Last updated: 2018-11-12

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Analysis of the 10x-180504 samples.

Loading the required packages and datasets.

library(Seurat)
library(dplyr)

all10x <- readRDS('output/10x-180504')
all10x.ccregout <- readRDS('output/10x-180504-ccregout')

QC Plots

VlnPlot(all10x, features.plot='nGene', group.by='sample_name', point.size.use=-1, x.lab.rot=T)

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VlnPlot(all10x, features.plot='nUMI', group.by='sample_name', point.size.use=-1, x.lab.rot=T)

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VlnPlot(all10x, features.plot='percent.mito', group.by='sample_name', point.size.use=-1, x.lab.rot=T)

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GenePlot(all10x, 'nUMI', 'nGene')

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TSNE

Below are several tSNE plots of the 10x-180504 data. tSNE was performed on the first 15 principal components of the log-normalized scaled (nUMI and percent.mito regressed out) data.

Visceral and perirenal seem a bit mixed, and supraclavicular and subcutaneous too.

TSNEPlot(all10x, pt.size=0.1, group.by='sample_name', do.label=T)

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tSNE plots of samples within their depot. Peri2 and Peri3 seem to overlap really well, as well as Supra1 and Supra2, and Visce1 and Visce3.

plot_grid(t1, t2, t3, t4)

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tSNE colored on subtissue.

TSNEPlot(all10x, group.by='depot', pt.size=0.1)

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tSNE colored by type.

TSNEPlot(all10x, group.by='type', pt.size=0.1, colors.use=c('#964B00', '#F7E7CE'))

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tSNE colored by cell cycle phase.

TSNEPlot(all10x, group.by='Phase', pt.size=0.1)

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Clusters

Some clustering with different resolutions. res=0.5

TSNEPlot(all10x, pt.size=0.1, group.by='res.0.5', do.label=T)

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res=0.7

TSNEPlot(all10x, pt.size=0.1, group.by='res.0.7', do.label=T)

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res=1

TSNEPlot(all10x, pt.size=0.1, group.by='res.1', do.label=T)

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Cell cycle regression

T-SNE of the data with cell cycle effects regressed out. There does not seem to be a lot of structure within clusters now.

TSNEPlot(all10x.ccregout, pt.size=0.1, group.by='sample_name')

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No cell cycle effect anymore.

TSNEPlot(all10x.ccregout, pt.size=0.1, group.by='Phase')

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Subtissues

plot_grid(t1, t2, t3, t4)

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TSNEPlot(all10x.ccregout, pt.size=0.1, group.by='depot')

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TSNEPlot(all10x.ccregout, group.by='type', pt.size=0.1, colors.use=c('#964B00', '#F7E7CE'))

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1f7e0da PytrikFolkertsma 2018-10-30

PCA

Some PCA plots. PC1 seems to capture cell cycle effects, and PC2 seems to capture some of the sample variability.

PCAPlot(all10x, group.by='Phase', pt.size=0.1)

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PCAPlot(all10x, group.by='sample_name', pt.size=0.1)

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PCA plot of the cell cycle regressed out data. There is no cell cycle effect anymore.

PCAPlot(all10x.ccregout, group.by='Phase', pt.size=0.1)

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PCAPlot(all10x.ccregout, group.by='sample_name', pt.size=0.1)

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Metadata plots

FeaturePlot(all10x, c("nGene"), cols.use = c("grey","blue"), no.legend=F)

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FeaturePlot(all10x, c("percent.mito"), cols.use = c("grey","blue"), no.legend=F)

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FeaturePlot(all10x, c("nUMI"), cols.use = c("grey","blue"), no.legend=F)

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Diff

TSNEPlot(all10x, group.by='diff', pt.size=0.1)

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all10x@meta.data['diff_int'] <- unlist(lapply(as.vector(unlist(all10x@meta.data$diff)), function(x){return(strtoi(strsplit(x, '%')))}))
FeaturePlot(all10x, features.plot='diff_int', cols.use=c('gray', 'blue'), no.legend=F)

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ucp1.ctrl

TSNEPlot(all10x, group.by='ucp1.ctrl', pt.size=0.1)

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ucp1.ne

TSNEPlot(all10x, group.by='ucp1.ne', pt.size=0.1)

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bmi

TSNEPlot(all10x, group.by='bmi', pt.size=0.1)

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age

TSNEPlot(all10x, group.by='age', pt.size=0.1)

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VlnPlot(all10x, group.by='sample_name', features.plot=c('nGene'), point.size.use = -1, x.lab.rot=T)

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1f7e0da PytrikFolkertsma 2018-10-30 

VlnPlot(all10x, group.by='sample_name', features.plot=c('nUMI'), point.size.use = -1, x.lab.rot=T)

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VlnPlot(all10x, group.by='sample_name', features.plot=c('percent.mito'), point.size.use = -1, x.lab.rot=T)

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1f7e0da PytrikFolkertsma 2018-10-30

Mixture cluster 12

Sample composition in cluster 12.

cluster12 <- SubsetData(all10x, cells.use=rownames(all10x@meta.data)[which(all10x@meta.data$res.0.5 %in% 12)])
rotate_x <- function(data, column_to_plot, labels_vec, rot_angle) {
     plt <- barplot(data[[column_to_plot]], col='steelblue', xaxt="n")
     text(plt, par("usr")[3], labels = labels_vec, srt = rot_angle, adj = c(1.1,1.1), xpd = TRUE, cex=1)
}
rotate_x((cluster12@meta.data %>% count(sample_name))[,2], 'n', as.vector(unlist((cluster12@meta.data %>% count(sample_name))[,1])), 45)

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cluster12 <- SubsetData(all10x, cells.use=rownames(all10x@meta.data)[which(all10x@meta.data$res.0.5 %in% 12)])
rotate_x <- function(data, column_to_plot, labels_vec, rot_angle) {
     plt <- barplot(data[[column_to_plot]], col='steelblue', xaxt="n")
     text(plt, par("usr")[3], labels = labels_vec, srt = rot_angle, adj = c(1.1,1.1), xpd = TRUE, cex=1)
}
rotate_x((cluster12@meta.data %>% count(sample_name))[,2], 'n', as.vector(unlist((cluster12@meta.data %>% count(sample_name))[,1])), 90)

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Figures for report

fig1 <- plot_grid(
  PCAPlot(all10x, group.by='Phase', pt.size=0.1),
  TSNEPlot(all10x, group.by='sample_name', pt.size=0.1),
  TSNEPlot(all10x, group.by='Phase', pt.size=0.1),
  TSNEPlot(all10x.ccregout, group.by='sample_name', pt.size=0.1),
  labels = "auto", nrow = 2)

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save_plot("plots/180504_pca_tsne.pdf", fig1, base_width=12, base_height = 9)
fig1

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#Supplementary figures
sfig1 <- plot_grid(
  VlnPlot(all10x, group.by='sample_name', features.plot=c('nGene'), point.size.use = -1, x.lab.rot=T, size.x.use=8),
  VlnPlot(all10x, group.by='sample_name', features.plot=c('nUMI'), point.size.use = -1, x.lab.rot=T, size.x.use=8),
  VlnPlot(all10x, group.by='sample_name', features.plot=c('percent.mito'), point.size.use = -1, x.lab.rot=T, size.x.use=8), 
  labels="auto", nrow=1
)
save_plot("plots/supplementary_figures/sfig_180504_qcplots.pdf", sfig1, base_width=12, base_height=3)
sfig1

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sfig2 <- plot_grid(
  PCElbowPlot(all10x, num.pc=30),
  PCElbowPlot(all10x.ccregout, num.pc=30),
  labels="auto", nrow=1)
save_plot("plots/supplementary_figures/sfig_180504_pcelbow.pdf", sfig2, base_width=12, base_height=4)
sfig2

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Session information

sessionInfo()
R version 3.4.3 (2017-11-30)
Platform: x86_64-redhat-linux-gnu (64-bit)
Running under: Red Hat Enterprise Linux

Matrix products: default
BLAS/LAPACK: /usr/lib64/R/lib/libRblas.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] bindrcpp_0.2.2 dplyr_0.7.6    Seurat_2.3.4   Matrix_1.2-14 
[5] cowplot_0.9.3  ggplot2_3.0.0 

loaded via a namespace (and not attached):
  [1] Rtsne_0.13          colorspace_1.3-2    class_7.3-14       
  [4] modeltools_0.2-22   ggridges_0.5.0      mclust_5.4.1       
  [7] rprojroot_1.3-2     htmlTable_1.12      base64enc_0.1-3    
 [10] rstudioapi_0.7      proxy_0.4-22        flexmix_2.3-14     
 [13] bit64_0.9-7         mvtnorm_1.0-8       codetools_0.2-15   
 [16] splines_3.4.3       R.methodsS3_1.7.1   robustbase_0.93-2  
 [19] knitr_1.20          Formula_1.2-3       jsonlite_1.5       
 [22] workflowr_1.1.1     ica_1.0-2           cluster_2.0.7-1    
 [25] kernlab_0.9-27      png_0.1-7           R.oo_1.22.0        
 [28] compiler_3.4.3      httr_1.3.1          backports_1.1.2    
 [31] assertthat_0.2.0    lazyeval_0.2.1      lars_1.2           
 [34] acepack_1.4.1       htmltools_0.3.6     tools_3.4.3        
 [37] igraph_1.2.2        gtable_0.2.0        glue_1.3.0         
 [40] RANN_2.6            reshape2_1.4.3      Rcpp_0.12.18       
 [43] trimcluster_0.1-2.1 gdata_2.18.0        ape_5.1            
 [46] nlme_3.1-137        iterators_1.0.10    fpc_2.1-11.1       
 [49] gbRd_0.4-11         lmtest_0.9-36       stringr_1.3.1      
 [52] irlba_2.3.2         gtools_3.8.1        DEoptimR_1.0-8     
 [55] MASS_7.3-50         zoo_1.8-3           scales_1.0.0       
 [58] doSNOW_1.0.16       parallel_3.4.3      RColorBrewer_1.1-2 
 [61] yaml_2.2.0          reticulate_1.10     pbapply_1.3-4      
 [64] gridExtra_2.3       rpart_4.1-13        segmented_0.5-3.0  
 [67] latticeExtra_0.6-28 stringi_1.2.4       foreach_1.4.4      
 [70] checkmate_1.8.5     caTools_1.17.1.1    bibtex_0.4.2       
 [73] Rdpack_0.9-0        SDMTools_1.1-221    rlang_0.2.2        
 [76] pkgconfig_2.0.2     dtw_1.20-1          prabclus_2.2-6     
 [79] bitops_1.0-6        evaluate_0.11       lattice_0.20-35    
 [82] ROCR_1.0-7          purrr_0.2.5         bindr_0.1.1        
 [85] labeling_0.3        htmlwidgets_1.2     bit_1.1-14         
 [88] tidyselect_0.2.4    plyr_1.8.4          magrittr_1.5       
 [91] R6_2.2.2            snow_0.4-2          gplots_3.0.1       
 [94] Hmisc_4.1-1         pillar_1.3.0        whisker_0.3-2      
 [97] foreign_0.8-70      withr_2.1.2         fitdistrplus_1.0-9 
[100] mixtools_1.1.0      survival_2.42-6     nnet_7.3-12        
[103] tsne_0.1-3          tibble_1.4.2        crayon_1.3.4       
[106] hdf5r_1.0.0         KernSmooth_2.23-15  rmarkdown_1.10     
[109] grid_3.4.3          data.table_1.11.4   git2r_0.23.0       
[112] metap_1.0           digest_0.6.15       diptest_0.75-7     
[115] tidyr_0.8.1         R.utils_2.7.0       stats4_3.4.3       
[118] munsell_0.5.0

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