
| o28576_1_10-10_20220525_Hu_nucseq_Graz_10_HH_GEM | 3731 |
| o28576_1_11-11_20220525_Hu_nucseq_Graz_11_HH_GEM | 3991 |
| o28576_1_12-12_20220525_Hu_nucseq_Graz_12_HH_GEM | 3818 |
| o28576_1_13-13_20220525_Hu_nucseq_EMB32_GEM | 1428 |
color vectors
colPal <- pal_igv()(length(levels(seurat)))
colTec <- pal_jama()(length(unique(seurat$technique)))
colSmp <- c(pal_uchicago()(8), pal_npg()(8), pal_aaas()(10))[1:length(unique(seurat$dataset))]
colCond <- pal_npg()(length(unique(seurat$cond)))
colID <- c(pal_jco()(10), pal_npg()(10))[1:length(unique(seurat$ID))]
colOrig <- pal_futurama()(length(unique(seurat$origin)))
colIso <- pal_nejm()(length(unique(seurat$isolation)))
colProc <- pal_aaas()(length(unique(seurat$processing)))
names(colPal) <- levels(seurat)
names(colTec) <- unique(seurat$technique)
names(colSmp) <- unique(seurat$dataset)
names(colCond) <- unique(seurat$cond)
names(colID) <- unique(seurat$ID)
names(colOrig) <- unique(seurat$origin)
names(colIso) <- unique(seurat$isolation)
names(colProc) <- unique(seurat$processing)vis data
clusters
DimPlot(seurat, reduction = "umap", cols=colPal)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
technique
DimPlot(seurat, reduction = "umap", group.by = "technique", cols=colTec)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
Sample
DimPlot(seurat, reduction = "umap", group.by = "dataset", cols=colSmp)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
ID
DimPlot(seurat, reduction = "umap", group.by = "ID", cols=colID)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
Origin
DimPlot(seurat, reduction = "umap", group.by = "origin", cols=colOrig)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
isolation
DimPlot(seurat, reduction = "umap", group.by = "isolation", cols=colIso)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
cond
DimPlot(seurat, reduction = "umap", group.by = "cond", cols=colCond)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
processing
DimPlot(seurat, reduction = "umap", group.by = "processing", cols=colProc)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
marker genes
seurat_markers_all <- FindAllMarkers(object = seurat, assay ="RNA",
only.pos = TRUE, min.pct = 0.25,
logfc.threshold = 0.25,
test.use = "wilcox")top 15 marker genes per cluster
cluster <- levels(seurat)
selGenesAll <- seurat_markers_all %>% group_by(cluster) %>%
top_n(-15, p_val_adj) %>%
top_n(15, avg_log2FC)
selGenesAll <- selGenesAll %>% mutate(geneIDval=gsub("^.*\\.", "", gene)) %>% filter(nchar(geneIDval)>1)
template_hm <- c(
"#### {{cl}}\n",
"```{r top marker {{cl}}, fig.height=8, fig.width=6, echo = FALSE}\n",
"selGenes <- selGenesAll %>% filter(cluster=='{{cl}}')",
"pOut <- avgHeatmap(seurat = seurat, selGenes = selGenes,
colVecIdent = colPal,
ordVec=levels(seurat),
gapVecR=NULL, gapVecC=NULL,cc=FALSE,
cr=T, condCol=F)\n",
"```\n",
"\n"
)
plots_gp <- lapply(cluster,
function(cl) knitr::knit_expand(text = template_hm)
)0
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save objects
Idents(seurat) <- seurat$seurat_clusters
saveRDS(seurat, file = paste0(basedir,
"/data/humanHeartsPlusGraz_merged_seurat.rds"))
write.table(seurat_markers_all,
file=paste0(basedir, "/data/humanHeartsPlusGraz_merged_markerGenes.txt"),
row.names = FALSE, col.names = TRUE, quote = FALSE, sep = "\t")session info
sessionInfo()R version 4.1.0 (2021-05-18)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] pheatmap_1.0.12 ggpubr_0.4.0
[3] ggsci_2.9 runSeurat3_0.1.0
[5] here_1.0.1 magrittr_2.0.3
[7] sp_1.4-7 SeuratObject_4.1.0
[9] Seurat_4.1.1 forcats_0.5.1
[11] stringr_1.4.0 dplyr_1.0.9
[13] purrr_0.3.4 readr_2.1.2
[15] tidyr_1.2.0 tibble_3.1.7
[17] ggplot2_3.3.6 tidyverse_1.3.1
[19] SingleCellExperiment_1.14.1 SummarizedExperiment_1.22.0
[21] Biobase_2.52.0 GenomicRanges_1.44.0
[23] GenomeInfoDb_1.28.4 IRanges_2.26.0
[25] S4Vectors_0.30.2 BiocGenerics_0.38.0
[27] MatrixGenerics_1.4.3 matrixStats_0.62.0
loaded via a namespace (and not attached):
[1] utf8_1.2.2 reticulate_1.24 tidyselect_1.1.2
[4] htmlwidgets_1.5.4 grid_4.1.0 Rtsne_0.16
[7] munsell_0.5.0 codetools_0.2-18 ica_1.0-2
[10] future_1.25.0 miniUI_0.1.1.1 withr_2.5.0
[13] spatstat.random_2.2-0 colorspace_2.0-3 progressr_0.10.0
[16] highr_0.9 knitr_1.39 rstudioapi_0.13
[19] ROCR_1.0-11 ggsignif_0.6.3 tensor_1.5
[22] listenv_0.8.0 labeling_0.4.2 git2r_0.30.1
[25] GenomeInfoDbData_1.2.6 polyclip_1.10-0 farver_2.1.0
[28] bit64_4.0.5 rprojroot_2.0.3 parallelly_1.31.1
[31] vctrs_0.4.1 generics_0.1.2 xfun_0.30
[34] R6_2.5.1 bitops_1.0-7 spatstat.utils_2.3-1
[37] DelayedArray_0.18.0 assertthat_0.2.1 promises_1.2.0.1
[40] scales_1.2.0 vroom_1.5.7 rgeos_0.5-9
[43] gtable_0.3.0 globals_0.15.0 goftest_1.2-3
[46] workflowr_1.7.0 rlang_1.0.2 splines_4.1.0
[49] rstatix_0.7.0 lazyeval_0.2.2 spatstat.geom_2.4-0
[52] broom_0.8.0 yaml_2.3.5 reshape2_1.4.4
[55] abind_1.4-5 modelr_0.1.8 backports_1.4.1
[58] httpuv_1.6.5 tools_4.1.0 ellipsis_0.3.2
[61] spatstat.core_2.4-2 jquerylib_0.1.4 RColorBrewer_1.1-3
[64] ggridges_0.5.3 Rcpp_1.0.8.3 plyr_1.8.7
[67] zlibbioc_1.38.0 RCurl_1.98-1.6 rpart_4.1.16
[70] deldir_1.0-6 pbapply_1.5-0 cowplot_1.1.1
[73] zoo_1.8-10 haven_2.5.0 ggrepel_0.9.1
[76] cluster_2.1.3 fs_1.5.2 data.table_1.14.2
[79] RSpectra_0.16-1 scattermore_0.8 lmtest_0.9-40
[82] reprex_2.0.1 RANN_2.6.1 fitdistrplus_1.1-8
[85] hms_1.1.1 patchwork_1.1.1 mime_0.12
[88] evaluate_0.15 xtable_1.8-4 readxl_1.4.0
[91] gridExtra_2.3 compiler_4.1.0 KernSmooth_2.23-20
[94] crayon_1.5.1 htmltools_0.5.2 mgcv_1.8-40
[97] later_1.3.0 tzdb_0.3.0 lubridate_1.8.0
[100] DBI_1.1.2 dbplyr_2.1.1 MASS_7.3-57
[103] Matrix_1.4-1 car_3.0-13 cli_3.3.0
[106] igraph_1.3.1 pkgconfig_2.0.3 plotly_4.10.0
[109] spatstat.sparse_2.1-1 xml2_1.3.3 bslib_0.3.1
[112] XVector_0.32.0 rvest_1.0.2 digest_0.6.29
[115] sctransform_0.3.3 RcppAnnoy_0.0.19 spatstat.data_2.2-0
[118] rmarkdown_2.14 cellranger_1.1.0 leiden_0.3.10
[121] uwot_0.1.11 shiny_1.7.1 lifecycle_1.0.1
[124] nlme_3.1-157 jsonlite_1.8.0 carData_3.0-5
[127] limma_3.48.3 viridisLite_0.4.0 fansi_1.0.3
[130] pillar_1.7.0 lattice_0.20-45 fastmap_1.1.0
[133] httr_1.4.3 survival_3.3-1 glue_1.6.2
[136] png_0.1-7 bit_4.0.4 stringi_1.7.6
[139] sass_0.4.1 irlba_2.3.5 future.apply_1.9.0 date()[1] "Thu Jun 23 14:56:37 2022"
sessionInfo()R version 4.1.0 (2021-05-18)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] pheatmap_1.0.12 ggpubr_0.4.0
[3] ggsci_2.9 runSeurat3_0.1.0
[5] here_1.0.1 magrittr_2.0.3
[7] sp_1.4-7 SeuratObject_4.1.0
[9] Seurat_4.1.1 forcats_0.5.1
[11] stringr_1.4.0 dplyr_1.0.9
[13] purrr_0.3.4 readr_2.1.2
[15] tidyr_1.2.0 tibble_3.1.7
[17] ggplot2_3.3.6 tidyverse_1.3.1
[19] SingleCellExperiment_1.14.1 SummarizedExperiment_1.22.0
[21] Biobase_2.52.0 GenomicRanges_1.44.0
[23] GenomeInfoDb_1.28.4 IRanges_2.26.0
[25] S4Vectors_0.30.2 BiocGenerics_0.38.0
[27] MatrixGenerics_1.4.3 matrixStats_0.62.0
loaded via a namespace (and not attached):
[1] utf8_1.2.2 reticulate_1.24 tidyselect_1.1.2
[4] htmlwidgets_1.5.4 grid_4.1.0 Rtsne_0.16
[7] munsell_0.5.0 codetools_0.2-18 ica_1.0-2
[10] future_1.25.0 miniUI_0.1.1.1 withr_2.5.0
[13] spatstat.random_2.2-0 colorspace_2.0-3 progressr_0.10.0
[16] highr_0.9 knitr_1.39 rstudioapi_0.13
[19] ROCR_1.0-11 ggsignif_0.6.3 tensor_1.5
[22] listenv_0.8.0 labeling_0.4.2 git2r_0.30.1
[25] GenomeInfoDbData_1.2.6 polyclip_1.10-0 farver_2.1.0
[28] bit64_4.0.5 rprojroot_2.0.3 parallelly_1.31.1
[31] vctrs_0.4.1 generics_0.1.2 xfun_0.30
[34] R6_2.5.1 bitops_1.0-7 spatstat.utils_2.3-1
[37] DelayedArray_0.18.0 assertthat_0.2.1 promises_1.2.0.1
[40] scales_1.2.0 vroom_1.5.7 rgeos_0.5-9
[43] gtable_0.3.0 globals_0.15.0 goftest_1.2-3
[46] workflowr_1.7.0 rlang_1.0.2 splines_4.1.0
[49] rstatix_0.7.0 lazyeval_0.2.2 spatstat.geom_2.4-0
[52] broom_0.8.0 yaml_2.3.5 reshape2_1.4.4
[55] abind_1.4-5 modelr_0.1.8 backports_1.4.1
[58] httpuv_1.6.5 tools_4.1.0 ellipsis_0.3.2
[61] spatstat.core_2.4-2 jquerylib_0.1.4 RColorBrewer_1.1-3
[64] ggridges_0.5.3 Rcpp_1.0.8.3 plyr_1.8.7
[67] zlibbioc_1.38.0 RCurl_1.98-1.6 rpart_4.1.16
[70] deldir_1.0-6 pbapply_1.5-0 cowplot_1.1.1
[73] zoo_1.8-10 haven_2.5.0 ggrepel_0.9.1
[76] cluster_2.1.3 fs_1.5.2 data.table_1.14.2
[79] RSpectra_0.16-1 scattermore_0.8 lmtest_0.9-40
[82] reprex_2.0.1 RANN_2.6.1 fitdistrplus_1.1-8
[85] hms_1.1.1 patchwork_1.1.1 mime_0.12
[88] evaluate_0.15 xtable_1.8-4 readxl_1.4.0
[91] gridExtra_2.3 compiler_4.1.0 KernSmooth_2.23-20
[94] crayon_1.5.1 htmltools_0.5.2 mgcv_1.8-40
[97] later_1.3.0 tzdb_0.3.0 lubridate_1.8.0
[100] DBI_1.1.2 dbplyr_2.1.1 MASS_7.3-57
[103] Matrix_1.4-1 car_3.0-13 cli_3.3.0
[106] igraph_1.3.1 pkgconfig_2.0.3 plotly_4.10.0
[109] spatstat.sparse_2.1-1 xml2_1.3.3 bslib_0.3.1
[112] XVector_0.32.0 rvest_1.0.2 digest_0.6.29
[115] sctransform_0.3.3 RcppAnnoy_0.0.19 spatstat.data_2.2-0
[118] rmarkdown_2.14 cellranger_1.1.0 leiden_0.3.10
[121] uwot_0.1.11 shiny_1.7.1 lifecycle_1.0.1
[124] nlme_3.1-157 jsonlite_1.8.0 carData_3.0-5
[127] limma_3.48.3 viridisLite_0.4.0 fansi_1.0.3
[130] pillar_1.7.0 lattice_0.20-45 fastmap_1.1.0
[133] httr_1.4.3 survival_3.3-1 glue_1.6.2
[136] png_0.1-7 bit_4.0.4 stringi_1.7.6
[139] sass_0.4.1 irlba_2.3.5 future.apply_1.9.0