Last updated: 2019-01-02

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✔ R Markdown file: up-to-date

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✔ Environment: empty

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Ignored files:
    Ignored:    code/.ipynb_checkpoints/
    Ignored:    output/10x-180504
    Ignored:    output/10x-180504-aligned
    Ignored:    output/10x-180504-aligned-metageneplot
    Ignored:    output/10x-180504-beforeQC
    Ignored:    output/10x-180504-beforeqc
    Ignored:    output/10x-180504-cca-discardedcells
    Ignored:    output/10x-180504-ccregout
    Ignored:    output/10x-180504-ccregout-aligned
    Ignored:    output/10x-180504-ccregout-cca-discardedcells
    Ignored:    output/10x-180831
    Ignored:    output/10x-180831-T1T2T3
    Ignored:    output/10x-180831-T4T5
    Ignored:    output/10x-180831-beforeqc
    Ignored:    output/10x-180831-notcleaned
    Ignored:    output/monocle/

Untracked files:
    Untracked:  analysis/.ipynb_checkpoints/velocyto_notebook_180831-checkpoint.ipynb
    Untracked:  analysis/velocyto_notebook_180831.ipynb
    Untracked:  output/velocyto/10x-180831.hdf5
    Untracked:  output/velocyto/10x-180831.loom
    Untracked:  tables/10x-180831-metadata.txt
    Untracked:  tables/BEAM_GSEA/

Unstaged changes:
    Deleted:    analysis/.ipynb_checkpoints/velocyto_notebook_180504-Copy1-checkpoint.ipynb
    Modified:   analysis/10x-180504-general-analysis.Rmd
    Deleted:    analysis/velocyto_notebook_180504-Copy1.ipynb
    Modified:   code/velocyto_preprocess.py
    Modified:   code/velocyto_workflow.py
    Modified:   plots/180504_pca_tsne.pdf
    Modified:   plots/supplementary_figures/sfig_180504_pcelbow.pdf
    Modified:   plots/supplementary_figures/sfig_180504_qcplots.pdf

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	9080c32	PytrikFolkertsma	2019-01-02	wflow_publish(c(“analysis/10x-180831-general-analysis.Rmd”))
Rmd	603cd15	PytrikFolkertsma	2019-01-01	updates
html	387bfa6	PytrikFolkertsma	2018-11-11	Build site.
Rmd	dc4a8ca	PytrikFolkertsma	2018-11-11	wflow_publish(c(“analysis/10x-180831-general-analysis.Rmd”))

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

The number of genes expressed descreases over time.

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

Expand here to see past versions of fig1-1.png:

Version	Author	Date
387bfa6	PytrikFolkertsma	2018-11-11

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

Expand here to see past versions of unnamed-chunk-2-1.png:

Version	Author	Date
387bfa6	PytrikFolkertsma	2018-11-11

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

Expand here to see past versions of unnamed-chunk-3-1.png:

Version	Author	Date
387bfa6	PytrikFolkertsma	2018-11-11

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)

Expand here to see past versions of unnamed-chunk-4-1.png:

Version	Author	Date
387bfa6	PytrikFolkertsma	2018-11-11

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)

Expand here to see past versions of fig2-1.png:

Version	Author	Date
387bfa6	PytrikFolkertsma	2018-11-11

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)

Expand here to see past versions of fig3-1.png:

Version	Author	Date
387bfa6	PytrikFolkertsma	2018-11-11

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)

Expand here to see past versions of fig4-1.png:

Version	Author	Date
387bfa6	PytrikFolkertsma	2018-11-11

TSNE

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

Expand here to see past versions of unnamed-chunk-5-1.png:

Version	Author	Date
387bfa6	PytrikFolkertsma	2018-11-11

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

Expand here to see past versions of unnamed-chunk-6-1.png:

Version	Author	Date
387bfa6	PytrikFolkertsma	2018-11-11

Cluster 11 = mixture cluster.

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

Expand here to see past versions of unnamed-chunk-7-1.png:

Version	Author	Date
387bfa6	PytrikFolkertsma	2018-11-11

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

Expand here to see past versions of fig5-1.png:

Version	Author	Date
387bfa6	PytrikFolkertsma	2018-11-11

Metadata feature plots

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

Expand here to see past versions of unnamed-chunk-8-1.png:

Version	Author	Date
387bfa6	PytrikFolkertsma	2018-11-11

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

Expand here to see past versions of unnamed-chunk-9-1.png:

Version	Author	Date
387bfa6	PytrikFolkertsma	2018-11-11

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

Expand here to see past versions of unnamed-chunk-10-1.png:

Version	Author	Date
387bfa6	PytrikFolkertsma	2018-11-11

Markergene expression

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

Expand here to see past versions of unnamed-chunk-11-1.png:

Version	Author	Date
387bfa6	PytrikFolkertsma	2018-11-11

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

Expand here to see past versions of unnamed-chunk-12-1.png:

Version	Author	Date
387bfa6	PytrikFolkertsma	2018-11-11

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

Expand here to see past versions of unnamed-chunk-13-1.png:

Version	Author	Date
387bfa6	PytrikFolkertsma	2018-11-11

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

Expand here to see past versions of unnamed-chunk-14-1.png:

Version	Author	Date
387bfa6	PytrikFolkertsma	2018-11-11

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)

Expand here to see past versions of fig6-1.png:

Version	Author	Date
387bfa6	PytrikFolkertsma	2018-11-11

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)

Expand here to see past versions of fig7-1.png:

Version	Author	Date
387bfa6	PytrikFolkertsma	2018-11-11

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

Expand here to see past versions of fig8-1.png:

Version	Author	Date
387bfa6	PytrikFolkertsma	2018-11-11

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

Expand here to see past versions of unnamed-chunk-15-1.png:

Version	Author	Date
387bfa6	PytrikFolkertsma	2018-11-11

Figures for report

fig <- plot_grid(
  PCAPlot(seurobj, group.by='timepoint', pt.size=0.1),
  TSNEPlot(seurobj, group.by='timepoint', pt.size=0.1),
  labels='auto', nrow=1
)

#save_plot("../plots/180831_pca_tsne.pdf", fig, base_width=12, base_height=5)
fig

sfig1 <- PCElbowPlot(seurobj, num.pc=50) #TSNE+clustering run on 21 PC's.
#save_plot("../plots/supplementary_figures/sfig_180831_pcelbow.pdf", sfig1, base_width=6, base_height=4)
sfig1

sfig2 <- TSNEPlot(seurobj, group.by='Phase', pt.size=0.1)

#save_plot("../plots/supplementary_figures/sfig_180831_tsne_cellcycle.pdf", sfig2, base_width=6, base_height=4.5)
sfig2

sfig3 <- plot_grid(
  VlnPlot(seurobj, c("nGene"), group.by='timepoint', point.size.use=-1),
  VlnPlot(seurobj, c("nUMI"), group.by='timepoint', point.size.use=-1),
  VlnPlot(seurobj, c("percent.mito"), group.by='timepoint', point.size.use=-1),
  labels='auto', nrow=1
)
#save_plot("../plots/supplementary_figures/sfig_180831_ngene-numi-pm.pdf", sfig3, base_width=12, base_height=3)
sfig3

Session information

sessionInfo()

R version 3.4.3 (2017-11-30)
Platform: x86_64-redhat-linux-gnu (64-bit)
Running under: Storage

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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