Merge samples
Mechthild Lütge
14 May 2020
Last updated: 2022-09-12
Checks: 6 1
Knit directory: humanCardiacFibroblasts/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 02765dc | mluetge | 2022-07-19 | GSEA across diff groups |
| Rmd | 3e98bf3 | mluetge | 2022-07-15 | run DE genes HH vs Myo |
| html | 3e98bf3 | mluetge | 2022-07-15 | run DE genes HH vs Myo |
| Rmd | d9e1a98 | mluetge | 2022-07-04 | integrate samples Graz |
| html | d9e1a98 | mluetge | 2022-07-04 | integrate samples 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)
library(viridis)
library(sctransform)
})integrate data
basedir <- here()
seurat <- readRDS(file = paste0(basedir,
"/data/humanHeartsPlusGraz_merged_seurat.rds"))
## integrate data across patients
Idents(seurat) <- seurat$ID
seurat.list <- SplitObject(object = seurat, split.by = "ID")
for (i in 1:length(x = seurat.list)) {
seurat.list[[i]] <- NormalizeData(object = seurat.list[[i]],
verbose = FALSE)
seurat.list[[i]] <- FindVariableFeatures(object = seurat.list[[i]],
selection.method = "vst", nfeatures = 500, verbose = FALSE)
}
seurat.anchors <- FindIntegrationAnchors(object.list = seurat.list, dims = 1:20)
seurat.int <- IntegrateData(anchorset = seurat.anchors, dims = 1:20)
DefaultAssay(object = seurat.int) <- "integrated"
# rerun seurat
seurat.int <- ScaleData(object = seurat.int, verbose = FALSE)
seurat.int <- RunPCA(object = seurat.int, npcs = 20, verbose = FALSE)
seurat.int <- RunTSNE(object = seurat.int, reduction = "pca", dims = 1:20)
seurat.int <- RunUMAP(object = seurat.int, reduction = "pca", dims = 1:20)
seurat.int <- FindNeighbors(object = seurat.int, reduction = "pca", dims = 1:20,
k.param = 50)
res <- c(0.6,0.8,0.4,0.25)
for(i in 1:length(res)){
seurat.int <- FindClusters(object = seurat.int, resolution = res[i],
random.seed = 1234)
}Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 52908
Number of edges: 6183319
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9212
Number of communities: 17
Elapsed time: 25 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 52908
Number of edges: 6183319
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9041
Number of communities: 18
Elapsed time: 27 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 52908
Number of edges: 6183319
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9424
Number of communities: 15
Elapsed time: 28 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 52908
Number of edges: 6183319
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9610
Number of communities: 12
Elapsed time: 25 secondsDefaultAssay(object = seurat.int) <- "RNA"
seurat <- seurat.int
remove(seurat.int)
seurat$seurat_clusters <- seurat$integrated_snn_res.0.25
Idents(seurat) <- seurat$seurat_clusterssave int object
saveRDS(seurat, file = paste0(basedir,
"/data/humanHeartsPlusGraz_intPatients_merged",
"_seurat.rds"))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_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")
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)
)
save objects
write.table(seurat_markers_all,
file=paste0(basedir,
"/data/humanHeartsPlusGraz_intPatients_merged",
"_markerGenes.txt"),
row.names = FALSE, col.names = TRUE, quote = FALSE, sep = "\t")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] sctransform_0.3.4 viridis_0.6.2
[3] viridisLite_0.4.1 pheatmap_1.0.12
[5] ggpubr_0.4.0 ggsci_2.9
[7] runSeurat3_0.1.0 here_1.0.1
[9] magrittr_2.0.3 sp_1.5-0
[11] SeuratObject_4.1.1 Seurat_4.1.1
[13] forcats_0.5.2 stringr_1.4.1
[15] dplyr_1.0.10 purrr_0.3.4
[17] readr_2.1.2 tidyr_1.2.0
[19] tibble_3.1.8 ggplot2_3.3.6
[21] tidyverse_1.3.2 SingleCellExperiment_1.18.0
[23] SummarizedExperiment_1.26.1 Biobase_2.56.0
[25] GenomicRanges_1.48.0 GenomeInfoDb_1.32.3
[27] IRanges_2.30.1 S4Vectors_0.34.0
[29] BiocGenerics_0.42.0 MatrixGenerics_1.8.1
[31] 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] rprojroot_2.0.3 parallelly_1.32.1 vctrs_0.4.1
[31] generics_0.1.3 xfun_0.32 R6_2.5.1
[34] bitops_1.0-7 spatstat.utils_2.3-1 cachem_1.0.6
[37] DelayedArray_0.22.0 assertthat_0.2.1 promises_1.2.0.1
[40] scales_1.2.1 googlesheets4_1.0.1 rgeos_0.5-9
[43] gtable_0.3.1 globals_0.16.1 goftest_1.2-3
[46] workflowr_1.7.0 rlang_1.0.5 splines_4.2.1
[49] rstatix_0.7.0 lazyeval_0.2.2 gargle_1.2.0
[52] spatstat.geom_2.4-0 broom_1.0.1 yaml_2.3.5
[55] reshape2_1.4.4 abind_1.4-5 modelr_0.1.9
[58] backports_1.4.1 httpuv_1.6.5 tools_4.2.1
[61] ellipsis_0.3.2 spatstat.core_2.4-4 jquerylib_0.1.4
[64] RColorBrewer_1.1-3 ggridges_0.5.3 Rcpp_1.0.9
[67] plyr_1.8.7 zlibbioc_1.42.0 RCurl_1.98-1.8
[70] rpart_4.1.16 deldir_1.0-6 pbapply_1.5-0
[73] cowplot_1.1.1 zoo_1.8-10 haven_2.5.1
[76] ggrepel_0.9.1 cluster_2.1.4 fs_1.5.2
[79] data.table_1.14.2 scattermore_0.8 lmtest_0.9-40
[82] reprex_2.0.2 RANN_2.6.1 googledrive_2.0.0
[85] whisker_0.4 fitdistrplus_1.1-8 hms_1.1.2
[88] patchwork_1.1.2 mime_0.12 evaluate_0.16
[91] xtable_1.8-4 readxl_1.4.1 gridExtra_2.3
[94] compiler_4.2.1 KernSmooth_2.23-20 crayon_1.5.1
[97] htmltools_0.5.3 mgcv_1.8-40 later_1.3.0
[100] tzdb_0.3.0 lubridate_1.8.0 DBI_1.1.3
[103] dbplyr_2.2.1 MASS_7.3-58.1 Matrix_1.4-1
[106] car_3.1-0 cli_3.3.0 parallel_4.2.1
[109] igraph_1.3.4 pkgconfig_2.0.3 plotly_4.10.0
[112] spatstat.sparse_2.1-1 xml2_1.3.3 bslib_0.4.0
[115] XVector_0.36.0 rvest_1.0.3 digest_0.6.29
[118] RcppAnnoy_0.0.19 spatstat.data_2.2-0 rmarkdown_2.16
[121] cellranger_1.1.0 leiden_0.4.2 uwot_0.1.14
[124] shiny_1.7.2 lifecycle_1.0.1 nlme_3.1-159
[127] jsonlite_1.8.0 carData_3.0-5 limma_3.52.2
[130] fansi_1.0.3 pillar_1.8.1 lattice_0.20-45
[133] fastmap_1.1.0 httr_1.4.4 survival_3.4-0
[136] glue_1.6.2 png_0.1-7 stringi_1.7.8
[139] sass_0.4.2 irlba_2.3.5 future.apply_1.9.0 date()[1] "Mon Sep 12 12:13:36 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] sctransform_0.3.4 viridis_0.6.2
[3] viridisLite_0.4.1 pheatmap_1.0.12
[5] ggpubr_0.4.0 ggsci_2.9
[7] runSeurat3_0.1.0 here_1.0.1
[9] magrittr_2.0.3 sp_1.5-0
[11] SeuratObject_4.1.1 Seurat_4.1.1
[13] forcats_0.5.2 stringr_1.4.1
[15] dplyr_1.0.10 purrr_0.3.4
[17] readr_2.1.2 tidyr_1.2.0
[19] tibble_3.1.8 ggplot2_3.3.6
[21] tidyverse_1.3.2 SingleCellExperiment_1.18.0
[23] SummarizedExperiment_1.26.1 Biobase_2.56.0
[25] GenomicRanges_1.48.0 GenomeInfoDb_1.32.3
[27] IRanges_2.30.1 S4Vectors_0.34.0
[29] BiocGenerics_0.42.0 MatrixGenerics_1.8.1
[31] 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] rprojroot_2.0.3 parallelly_1.32.1 vctrs_0.4.1
[31] generics_0.1.3 xfun_0.32 R6_2.5.1
[34] bitops_1.0-7 spatstat.utils_2.3-1 cachem_1.0.6
[37] DelayedArray_0.22.0 assertthat_0.2.1 promises_1.2.0.1
[40] scales_1.2.1 googlesheets4_1.0.1 rgeos_0.5-9
[43] gtable_0.3.1 globals_0.16.1 goftest_1.2-3
[46] workflowr_1.7.0 rlang_1.0.5 splines_4.2.1
[49] rstatix_0.7.0 lazyeval_0.2.2 gargle_1.2.0
[52] spatstat.geom_2.4-0 broom_1.0.1 yaml_2.3.5
[55] reshape2_1.4.4 abind_1.4-5 modelr_0.1.9
[58] backports_1.4.1 httpuv_1.6.5 tools_4.2.1
[61] ellipsis_0.3.2 spatstat.core_2.4-4 jquerylib_0.1.4
[64] RColorBrewer_1.1-3 ggridges_0.5.3 Rcpp_1.0.9
[67] plyr_1.8.7 zlibbioc_1.42.0 RCurl_1.98-1.8
[70] rpart_4.1.16 deldir_1.0-6 pbapply_1.5-0
[73] cowplot_1.1.1 zoo_1.8-10 haven_2.5.1
[76] ggrepel_0.9.1 cluster_2.1.4 fs_1.5.2
[79] data.table_1.14.2 scattermore_0.8 lmtest_0.9-40
[82] reprex_2.0.2 RANN_2.6.1 googledrive_2.0.0
[85] whisker_0.4 fitdistrplus_1.1-8 hms_1.1.2
[88] patchwork_1.1.2 mime_0.12 evaluate_0.16
[91] xtable_1.8-4 readxl_1.4.1 gridExtra_2.3
[94] compiler_4.2.1 KernSmooth_2.23-20 crayon_1.5.1
[97] htmltools_0.5.3 mgcv_1.8-40 later_1.3.0
[100] tzdb_0.3.0 lubridate_1.8.0 DBI_1.1.3
[103] dbplyr_2.2.1 MASS_7.3-58.1 Matrix_1.4-1
[106] car_3.1-0 cli_3.3.0 parallel_4.2.1
[109] igraph_1.3.4 pkgconfig_2.0.3 plotly_4.10.0
[112] spatstat.sparse_2.1-1 xml2_1.3.3 bslib_0.4.0
[115] XVector_0.36.0 rvest_1.0.3 digest_0.6.29
[118] RcppAnnoy_0.0.19 spatstat.data_2.2-0 rmarkdown_2.16
[121] cellranger_1.1.0 leiden_0.4.2 uwot_0.1.14
[124] shiny_1.7.2 lifecycle_1.0.1 nlme_3.1-159
[127] jsonlite_1.8.0 carData_3.0-5 limma_3.52.2
[130] fansi_1.0.3 pillar_1.8.1 lattice_0.20-45
[133] fastmap_1.1.0 httr_1.4.4 survival_3.4-0
[136] glue_1.6.2 png_0.1-7 stringi_1.7.8
[139] sass_0.4.2 irlba_2.3.5 future.apply_1.9.0