Merge samples
Mechthild Lütge
14 May 2020
Last updated: 2022-09-22
Checks: 6 1
Knit directory: humanCardiacFibroblasts/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | cd902dd | mluetge | 2022-09-12 | add human cardiac samples GZ and SG |
| html | cd902dd | mluetge | 2022-09-12 | add human cardiac samples GZ and SG |
| Rmd | d9e1a98 | mluetge | 2022-07-04 | integrate samples Graz |
| html | d9e1a98 | mluetge | 2022-07-04 | integrate samples Graz |
| Rmd | 9fc92e5 | mluetge | 2022-06-23 | add samples from Graz |
| html | 9fc92e5 | mluetge | 2022-06-23 | add samples from Graz |
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: 71279
Number of edges: 2341724
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9617
Number of communities: 15
Elapsed time: 17 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 71279
Number of edges: 2341724
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9273
Number of communities: 23
Elapsed time: 20 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 71279
Number of edges: 2341724
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9122
Number of communities: 28
Elapsed time: 17 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 71279
Number of edges: 2341724
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9448
Number of communities: 20
Elapsed time: 17 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 |
|---|---|
| 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_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 |
| o292731_1-1_20220818_Hu_nucseq_Graz_9_HH_GEM | 4908 |
| o292731_2-2_20220818_Hu_nucseq_Graz_13_HH_GEM | 9882 |
| o292731_3-3_20220818_Hu_nucseq_SG_33_EMB_GEM | 6286 |
| o292731_4-4_20220818_Hu_nucseq_SG_34_EMB_GEM | 620 |
| o292731_5-5_20220818_Hu_nucseq_SG_35_EMB_GEM | 2363 |
| o294781_01-1_20220912_Hu_nucseq_Graz_21_HH_GEM | 1442 |
| o294781_02-2_20220912_Hu_nucseq_Graz_22_HH_GEM | 1998 |
| o294781_03-3_20220912_Hu_nucseq_Graz_23_HH_GEM | 841 |
| o294781_04-4_20220912_Hu_nucseq_Graz_24_HH_GEM | 1480 |
| o294781_05-5_20220912_Hu_nucseq_Graz_14_EMB_GEM | 1268 |
| o294781_06-6_20220912_Hu_nucseq_Graz_15_EMB_GEM | 4439 |
| o294781_07-7_20220912_Hu_nucseq_Graz_16_EMB_GEM | 436 |
| o294781_08-8_20220912_Hu_nucseq_Graz_17_EMB_GEM | 1370 |
| o294781_09-9_20220912_Hu_nucseq_Graz_18_EMB_GEM | 2280 |
| o294781_10-10_20220912_Hu_nucseq_Graz_19_EMB_GEM | 111 |
| o294781_11-11_20220912_Hu_nucseq_Graz_20_EMB_GEM | 2706 |
color vectors
colPal <- c(pal_igv()(12),
pal_aaas()(10))[1:length(levels(seurat))]
colTec <- pal_jama()(length(unique(seurat$technique)))
colSmp <- c(pal_uchicago()(9), pal_npg()(10), pal_aaas()(10),
pal_jama()(7))[1:length(unique(seurat$dataset))]
colCond <- pal_npg()(length(unique(seurat$cond)))
colID <- c(pal_jco()(10), pal_npg()(10), pal_futurama()(10),
pal_d3()(10))[1:length(unique(seurat$ID))]
colOrig <- pal_aaas()(length(unique(seurat$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$processing)vis data
clusters
DimPlot(seurat, reduction = "umap", cols=colPal)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
technique
DimPlot(seurat, reduction = "umap", group.by = "technique", cols=colTec)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
Sample
DimPlot(seurat, reduction = "umap", group.by = "dataset", cols=colSmp)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
ID
DimPlot(seurat, reduction = "umap", group.by = "ID", cols=colID)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
Origin
DimPlot(seurat, reduction = "umap", group.by = "origin", cols=colOrig)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
isolation
DimPlot(seurat, reduction = "umap", group.by = "isolation", cols=colIso)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
cond
DimPlot(seurat, reduction = "umap", group.by = "cond", cols=colCond)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
processing
DimPlot(seurat, reduction = "umap", group.by = "processing", cols=colProc)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
save objects
Idents(seurat) <- seurat$seurat_clusters
saveRDS(seurat, file = paste0(basedir,
"/data/humanHeartsPlusGraz_merged_seurat.rds"))session info
sessionInfo()R version 4.2.1 (2022-06-23)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur ... 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/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] stats4 stats graphics grDevices utils datasets methods
[8] 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_1.5-0 SeuratObject_4.1.1
[9] Seurat_4.1.1 forcats_0.5.2
[11] stringr_1.4.1 dplyr_1.0.10
[13] purrr_0.3.4 readr_2.1.2
[15] tidyr_1.2.1 tibble_3.1.8
[17] ggplot2_3.3.6 tidyverse_1.3.2
[19] SingleCellExperiment_1.18.0 SummarizedExperiment_1.26.1
[21] Biobase_2.56.0 GenomicRanges_1.48.0
[23] GenomeInfoDb_1.32.4 IRanges_2.30.1
[25] S4Vectors_0.34.0 BiocGenerics_0.42.0
[27] MatrixGenerics_1.8.1 matrixStats_0.62.0
loaded via a namespace (and not attached):
[1] utf8_1.2.2 reticulate_1.26 tidyselect_1.1.2
[4] htmlwidgets_1.5.4 grid_4.2.1 Rtsne_0.16
[7] munsell_0.5.0 codetools_0.2-18 ica_1.0-3
[10] future_1.28.0 miniUI_0.1.1.1 withr_2.5.0
[13] spatstat.random_2.2-0 colorspace_2.0-3 progressr_0.11.0
[16] highr_0.9 knitr_1.40 rstudioapi_0.14
[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_1.2.8 polyclip_1.10-0 farver_2.1.1
[28] bit64_4.0.5 rprojroot_2.0.3 parallelly_1.32.1
[31] vctrs_0.4.1 generics_0.1.3 xfun_0.32
[34] R6_2.5.1 bitops_1.0-7 spatstat.utils_2.3-1
[37] cachem_1.0.6 DelayedArray_0.22.0 assertthat_0.2.1
[40] vroom_1.5.7 promises_1.2.0.1 scales_1.2.1
[43] googlesheets4_1.0.1 rgeos_0.5-9 gtable_0.3.1
[46] globals_0.16.1 goftest_1.2-3 workflowr_1.7.0
[49] rlang_1.0.5 splines_4.2.1 rstatix_0.7.0
[52] lazyeval_0.2.2 gargle_1.2.0 spatstat.geom_2.4-0
[55] broom_1.0.1 yaml_2.3.5 reshape2_1.4.4
[58] abind_1.4-5 modelr_0.1.9 backports_1.4.1
[61] httpuv_1.6.6 tools_4.2.1 ellipsis_0.3.2
[64] spatstat.core_2.4-4 jquerylib_0.1.4 RColorBrewer_1.1-3
[67] ggridges_0.5.3 Rcpp_1.0.9 plyr_1.8.7
[70] zlibbioc_1.42.0 RCurl_1.98-1.8 rpart_4.1.16
[73] deldir_1.0-6 pbapply_1.5-0 cowplot_1.1.1
[76] zoo_1.8-10 haven_2.5.1 ggrepel_0.9.1
[79] cluster_2.1.4 fs_1.5.2 data.table_1.14.2
[82] scattermore_0.8 lmtest_0.9-40 reprex_2.0.2
[85] RANN_2.6.1 googledrive_2.0.0 whisker_0.4
[88] fitdistrplus_1.1-8 hms_1.1.2 patchwork_1.1.2
[91] mime_0.12 evaluate_0.16 xtable_1.8-4
[94] readxl_1.4.1 gridExtra_2.3 compiler_4.2.1
[97] KernSmooth_2.23-20 crayon_1.5.1 htmltools_0.5.3
[100] mgcv_1.8-40 later_1.3.0 tzdb_0.3.0
[103] lubridate_1.8.0 DBI_1.1.3 dbplyr_2.2.1
[106] MASS_7.3-58.1 Matrix_1.4-1 car_3.1-0
[109] cli_3.4.0 parallel_4.2.1 igraph_1.3.4
[112] pkgconfig_2.0.3 plotly_4.10.0 spatstat.sparse_2.1-1
[115] xml2_1.3.3 bslib_0.4.0 XVector_0.36.0
[118] rvest_1.0.3 digest_0.6.29 sctransform_0.3.4
[121] RcppAnnoy_0.0.19 spatstat.data_2.2-0 rmarkdown_2.16
[124] cellranger_1.1.0 leiden_0.4.2 uwot_0.1.14
[127] shiny_1.7.2 lifecycle_1.0.2 nlme_3.1-159
[130] jsonlite_1.8.0 carData_3.0-5 viridisLite_0.4.1
[133] fansi_1.0.3 pillar_1.8.1 lattice_0.20-45
[136] fastmap_1.1.0 httr_1.4.4 survival_3.4-0
[139] glue_1.6.2 png_0.1-7 bit_4.0.4
[142] stringi_1.7.8 sass_0.4.2 irlba_2.3.5
[145] future.apply_1.9.0 date()[1] "Thu Sep 22 16:01:11 2022"
sessionInfo()R version 4.2.1 (2022-06-23)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur ... 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/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] stats4 stats graphics grDevices utils datasets methods
[8] 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_1.5-0 SeuratObject_4.1.1
[9] Seurat_4.1.1 forcats_0.5.2
[11] stringr_1.4.1 dplyr_1.0.10
[13] purrr_0.3.4 readr_2.1.2
[15] tidyr_1.2.1 tibble_3.1.8
[17] ggplot2_3.3.6 tidyverse_1.3.2
[19] SingleCellExperiment_1.18.0 SummarizedExperiment_1.26.1
[21] Biobase_2.56.0 GenomicRanges_1.48.0
[23] GenomeInfoDb_1.32.4 IRanges_2.30.1
[25] S4Vectors_0.34.0 BiocGenerics_0.42.0
[27] MatrixGenerics_1.8.1 matrixStats_0.62.0
loaded via a namespace (and not attached):
[1] utf8_1.2.2 reticulate_1.26 tidyselect_1.1.2
[4] htmlwidgets_1.5.4 grid_4.2.1 Rtsne_0.16
[7] munsell_0.5.0 codetools_0.2-18 ica_1.0-3
[10] future_1.28.0 miniUI_0.1.1.1 withr_2.5.0
[13] spatstat.random_2.2-0 colorspace_2.0-3 progressr_0.11.0
[16] highr_0.9 knitr_1.40 rstudioapi_0.14
[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_1.2.8 polyclip_1.10-0 farver_2.1.1
[28] bit64_4.0.5 rprojroot_2.0.3 parallelly_1.32.1
[31] vctrs_0.4.1 generics_0.1.3 xfun_0.32
[34] R6_2.5.1 bitops_1.0-7 spatstat.utils_2.3-1
[37] cachem_1.0.6 DelayedArray_0.22.0 assertthat_0.2.1
[40] vroom_1.5.7 promises_1.2.0.1 scales_1.2.1
[43] googlesheets4_1.0.1 rgeos_0.5-9 gtable_0.3.1
[46] globals_0.16.1 goftest_1.2-3 workflowr_1.7.0
[49] rlang_1.0.5 splines_4.2.1 rstatix_0.7.0
[52] lazyeval_0.2.2 gargle_1.2.0 spatstat.geom_2.4-0
[55] broom_1.0.1 yaml_2.3.5 reshape2_1.4.4
[58] abind_1.4-5 modelr_0.1.9 backports_1.4.1
[61] httpuv_1.6.6 tools_4.2.1 ellipsis_0.3.2
[64] spatstat.core_2.4-4 jquerylib_0.1.4 RColorBrewer_1.1-3
[67] ggridges_0.5.3 Rcpp_1.0.9 plyr_1.8.7
[70] zlibbioc_1.42.0 RCurl_1.98-1.8 rpart_4.1.16
[73] deldir_1.0-6 pbapply_1.5-0 cowplot_1.1.1
[76] zoo_1.8-10 haven_2.5.1 ggrepel_0.9.1
[79] cluster_2.1.4 fs_1.5.2 data.table_1.14.2
[82] scattermore_0.8 lmtest_0.9-40 reprex_2.0.2
[85] RANN_2.6.1 googledrive_2.0.0 whisker_0.4
[88] fitdistrplus_1.1-8 hms_1.1.2 patchwork_1.1.2
[91] mime_0.12 evaluate_0.16 xtable_1.8-4
[94] readxl_1.4.1 gridExtra_2.3 compiler_4.2.1
[97] KernSmooth_2.23-20 crayon_1.5.1 htmltools_0.5.3
[100] mgcv_1.8-40 later_1.3.0 tzdb_0.3.0
[103] lubridate_1.8.0 DBI_1.1.3 dbplyr_2.2.1
[106] MASS_7.3-58.1 Matrix_1.4-1 car_3.1-0
[109] cli_3.4.0 parallel_4.2.1 igraph_1.3.4
[112] pkgconfig_2.0.3 plotly_4.10.0 spatstat.sparse_2.1-1
[115] xml2_1.3.3 bslib_0.4.0 XVector_0.36.0
[118] rvest_1.0.3 digest_0.6.29 sctransform_0.3.4
[121] RcppAnnoy_0.0.19 spatstat.data_2.2-0 rmarkdown_2.16
[124] cellranger_1.1.0 leiden_0.4.2 uwot_0.1.14
[127] shiny_1.7.2 lifecycle_1.0.2 nlme_3.1-159
[130] jsonlite_1.8.0 carData_3.0-5 viridisLite_0.4.1
[133] fansi_1.0.3 pillar_1.8.1 lattice_0.20-45
[136] fastmap_1.1.0 httr_1.4.4 survival_3.4-0
[139] glue_1.6.2 png_0.1-7 bit_4.0.4
[142] stringi_1.7.8 sass_0.4.2 irlba_2.3.5
[145] future.apply_1.9.0