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Last updated: 2020-03-23
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Knit directory: 10x-adipocyte-analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | cfb1146 | Pytrik Folkertsma | 2020-03-23 | Moved integration to separate notebook |
library(Seurat)
library(SeuratWrappers)
Attaching package: 'SeuratWrappers'The following objects are masked from 'package:Seurat':
ALRAChooseKPlot, RunALRAlibrary(monocle)Loading required package: MatrixLoading required package: BiobaseLoading required package: BiocGenericsLoading required package: parallel
Attaching package: 'BiocGenerics'The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLBThe following objects are masked from 'package:Matrix':
colMeans, colSums, rowMeans, rowSums, whichThe following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabsThe following objects are masked from 'package:base':
anyDuplicated, append, as.data.frame, basename, cbind, colMeans,
colnames, colSums, dirname, do.call, duplicated, eval, evalq,
Filter, Find, get, grep, grepl, intersect, is.unsorted, lapply,
lengths, Map, mapply, match, mget, order, paste, pmax, pmax.int,
pmin, pmin.int, Position, rank, rbind, Reduce, rowMeans, rownames,
rowSums, sapply, setdiff, sort, table, tapply, union, unique,
unsplit, which, which.max, which.minWelcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.Loading required package: ggplot2Loading required package: VGAMLoading required package: stats4Loading required package: splinesLoading required package: DDRTreeLoading required package: irlbalibrary(cowplot)
********************************************************Note: As of version 1.0.0, cowplot does not change the default ggplot2 theme anymore. To recover the previous behavior, execute:
theme_set(theme_cowplot())********************************************************library(dplyr)
Attaching package: 'dplyr'The following object is masked from 'package:Biobase':
combineThe following objects are masked from 'package:BiocGenerics':
combine, intersect, setdiff, unionThe following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionlibrary(tidyr)
Attaching package: 'tidyr'The following object is masked from 'package:VGAM':
fillThe following objects are masked from 'package:Matrix':
expand, pack, unpacklibrary(knitr)
library(kableExtra)
Attaching package: 'kableExtra'The following object is masked from 'package:dplyr':
group_rowslibrary(DT)
Attaching package: 'DT'The following object is masked from 'package:Seurat':
JSlibrary(harmony)Loading required package: Rcppdata_180831 <- readRDS('output/seurat_objects/180831/10x-180831-S3')Seurat integration
Integrating Wolfrum with 10x-180831 data
#anchors <- FindIntegrationAnchors(object.list = list(wolfrum, data_180831), dims = 1:20)
#integrated <- IntegrateData(anchorset = anchors, dims = 1:20)
#saveRDS(integrated, '/projects/pytrik/sc_adipose/analyze_10x_fluidigm/10x-adipocyte-analysis/output/seurat_objects/wolfrum/wolfrum.180831.integrated.rds')
integrated <- readRDS('output/seurat_objects/wolfrum/wolfrum.180831.integrated.rds')
integrated@meta.data['dataset'] <- '10x-180831'
integrated@meta.data[which(is.na(integrated@meta.data$branch)), 'dataset'] <- 'Wolfrum'
plot_grid(
UMAPPlot(integrated, group.by='dataset'),
UMAPPlot(integrated, group.by='seurat_clusters', label=T),
UMAPPlot(integrated, group.by='branch'), ncol=2
)Warning: Using `as.character()` on a quosure is deprecated as of rlang 0.3.0.
Please use `as_label()` or `as_name()` instead.
This warning is displayed once per session.
These results also confirm that the L branch is closest to cluster 11 and U is closest to the U branch.
Predict cell types with Seurat’s TransferData
wolfrum.predicted_labels <- readRDS('output/seurat_objects/wolfrum/wolfrum.predicted_labels_180831.rds')Used pca as dimred for FindTransferAnchors and IntegrateData.
plot_grid(
FeaturePlot(wolfrum.predicted_labels, features='predictions_pca.prediction.score.Progenitor'),
FeaturePlot(wolfrum.predicted_labels, features='predictions_pca.prediction.score.Metabolic'),
FeaturePlot(wolfrum.predicted_labels, features='predictions_pca.prediction.score.ECM'), ncol=2
)
For all predictions, change predicted id to NA if max score is below a certain threshold.
assign_labels <- function(colname, threshold=0.5){
pred_ids <- unlist(as.vector(apply(wolfrum.predicted_labels@meta.data[,c(paste(colname,'.prediction.score.max', sep=''), paste(colname, '.predicted.id', sep=''))], 1, function(x){
if (x[[1]] < threshold){
return(NA)
} else{
return(x[[2]])
}
})))
return(pred_ids)
}
for (col in c('predictions_pca')){
for (t in c(0.5, 0.7, 0.9, 0.95, 0.99)){
preds <- assign_labels(col, t)
wolfrum.predicted_labels <- AddMetaData(wolfrum.predicted_labels, preds, col.name=paste(col, 'predicted_label', t, sep='.'))
}
}plot_grid(
UMAPPlot(wolfrum.predicted_labels, group.by='predictions_pca.predicted_label.0.7') + ggtitle('threshold=0.7'),
UMAPPlot(wolfrum.predicted_labels, group.by='predictions_pca.predicted_label.0.9') + ggtitle('threshold=0.9'),
UMAPPlot(wolfrum.predicted_labels, group.by='predictions_pca.predicted_label.0.95') + ggtitle('threshold=0.95'),
UMAPPlot(wolfrum.predicted_labels, group.by='predictions_pca.predicted_label.0.99') + ggtitle('threshold=0.99'),
ncol=2
)
Color predictions in integrated dataset
integrated@meta.data['branch_labels_integrated'] <- wolfrum.predicted_labels@meta.data$predictions_pca.predicted_label.0.9[match(rownames(integrated@meta.data), rownames(wolfrum.predicted_labels@meta.data))]
integrated@meta.data['branch_labels_integrated'] <- unlist(lapply(integrated@meta.data$branch_labels_integrated, function(x){
if (is.na(x)){
return('Non-matching cells')
} else {
if (x == 'Progenitor'){
return('Seurat predicted match to P')
} else if (x == 'Metabolic') {
return('Seurat predicted match to U')
} else if (x == 'ECM') {
return('Seurat predicted match to L')
} else {
return('Non-matching cells')
}
}
}))
integrated@meta.data$branch_labels_integrated[match(rownames(data_180831@meta.data), rownames(integrated@meta.data))] <- data_180831@meta.data$State.labelscolormap <- c(
P='#ecdd83',
U='#e27268',
L='#93c8bc',
'Seurat predicted match to P'='#b7a333',
'Seurat predicted match to U'='#af4f48',
'Seurat predicted match to L'='#4f8579',
'Non-matching cells'='#b6b6b6'
)
integrated@meta.data$branch_labels_integrated <- factor(integrated@meta.data$branch_labels_integrated, levels = c('P', 'U', 'L', 'Seurat predicted match to P', 'Seurat predicted match to U', 'Seurat predicted match to L', 'Non-matching cells'))
DimPlot(integrated, reduction='umap', group.by='branch_labels_integrated', cols=colormap) + NoAxes()
Harmony integration
#harmony <- RunHarmony(data_combined, "Dataset", plot_convergence =TRUE)
#harmony <- RunUMAP(harmony, reduction='harmony', dims=1:20)
harmony <- readRDS('output/seurat_objects/wolfrum/wolfrum.180831.harmony_default_integration')plot_grid(
DimPlot(harmony, reduction='umap', group.by='Dataset'),
DimPlot(harmony, reduction='umap', group.by='RNA_snn_res.0.8', label=T),
DimPlot(harmony, reduction='umap', group.by='branch'), ncol=2
)
harmony@meta.data['branch_labels_integrated'] <- wolfrum.predicted_labels@meta.data$predictions_pca.predicted_label.0.9[match(rownames(harmony@meta.data), rownames(wolfrum.predicted_labels@meta.data))]
harmony@meta.data['branch_labels_integrated'] <- unlist(lapply(harmony@meta.data$branch_labels_integrated, function(x){
if (is.na(x)){
return('Non-matching cells')
} else {
if (x == 'Progenitor'){
return('Seurat predicted match to P')
} else if (x == 'Metabolic') {
return('Seurat predicted match to U')
} else if (x == 'ECM') {
return('Seurat predicted match to L')
} else {
return('Non-matching cells')
}
}
}))
harmony@meta.data$branch_labels_integrated[match(rownames(data_180831@meta.data), rownames(harmony@meta.data))] <- data_180831@meta.data$State.labelscolormap <- c(
P='#ecdd83',
U='#e27268',
L='#93c8bc',
'Seurat predicted match to P'='#b7a333',
'Seurat predicted match to U'='#af4f48',
'Seurat predicted match to L'='#4f8579',
'Non-matching cells'='#b6b6b6'
)
harmony@meta.data$branch_labels_integrated <- factor(harmony@meta.data$branch_labels_integrated, levels = c('P', 'U', 'L', 'Seurat predicted match to P', 'Seurat predicted match to U', 'Seurat predicted match to L', 'Non-matching cells'))
DimPlot(harmony, reduction='umap', group.by='branch_labels_integrated', cols=colormap) + NoAxes()
#Figures
harmony_180831 <- DimPlot(harmony, reduction='umap', group.by='branch_labels_integrated', cols=colormap) + NoAxes()
harmony_180831_vf <- AugmentPlot(harmony_180831, width=7, height=5, dpi=500)
#save_plot("../figures/figures_paper/main_figures/Figure_wolfrum/UMAP_wolfrum_harmony_branch-labels.vf.pdf", harmony_180831_vf, base_width=5, base_height=5)
#save_plot("../figures/figures_paper/main_figures/Figure_wolfrum/UMAP_wolfrum_harmony_branch-labels.pdf", harmony_180831, base_width=7, base_height=5)
harmony_180831
harmony_180831_split_by_source <- DimPlot(harmony, reduction='umap', group.by='branch_labels_integrated', cols=colormap, split.by='Dataset')
#harmony_180831_split_by_source_vf <- lapply(X = harmony_180831_split_by_source, FUN = AugmentPlot)
#save_plot("../figures/figures_paper/main_figures/Figure_wolfrum/UMAP_wolfrum_harmony_branch-labels.split-dataset.pdf", harmony_180831_split_by_source, base_width=12, base_height=5)
harmony_180831_split_by_source
harmony_180831 <- DimPlot(harmony, reduction='umap', group.by='Dataset') + NoAxes()
harmony_180831_vf <- AugmentPlot(harmony_180831, width=5, height=5, dpi=500)
#save_plot("../figures/figures_paper/main_figures/Figure_wolfrum/UMAP_wolfrum_harmony_dataset.vf.pdf", harmony_180831_vf, base_width=5, base_height=5)
#save_plot("../figures/figures_paper/main_figures/Figure_wolfrum/UMAP_wolfrum_harmony_dataset.pdf", harmony_180831, base_width=7, base_height=5)
harmony_180831
labels <- unlist(lapply(wolfrum.predicted_labels@meta.data$predictions_pca.predicted_label.0.7, function(x){
if (is.na(x)){
return('Non-matching cells')
} else if(x == 'ECM'){
return('L')
} else if(x == 'Metabolic'){
return('U')
} else if (x == 'Progenitor'){
return('P')
}
}))
wolfrum.predicted_labels@meta.data['predictions_pca.predicted_label.0.7_labels'] <- labels
wolfrum.predicted_labels@meta.data['predictions_pca.predicted_label.0.7_labels'] <- factor(wolfrum.predicted_labels@meta.data$predictions_pca.predicted_label.0.7_labels, levels = c("P", "L", "U", 'Non-matching cells'))colormap.branches <- c(
P="#ecdd83",
U="#e27268",
L="#93c8bc",
'Non-matching cells'='#7a7a7a')
p_predictions <- UMAPPlot(wolfrum.predicted_labels, group.by='predictions_pca.predicted_label.0.7_labels', cols=colormap.branches) + NoAxes() + theme(legend.text=element_text(size=12), legend.key.height=unit(0.4, 'cm'), axis.text = element_blank(), axis.ticks = element_blank(), axis.title = element_blank(), plot.margin=grid::unit(c(0,0,0,0), "mm")) + labs(color='Seurat prediction')
p_predictions
#save_plot("figures/figures_paper/main_figures/Figure_wolfrum/UMAP_wolfrum_predicted-labels_180831.pdf", p_predictions, base_width=8, base_height=5)p_clusters <- UMAPPlot(wolfrum.predicted_labels, group.by='RNA_snn_res.0.8', label=T) + NoAxes() + theme(legend.position = "none", axis.text = element_blank(), axis.ticks = element_blank(), axis.title = element_blank(), plot.margin=grid::unit(c(0,0,0,0), "mm"))
p_clusters
#save_plot("figures/figures_paper/main_figures/Figure_wolfrum/UMAP_wolfrum_clusters.pdf", p_clusters, base_width=6, base_height=5)
sessionInfo()R version 3.5.3 (2019-03-11)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Storage
Matrix products: default
BLAS/LAPACK: /usr/lib64/libopenblas-r0.3.3.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=en_US.UTF-8
[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] splines stats4 parallel stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] harmony_1.0 Rcpp_1.0.3 DT_0.12
[4] kableExtra_1.1.0 knitr_1.28 tidyr_1.0.2
[7] dplyr_0.8.5 cowplot_1.0.0 monocle_2.10.1
[10] DDRTree_0.1.5 irlba_2.3.3 VGAM_1.1-2
[13] ggplot2_3.3.0 Biobase_2.42.0 BiocGenerics_0.28.0
[16] Matrix_1.2-18 SeuratWrappers_0.1.0 Seurat_3.1.4
[19] workflowr_1.6.0
loaded via a namespace (and not attached):
[1] backports_1.1.5 sn_1.5-5 plyr_1.8.6
[4] igraph_1.2.4.2 lazyeval_0.2.2 densityClust_0.3
[7] listenv_0.8.0 fastICA_1.2-2 TH.data_1.0-10
[10] digest_0.6.25 htmltools_0.4.0 viridis_0.5.1
[13] gdata_2.18.0 magrittr_1.5 cluster_2.1.0
[16] ROCR_1.0-7 limma_3.38.3 remotes_2.1.1
[19] readr_1.3.1 globals_0.12.5 RcppParallel_4.4.4
[22] matrixStats_0.55.0 docopt_0.6.1 sandwich_2.5-1
[25] colorspace_1.4-1 rvest_0.3.5 rappdirs_0.3.1
[28] ggrepel_0.8.2 xfun_0.12 sparsesvd_0.2
[31] crayon_1.3.4 jsonlite_1.6.1 survival_3.1-11
[34] zoo_1.8-7 ape_5.3 glue_1.3.1
[37] gtable_0.3.0 webshot_0.5.2 leiden_0.3.3
[40] future.apply_1.4.0 scales_1.1.0 pheatmap_1.0.12
[43] mvtnorm_1.1-0 bibtex_0.4.2.2 metap_1.3
[46] plotrix_3.7-7 viridisLite_0.3.0 reticulate_1.14
[49] rsvd_1.0.3 tsne_0.1-3 htmlwidgets_1.5.1
[52] httr_1.4.1 FNN_1.1.3 gplots_3.0.3
[55] RColorBrewer_1.1-2 TFisher_0.2.0 ica_1.0-2
[58] farver_2.0.3 pkgconfig_2.0.3 uwot_0.1.5
[61] labeling_0.3 tidyselect_1.0.0 rlang_0.4.5
[64] reshape2_1.4.3 later_1.0.0 munsell_0.5.0
[67] tools_3.5.3 ggridges_0.5.2 evaluate_0.14
[70] stringr_1.4.0 yaml_2.2.1 npsurv_0.4-0
[73] fs_1.3.2 fitdistrplus_1.0-14 caTools_1.17.1.2
[76] purrr_0.3.3 RANN_2.6.1 pbapply_1.4-2
[79] future_1.16.0 nlme_3.1-140 whisker_0.4
[82] slam_0.1-47 xml2_1.2.2 rstudioapi_0.11
[85] compiler_3.5.3 plotly_4.9.2 png_0.1-7
[88] lsei_1.2-0 tibble_2.1.3 stringi_1.4.6
[91] highr_0.8 lattice_0.20-38 HSMMSingleCell_1.2.0
[94] multtest_2.38.0 vctrs_0.2.4 mutoss_0.1-12
[97] pillar_1.4.3 lifecycle_0.2.0 BiocManager_1.30.10
[100] combinat_0.0-8 Rdpack_0.11-1 lmtest_0.9-37
[103] RcppAnnoy_0.0.15 data.table_1.12.8 bitops_1.0-6
[106] gbRd_0.4-11 httpuv_1.5.2 patchwork_1.0.0.9000
[109] R6_2.4.1 promises_1.1.0 KernSmooth_2.23-15
[112] gridExtra_2.3 codetools_0.2-16 MASS_7.3-51.4
[115] gtools_3.8.1 assertthat_0.2.1 rprojroot_1.3-2
[118] withr_2.1.2 qlcMatrix_0.9.7 sctransform_0.2.1
[121] mnormt_1.5-6 multcomp_1.4-12 hms_0.5.3
[124] grid_3.5.3 rmarkdown_2.1 Rtsne_0.15
[127] git2r_0.26.1 numDeriv_2016.8-1.1