R Notebook
Last updated: 2019-04-12
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Knit directory: 10x-adipocyte-analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | e55aa11 | Pytrik Folkertsma | 2019-04-12 | Supplementary figures |
library(Seurat)Loading required package: ggplot2Loading required package: cowplot
Attaching package: 'cowplot'The following object is masked from 'package:ggplot2':
ggsaveLoading required package: Matrixlibrary(knitr)
library(ggplot2)
require(scales)Loading required package: scalesseurobj <- readRDS('output/seurat_objects/180831/10x-180831')
source('code/colors.R')BATLAS
Calculate averages for clusters from resolution 1.5:
p <- TSNEPlot(seurobj, group.by='res.1.5', do.label=T, pt.size=0.1)
p
v1 <- VlnPlot(seurobj, features.plot='percent.mito', point.size.use=-1, group.by='res.1.5')
v1
v2 <- VlnPlot(seurobj, features.plot='percent.mito', point.size.use=-1, group.by='res.1.5', y.max=0.2)
v2Warning: Removed 58 rows containing non-finite values (stat_ydensity).Warning: Removed 58 rows containing missing values (geom_point).
save_plot(p, file='figures/figures_paper/supplementary_figures/batlas/10x-180831_tsne_res.1.5.pdf', base_width=5, base_height=4)
save_plot(v1, file='figures/figures_paper/supplementary_figures/batlas/10x-180831_vlnplot_percent.mito_clustering.1.5.pdf', base_width=12, base_height=10)
save_plot(v2, file='figures/figures_paper/supplementary_figures/batlas/10x-180831_vlnplot_percent.mito_clustering.1.5_y-cutoff-0.2.pdf', base_width=12, base_height=4)Warning: Removed 58 rows containing non-finite values (stat_ydensity).Warning: Removed 58 rows containing missing values (geom_point).#Average expression is calculated by: (mean(1expm(seurobj@data[gene, cluster])))
#average.expression <- AverageExpression(SetAllIdent(seurobj, id='res.1.5'))BATLAS results.
batlas <- read.table('tables/tables_paper/supplementary_tables/BATLAS/BATLAS.txt', header=T, sep='\t')
batlas$cluster <- as.character(batlas$cluster)
kable(batlas)| cluster | brown | white |
|---|---|---|
| 0 | 0.389 | 0.611 |
| 1 | 0.471 | 0.529 |
| 2 | 0.384 | 0.616 |
| 3 | 0.600 | 0.400 |
| 4 | 0.567 | 0.433 |
| 5 | 0.422 | 0.578 |
| 6 | 0.315 | 0.685 |
| 7 | 0.607 | 0.393 |
| 8 | 0.560 | 0.440 |
| 9 | 0.680 | 0.320 |
| 10 | 0.479 | 0.521 |
| 11 | 0.545 | 0.455 |
| 12 | 0.420 | 0.580 |
| 13 | 0.542 | 0.458 |
| 14 | 0.723 | 0.277 |
| 15 | 0.443 | 0.557 |
| 16 | 0.845 | 0.155 |
| 17 | 0.769 | 0.231 |
| 18 | 0.488 | 0.512 |
| 19 | 0.446 | 0.554 |
| 20 | 0.390 | 0.610 |
| 21 | 0.578 | 0.422 |
p <- ggplot(data=batlas, aes(x=cluster, y=brown)) +
geom_bar(stat="identity") +
ylab('Brown percentage estimate') +
coord_flip()
p
save_plot('figures/figures_paper/supplementary_figures/batlas/BATLAS_results.pdf', p, base_height=6, base_width=12)BATLAS results log normalized.
batlas <- read.table('tables/tables_paper/supplementary_tables/BATLAS/BATLAS_log_normalized.txt', header=T, sep='\t')
batlas$cluster <- as.character(batlas$cluster)
kable(batlas)| cluster | brown | white |
|---|---|---|
| 0 | 0.569 | 0.431 |
| 1 | 0.590 | 0.410 |
| 2 | 0.564 | 0.436 |
| 3 | 0.634 | 0.366 |
| 4 | 0.618 | 0.382 |
| 5 | 0.578 | 0.422 |
| 6 | 0.539 | 0.461 |
| 7 | 0.635 | 0.365 |
| 8 | 0.623 | 0.377 |
| 9 | 0.659 | 0.341 |
| 10 | 0.594 | 0.406 |
| 11 | 0.618 | 0.382 |
| 12 | 0.579 | 0.421 |
| 13 | 0.618 | 0.382 |
| 14 | 0.677 | 0.323 |
| 15 | 0.579 | 0.421 |
| 16 | 0.730 | 0.270 |
| 17 | 0.697 | 0.303 |
| 18 | 0.595 | 0.405 |
| 19 | 0.583 | 0.417 |
| 20 | 0.526 | 0.474 |
| 21 | 0.634 | 0.366 |
p <- ggplot(data=batlas, aes(x=cluster, y=brown)) +
geom_bar(stat="identity") +
ylab('Brown percentage estimate') +
coord_flip()
p
save_plot('figures/figures_paper/supplementary_figures/batlas/BATLAS_results_log-normalized.pdf', p, base_height=6, base_width=12)PC analysis
p1 <- FeaturePlot(seurobj, features.plot=c('PC1'), cols.use=c('gray', 'blue'), no.legend=F, do.return=T)[['PC1']]
p2 <- FeaturePlot(seurobj, features.plot=c('Pseudotime'), cols.use=c('gray', 'blue'), no.legend=F, do.return=T)[['Pseudotime']]
p3 <- FeaturePlot(seurobj, features.plot=c('Pseudotime'), cols.use=c('gray', 'blue'), no.legend=F, min.cutoff=30, do.return=T)[['Pseudotime']]
p4 <- FeaturePlot(seurobj, features.plot=c('PC2'), cols.use=c('gray', 'blue'), no.legend=F, do.return=T)[['PC2']]
p5 <- TSNEPlot(seurobj, group.by='pc2.groups', pt.size=0.5)
p6 <- DimPlot(SetAllIdent(seurobj, id='State.names'), reduction.use = 'pca', dim.1=5, dim.2=2, cols.use=colors.states.names)
p7 <- DimPlot(SetAllIdent(seurobj, id='State.names'), reduction.use = 'pca', dim.1=1, dim.2=2, cols.use=colors.states.names)
plot_grid(
p1, p2, p3, p4, p5, p6, p7, ncol=2
)
save_plot('figures/figures_paper/supplementary_figures/pc_analysis/PC1_featureplot.pdf', p1, base_width=6, base_height=4.5)
save_plot('figures/figures_paper/supplementary_figures/pc_analysis/Pseudotime_featureplot.pdf', p2, base_width=6, base_height=4.5)
save_plot('figures/figures_paper/supplementary_figures/pc_analysis/Pseudotime_featureplot_min.cutoff30.pdf', p1, base_width=6, base_height=4.5)
save_plot('figures/figures_paper/supplementary_figures/pc_analysis/PC2_featureplot.pdf', p4, base_width=6, base_height=4.5)
save_plot('figures/figures_paper/supplementary_figures/pc_analysis/tSNE_top10percentPC2.pdf', p5, base_width=6, base_height=4.5)
save_plot('figures/figures_paper/supplementary_figures/pc_analysis/PC5_PC2.pdf', p6, base_width=6, base_height=4.5)
save_plot('figures/figures_paper/supplementary_figures/pc_analysis/PC1_PC2.pdf', p7, base_width=6, base_height=4.5)WGCNA
plots <- FeaturePlot(seurobj, features.plot=c('SeuratProject__blue', 'SeuratProject__lightgreen', 'SeuratProject__mediumpurple', 'SeuratProject__sandybrown', 'SeuratProject__springgreen'), cols.use=c('gray', 'blue'), no.legend=F, do.return=T)
plot_grid(
plots$SeuratProject__blue,
plots$SeuratProject__lightgreen,
plots$SeuratProject__mediumpurple,
plots$SeuratProject__sandybrown,
plots$SeuratProject__springgreen,
ncol=2
)
save_plot('figures/figures_paper/supplementary_figures/wgcna/blue.pdf', plots$SeuratProject__blue, base_width=6, base_height=4)
save_plot('figures/figures_paper/supplementary_figures/wgcna/lightgreen.pdf', plots$SeuratProject__lightgreen, base_width=6, base_height=4)
save_plot('figures/figures_paper/supplementary_figures/wgcna/mediumpurple.pdf', plots$SeuratProject__mediumpurple, base_width=6, base_height=4)
save_plot('figures/figures_paper/supplementary_figures/wgcna/sandybrown.pdf', plots$SeuratProject__sandybrown, base_width=6, base_height=4)
save_plot('figures/figures_paper/supplementary_figures/wgcna/springgreen.pdf', plots$SeuratProject__springgreen, base_width=6, base_height=4)FTO analysis
plots <- FeaturePlot(seurobj, features.plot=c('FTO', 'percent.mito', 'IRX3', 'IRX5'), cols.use=c('gray', 'blue'), no.legend=F, do.return=T)
save_plot('figures/figures_paper/supplementary_figures/fto_analysis/FTO_featureplot.pdf', plots$FTO, base_width=6, base_height=4.5)
save_plot('figures/figures_paper/supplementary_figures/fto_analysis/percent.mito_featureplot.pdf', plots$percent.mito, base_width=6, base_height=4.5)
save_plot('figures/figures_paper/supplementary_figures/fto_analysis/IRX3_featureplot.pdf', plots$IRX3, base_width=6, base_height=4.5)
save_plot('figures/figures_paper/supplementary_figures/fto_analysis/IRX5_featureplot.pdf', plots$IRX5, base_width=6, base_height=4.5)pm <- FeaturePlot(seurobj, features.plot=c('percent.mito'), cols.use=c('gray', 'blue'), no.legend=F, do.return=T, max.cutoff = 0.2)[['percent.mito']]
save_plot('figures/figures_paper/supplementary_figures/fto_analysis/percent.mito_featureplot_max.cutoff0.2.pdf', plots$percent.mito, base_width=6, base_height=4.5)
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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] scales_1.0.0 knitr_1.22 Seurat_2.3.4 Matrix_1.2-17 cowplot_0.9.4
[6] ggplot2_3.1.0
loaded via a namespace (and not attached):
[1] Rtsne_0.15 colorspace_1.4-1 class_7.3-15
[4] modeltools_0.2-22 ggridges_0.5.1 mclust_5.4.3
[7] rprojroot_1.3-2 htmlTable_1.13.1 base64enc_0.1-3
[10] fs_1.2.7 rstudioapi_0.10 proxy_0.4-23
[13] npsurv_0.4-0 flexmix_2.3-15 bit64_0.9-7
[16] mvtnorm_1.0-10 codetools_0.2-16 splines_3.5.3
[19] R.methodsS3_1.7.1 lsei_1.2-0 robustbase_0.93-4
[22] jsonlite_1.6 Formula_1.2-3 workflowr_1.2.0
[25] ica_1.0-2 cluster_2.0.7-1 kernlab_0.9-27
[28] png_0.1-7 R.oo_1.22.0 compiler_3.5.3
[31] httr_1.4.0 backports_1.1.3 assertthat_0.2.1
[34] lazyeval_0.2.2 lars_1.2 acepack_1.4.1
[37] htmltools_0.3.6 tools_3.5.3 igraph_1.2.4
[40] gtable_0.3.0 glue_1.3.1 reshape2_1.4.3
[43] RANN_2.6.1 dplyr_0.8.0.1 Rcpp_1.0.1
[46] trimcluster_0.1-2.1 gdata_2.18.0 ape_5.3
[49] nlme_3.1-137 iterators_1.0.10 fpc_2.1-11.1
[52] gbRd_0.4-11 lmtest_0.9-36 xfun_0.5
[55] stringr_1.4.0 irlba_2.3.3 gtools_3.8.1
[58] DEoptimR_1.0-8 MASS_7.3-51.1 zoo_1.8-5
[61] doSNOW_1.0.16 parallel_3.5.3 RColorBrewer_1.1-2
[64] yaml_2.2.0 reticulate_1.11.1 pbapply_1.4-0
[67] gridExtra_2.3 rpart_4.1-13 segmented_0.5-3.0
[70] latticeExtra_0.6-28 stringi_1.4.3 highr_0.8
[73] foreach_1.4.4 checkmate_1.9.1 caTools_1.17.1.2
[76] bibtex_0.4.2 Rdpack_0.10-1 SDMTools_1.1-221
[79] rlang_0.3.2 pkgconfig_2.0.2 dtw_1.20-1
[82] prabclus_2.2-7 bitops_1.0-6 evaluate_0.13
[85] lattice_0.20-38 ROCR_1.0-7 purrr_0.3.2
[88] labeling_0.3 htmlwidgets_1.3 bit_1.1-14
[91] tidyselect_0.2.5 plyr_1.8.4 magrittr_1.5
[94] R6_2.4.0 snow_0.4-3 gplots_3.0.1.1
[97] Hmisc_4.2-0 pillar_1.3.1 whisker_0.3-2
[100] foreign_0.8-71 withr_2.1.2 fitdistrplus_1.0-14
[103] mixtools_1.1.0 survival_2.43-3 nnet_7.3-12
[106] tsne_0.1-3 tibble_2.1.1 crayon_1.3.4
[109] hdf5r_1.1.1 KernSmooth_2.23-15 rmarkdown_1.12
[112] grid_3.5.3 data.table_1.12.0 git2r_0.25.2
[115] metap_1.1 digest_0.6.18 diptest_0.75-7
[118] tidyr_0.8.3 R.utils_2.8.0 stats4_3.5.3
[121] munsell_0.5.0