Merge samples
Mechthild Lütge
14 May 2020
Last updated: 2022-04-08
Checks: 6 1
Knit directory: humanCardiacFibroblasts/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | cb18127 | mluetge | 2021-12-21 | add human sample |
| html | c967df3 | mluetge | 2021-11-10 | add two new samples |
| Rmd | 6057189 | mluetge | 2021-09-23 | add samples |
| html | 6057189 | mluetge | 2021-09-23 | add samples |
| Rmd | cf82798 | mluetge | 2021-09-03 | first merge |
| html | cf82798 | mluetge | 2021-09-03 | first merge |
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, "/metadata.txt"), col_names = T)load and assign samples
assignSamples <- function(smpNam, basedirSmp, smpTec, smpBatch, smpLoc, smpOri,
smpIso){
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 <- smpBatch
seuratSmp$location <- smpLoc
seuratSmp$origin <- smpOri
seuratSmp$isolation <- smpIso
return(seuratSmp)
}
####################################################################
for(i in 1:length(metaDat$Sample)){
seuratX <- assignSamples(smpNam = metaDat$Sample[i],
basedirSmp = basedir,
smpTec = metaDat$technique[i],
smpBatch = metaDat$ID[i],
smpLoc = metaDat$location[i],
smpOri = metaDat$origin[i],
smpIso = metaDat$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: 40657
Number of edges: 1477001
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9627
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: 40657
Number of edges: 1477001
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9340
Number of communities: 24
Elapsed time: 12 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 40657
Number of edges: 1477001
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9200
Number of communities: 26
Elapsed time: 9 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 40657
Number of edges: 1477001
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9494
Number of communities: 22
Elapsed time: 11 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$dataset))
colnames(dat) <- c("dataset", "all")
knitr::kable(dat)| dataset | all |
|---|---|
| 1_20210811_Hu_nucleoi_seq_ECMO_Heart01_Myocardium_NextGEM | 4041 |
| 1_20210930_Hu_nucseq_HTrans_Heart22_24_GEM | 2381 |
| 1_20211018_Hu_nucseq_Myocar_Heart27_GEM | 238 |
| 10_20210817_Hu_nucleoi_seq_ECMO_Heart04_Myocardium_NextGEM | 1549 |
| 10_20210907_Hu_nucseq_iDCM_Heart01_GEM | 826 |
| 11_20210819_Hu_nucleoi_seq_ECMO_Heart01_Myocardium_Frozen_NG | 3162 |
| 12_20210819_Hu_nucleoi_seq_ECMO_Heart03_Myocardium_Frozen_NG | 1469 |
| 13_20210819_Hu_nucleoi_seq_ECMO_Heart04_Myocardium_Frozen_NG | 1819 |
| 2_20210811_Hu_nucleoi_seq_ECMO_Heart01_Septum_NextGEM | 1895 |
| 2_20210930_Hu_nucseq_HTrans_Heart23_25_GEM | 3131 |
| 9_20210817_Hu_nucleoi_seq_cardiac_transplant_Heart17_biopsy_NG | 1321 |
| 9_20210907_Hu_nucseq_HTrans_Heart21_GEM | 1536 |
| o27533_1_09-9_20220203_Hu_nucseq_EMB28_GEM | 1545 |
| o27533_1_10-10_20220203_Hu_nucseq_EMB29_GEM | 1254 |
| o27533_1_11-11_20220203_Hu_nucseq_EMB30_GEM | 1242 |
| o27533_1_12-12_20220203_Hu_nucseq_EMB31_GEM | 236 |
| o27533_1_13-13_20220203_Hu_nucseq_ECMO_7R_GEM | 6063 |
| o27533_1_14-14_20220203_Hu_nucseq_ECMO_7L_GEM | 5757 |
| o27936_1_7-7_20220309_Hu_nucseq_EMB32_GEM | 1192 |
color vectors
colPal <- pal_igv()(length(levels(seurat)))
colTec <- pal_jama()(length(unique(seurat$technique)))
colSmp <- c(pal_uchicago()(8), pal_npg()(8), pal_aaas()(10))[1:length(unique(seurat$dataset))]
colLoc <- pal_npg()(length(unique(seurat$location)))
colBatch <- 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$isolation)))
names(colPal) <- levels(seurat)
names(colTec) <- unique(seurat$technique)
names(colSmp) <- unique(seurat$dataset)
names(colLoc) <- unique(seurat$location)
names(colBatch) <- unique(seurat$ID)
names(colOrig) <- unique(seurat$origin)
names(colIso) <- unique(seurat$isolation)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=colBatch)+
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")
location
DimPlot(seurat, reduction = "umap", group.by = "location", cols=colLoc)+
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/humanHearts_merged_seurat.rds"))
write.table(seurat_markers_all,
file=paste0(basedir, "/data/humanHearts_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 Catalina 10.15.7
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_2.0.2
[7] SeuratObject_4.0.4 Seurat_4.0.6
[9] forcats_0.5.1 stringr_1.4.0
[11] dplyr_1.0.8 purrr_0.3.4
[13] readr_2.1.1 tidyr_1.1.4
[15] tibble_3.1.6 ggplot2_3.3.5
[17] tidyverse_1.3.1 SingleCellExperiment_1.14.1
[19] SummarizedExperiment_1.22.0 Biobase_2.52.0
[21] GenomicRanges_1.44.0 GenomeInfoDb_1.28.4
[23] IRanges_2.26.0 S4Vectors_0.30.2
[25] BiocGenerics_0.38.0 MatrixGenerics_1.4.3
[27] matrixStats_0.61.0
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_0.15
[7] munsell_0.5.0 codetools_0.2-18 ica_1.0-2
[10] future_1.23.0 miniUI_0.1.1.1 withr_2.5.0
[13] colorspace_2.0-2 highr_0.9 knitr_1.37
[16] rstudioapi_0.13 ROCR_1.0-11 ggsignif_0.6.3
[19] tensor_1.5 listenv_0.8.0 labeling_0.4.2
[22] git2r_0.29.0 GenomeInfoDbData_1.2.6 polyclip_1.10-0
[25] farver_2.1.0 bit64_4.0.5 rprojroot_2.0.2
[28] parallelly_1.30.0 vctrs_0.3.8 generics_0.1.2
[31] xfun_0.29 R6_2.5.1 bitops_1.0-7
[34] spatstat.utils_2.3-0 DelayedArray_0.18.0 assertthat_0.2.1
[37] promises_1.2.0.1 scales_1.1.1 vroom_1.5.7
[40] gtable_0.3.0 globals_0.14.0 goftest_1.2-3
[43] workflowr_1.7.0 rlang_1.0.2 splines_4.1.0
[46] rstatix_0.7.0 lazyeval_0.2.2 spatstat.geom_2.3-1
[49] broom_0.7.11 yaml_2.2.1 reshape2_1.4.4
[52] abind_1.4-5 modelr_0.1.8 backports_1.4.1
[55] httpuv_1.6.5 tools_4.1.0 ellipsis_0.3.2
[58] spatstat.core_2.3-2 jquerylib_0.1.4 RColorBrewer_1.1-2
[61] ggridges_0.5.3 Rcpp_1.0.8.3 plyr_1.8.6
[64] zlibbioc_1.38.0 RCurl_1.98-1.5 rpart_4.1-15
[67] deldir_1.0-6 pbapply_1.5-0 cowplot_1.1.1
[70] zoo_1.8-9 haven_2.4.3 ggrepel_0.9.1
[73] cluster_2.1.2 fs_1.5.2 data.table_1.14.2
[76] RSpectra_0.16-0 scattermore_0.7 lmtest_0.9-39
[79] reprex_2.0.1 RANN_2.6.1 whisker_0.4
[82] fitdistrplus_1.1-6 hms_1.1.1 patchwork_1.1.1
[85] mime_0.12 evaluate_0.14 xtable_1.8-4
[88] readxl_1.3.1 gridExtra_2.3 compiler_4.1.0
[91] KernSmooth_2.23-20 crayon_1.5.0 htmltools_0.5.2
[94] mgcv_1.8-38 later_1.3.0 tzdb_0.2.0
[97] lubridate_1.8.0 DBI_1.1.2 dbplyr_2.1.1
[100] MASS_7.3-54 Matrix_1.4-0 car_3.0-12
[103] cli_3.2.0 igraph_1.2.11 pkgconfig_2.0.3
[106] plotly_4.10.0 spatstat.sparse_2.1-0 xml2_1.3.3
[109] bslib_0.3.1 XVector_0.32.0 rvest_1.0.2
[112] digest_0.6.29 sctransform_0.3.2 RcppAnnoy_0.0.19
[115] spatstat.data_2.1-2 rmarkdown_2.11 cellranger_1.1.0
[118] leiden_0.3.9 uwot_0.1.11 shiny_1.7.1
[121] lifecycle_1.0.1 nlme_3.1-153 jsonlite_1.8.0
[124] carData_3.0-5 limma_3.48.3 viridisLite_0.4.0
[127] fansi_1.0.2 pillar_1.7.0 lattice_0.20-45
[130] fastmap_1.1.0 httr_1.4.2 survival_3.2-13
[133] glue_1.6.2 png_0.1-7 bit_4.0.4
[136] stringi_1.7.6 sass_0.4.0 irlba_2.3.5
[139] future.apply_1.8.1 date()[1] "Fri Apr 8 10:31:58 2022"
sessionInfo()R version 4.1.0 (2021-05-18)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7
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_2.0.2
[7] SeuratObject_4.0.4 Seurat_4.0.6
[9] forcats_0.5.1 stringr_1.4.0
[11] dplyr_1.0.8 purrr_0.3.4
[13] readr_2.1.1 tidyr_1.1.4
[15] tibble_3.1.6 ggplot2_3.3.5
[17] tidyverse_1.3.1 SingleCellExperiment_1.14.1
[19] SummarizedExperiment_1.22.0 Biobase_2.52.0
[21] GenomicRanges_1.44.0 GenomeInfoDb_1.28.4
[23] IRanges_2.26.0 S4Vectors_0.30.2
[25] BiocGenerics_0.38.0 MatrixGenerics_1.4.3
[27] matrixStats_0.61.0
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_0.15
[7] munsell_0.5.0 codetools_0.2-18 ica_1.0-2
[10] future_1.23.0 miniUI_0.1.1.1 withr_2.5.0
[13] colorspace_2.0-2 highr_0.9 knitr_1.37
[16] rstudioapi_0.13 ROCR_1.0-11 ggsignif_0.6.3
[19] tensor_1.5 listenv_0.8.0 labeling_0.4.2
[22] git2r_0.29.0 GenomeInfoDbData_1.2.6 polyclip_1.10-0
[25] farver_2.1.0 bit64_4.0.5 rprojroot_2.0.2
[28] parallelly_1.30.0 vctrs_0.3.8 generics_0.1.2
[31] xfun_0.29 R6_2.5.1 bitops_1.0-7
[34] spatstat.utils_2.3-0 DelayedArray_0.18.0 assertthat_0.2.1
[37] promises_1.2.0.1 scales_1.1.1 vroom_1.5.7
[40] gtable_0.3.0 globals_0.14.0 goftest_1.2-3
[43] workflowr_1.7.0 rlang_1.0.2 splines_4.1.0
[46] rstatix_0.7.0 lazyeval_0.2.2 spatstat.geom_2.3-1
[49] broom_0.7.11 yaml_2.2.1 reshape2_1.4.4
[52] abind_1.4-5 modelr_0.1.8 backports_1.4.1
[55] httpuv_1.6.5 tools_4.1.0 ellipsis_0.3.2
[58] spatstat.core_2.3-2 jquerylib_0.1.4 RColorBrewer_1.1-2
[61] ggridges_0.5.3 Rcpp_1.0.8.3 plyr_1.8.6
[64] zlibbioc_1.38.0 RCurl_1.98-1.5 rpart_4.1-15
[67] deldir_1.0-6 pbapply_1.5-0 cowplot_1.1.1
[70] zoo_1.8-9 haven_2.4.3 ggrepel_0.9.1
[73] cluster_2.1.2 fs_1.5.2 data.table_1.14.2
[76] RSpectra_0.16-0 scattermore_0.7 lmtest_0.9-39
[79] reprex_2.0.1 RANN_2.6.1 whisker_0.4
[82] fitdistrplus_1.1-6 hms_1.1.1 patchwork_1.1.1
[85] mime_0.12 evaluate_0.14 xtable_1.8-4
[88] readxl_1.3.1 gridExtra_2.3 compiler_4.1.0
[91] KernSmooth_2.23-20 crayon_1.5.0 htmltools_0.5.2
[94] mgcv_1.8-38 later_1.3.0 tzdb_0.2.0
[97] lubridate_1.8.0 DBI_1.1.2 dbplyr_2.1.1
[100] MASS_7.3-54 Matrix_1.4-0 car_3.0-12
[103] cli_3.2.0 igraph_1.2.11 pkgconfig_2.0.3
[106] plotly_4.10.0 spatstat.sparse_2.1-0 xml2_1.3.3
[109] bslib_0.3.1 XVector_0.32.0 rvest_1.0.2
[112] digest_0.6.29 sctransform_0.3.2 RcppAnnoy_0.0.19
[115] spatstat.data_2.1-2 rmarkdown_2.11 cellranger_1.1.0
[118] leiden_0.3.9 uwot_0.1.11 shiny_1.7.1
[121] lifecycle_1.0.1 nlme_3.1-153 jsonlite_1.8.0
[124] carData_3.0-5 limma_3.48.3 viridisLite_0.4.0
[127] fansi_1.0.2 pillar_1.7.0 lattice_0.20-45
[130] fastmap_1.1.0 httr_1.4.2 survival_3.2-13
[133] glue_1.6.2 png_0.1-7 bit_4.0.4
[136] stringi_1.7.6 sass_0.4.0 irlba_2.3.5
[139] future.apply_1.8.1