Last updated: 2018-10-30

workflowr checks: (Click a bullet for more information)

✔ R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
✔ Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
✔ Seed: set.seed(20181026)

The command set.seed(20181026) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.
✔ Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
✔ Repository version: f7080f1
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
```
Ignored files:
    Ignored:    docs/figure/

Untracked files:
    Untracked:  code/out/
    Untracked:  code/run-alignment.R
    Untracked:  plots/

Unstaged changes:
    Modified:   code/regressout-cellcycle.R
```
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

Expand here to see past versions:

File	Version	Author	Date	Message
Rmd	f7080f1	PytrikFolkertsma	2018-10-30	wflow_publish(c(“analysis/10x-180504-general-analysis.Rmd”,

Analysis of the 10x samples: - tSNE plots - Cell cycle regression - PCA - Alignment - Marker gene expression - tSNE colored on metadata

library(Seurat)

Loading required package: ggplot2

Loading required package: cowplot


Attaching package: 'cowplot'

The following object is masked from 'package:ggplot2':

    ggsave

Loading required package: Matrix

library(ggplot2)
library(dplyr)


Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

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

QC Plots

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

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

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

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

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)

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)

tSNE colored on subtissue.

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

tSNE colored by cell cycle phase.

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

Clusters

Some clustering with different resolutions. res=0.5

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

res=0.7

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

res=1

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

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

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')

No cell cycle effect anymore.

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

Subtissues

plot_grid(t1, t2, t3, t4)

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

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)

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

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)

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

Alignment

Alignment of the data with and without cell cycle effects regressed out. Both were aligned on 30 subspaces, tSNE was performed on the first 15 CCs.

tSNE of the aligned data.

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

tSNE of the aligned data coloured on cell cycle phase.

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

tSNE of the aligned data with cell cycle effects regressed out.

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

tSNE of the aligned data with cell cycle effects regressed out, colored by phase.

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

tSNE of the aligned data with cell cycle effects regressed out, colored by subtissue

#TSNEPlot(all10x.aligned.ccregout, group.by='sample_name2', pt.size=0.1)

Metadata plots

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

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

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

Diff

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

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)

ucp1.ctrl

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

ucp1.ne

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

bmi

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

age

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

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

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

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

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)

Figures for report

fig1

#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=c('a', 'b', 'c'), nrow=1
)
save_plot("plots/sfig1_180504_qcplots.pdf", sfig1, base_width=12, base_height=3)
sfig1

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

This reproducible R Markdown analysis was created with workflowr 1.1.1

R Notebook