Last updated: 2018-11-11

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```
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File	Version	Author	Date	Message
Rmd	dc4a8ca	PytrikFolkertsma	2018-11-11	wflow_publish(c(“analysis/10x-180831-general-analysis.Rmd”))

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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(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

seurobj <- readRDS('output/10x-180831')

Quality control plots

For now the data was only filtered on 0.1 percent.mito. The subcluster from T1 with low UMI and gene counts was removed.

VlnPlot(seurobj, c("nGene", "percent.mito", "nUMI"), group.by='timepoint', nCol = 1, point.size.use=-1, size.x.use = 10)

GenePlot(seurobj, 'nUMI', 'nGene', cex.use = 0.5)

PCElbowPlot(seurobj, num.pc=50) #TSNE+clustering run on 21 PC's.

PCA

Interesting to see: T4 and T5 contain a lot more variation than T1, T2 and T3, and PC2 seems to split T4 and T5. Could the split in PC2 describe the cells developing into white or brown?

PCAPlot(seurobj, group.by='timepoint', pt.size=0.1)

A few clusters in the data have much higher expression of ‘ADIPOQ’, ‘SCD’, ‘RBP4’, ‘G0S2’, ‘PLIN4’, ‘FABP5’. This seems to be captured by PC2.

FeaturePlot(seurobj, reduction.use='pca', features.plot=c('ADIPOQ', 'SCD', 'RBP4', 'G0S2', 'PLIN4', 'FABP5'), pt.size=1, cols.use=c('gray', 'blue'), no.legend=F, nCol=2)

FeaturePlot(seurobj, reduction.use='pca', features.plot=c('PLA2G2A', 'MT1X', 'APOD', 'DPT', 'PTGDS', 'IGF2'), pt.size=1, cols.use=c('gray', 'blue'), no.legend=F, nCol=2)

PLA2G2A: http://www.jlr.org/content/early/2017/06/29/jlr.M076141 “…suggesting that PLA2G2A activates mitochondrial uncoupling in brown adipose tissue.”

PDGFRα/PDGFRβ signaling balance modulates progenitor cell differentiation into white and beige adipocytes. Based on PDGFRα or PDGFRβ deletion and ectopic expression experiments, we conclude that the PDGFRα/PDGFRβ signaling balance determines progenitor commitment to beige (PDGFRα) or white (PDGFRβ) adipogenesis. Our study suggests that adipocyte lineage specification and metabolism can be modulated through PDGFR signaling. http://dev.biologists.org/content/145/1/dev155861.long

FeaturePlot(seurobj, reduction.use='pca', features.plot=c('PDGFRA', 'PDGFRB'), pt.size=1, cols.use=c('gray', 'blue'), no.legend=F, nCol=2)

TSNE

TSNEPlot(seurobj, group.by='timepoint', pt.size=0.1)

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

Cluster 11 = mixture cluster.

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

VlnPlot(seurobj, group.by='res.0.5', features.plot=c('MALAT1', 'NEAT1'), point.size.use=-1)

Metadata feature plots

FeaturePlot(seurobj, reduction.use='tsne', features.plot = 'nUMI', cols.use=c('grey', 'blue'), no.legend=F)

FeaturePlot(seurobj, features.plot = 'percent.mito', cols.use=c('grey', 'blue'), no.legend = F)

FeaturePlot(seurobj, reduction.use='tsne', features.plot = 'nGene', cols.use=c('grey', 'blue'), no.legend = F)

Markergene expression

FeaturePlot(seurobj, reduction.use='tsne', features.plot = 'EBF2', cols.use=c('grey', 'blue'), no.legend = F)

FeaturePlot(seurobj, reduction.use='tsne', features.plot = 'TM4SF1', cols.use=c('grey', 'blue'), no.legend = F)

FeaturePlot(seurobj, reduction.use='tsne', features.plot = 'LY6K', cols.use=c('grey', 'blue'), no.legend = F)

FeaturePlot(seurobj, reduction.use='tsne', features.plot = 'PDGFRA', cols.use=c('grey', 'blue'), no.legend = F)

Markers literature

Marker genes for mature brown/beige compared to white mentioned by Seale 2016: UCP1, DIO2, CIDEA, PPARGC1A, PPARA, COX7A1, COX8B, PRDM16, EBF2. \

VlnPlot(seurobj, features.plot=c('UCP1', 'DIO2', 'CIDEA', 'PPARGC1A', 'PPARA', 'COX7A1', 'PRDM16', 'EBF2'), group.by='timepoint', point.size.use = -1, nCol=2)

Based on PDGFRα or PDGFRβ deletion and ectopic expression experiments, we conclude that the PDGFRα/PDGFRβ signaling balance determines progenitor commitment to beige (PDGFRα) or white (PDGFRβ) adipogenesis. Our study suggests that adipocyte lineage specification and metabolism can be modulated through PDGFR signaling. http://dev.biologists.org/content/145/1/dev155861.long

FeaturePlot(seurobj, reduction.use='pca', features.plot=c('PDGFRA', 'PDGFRB'), pt.size=1, cols.use=c('gray', 'blue'), no.legend=F, nCol=2)

FeaturePlot(seurobj, reduction.use='tsne', features.plot=c('PDGFRA', 'PDGFRB'), pt.size=1, cols.use=c('gray', 'blue'), no.legend=F, nCol=2)

GenePlot(SetAllIdent(seurobj, id='timepoint'), gene1='PDGFRA', gene2='PDGFRB', cex.use=0.5)

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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