Last updated: 2019-03-29

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Knit directory: 10x-adipocyte-analysis/

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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(monocle)

Loading required package: Biobase

Loading required package: BiocGenerics

Loading required package: parallel


Attaching package: 'BiocGenerics'

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

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

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

    colMeans, colSums, rowMeans, rowSums, which

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

    IQR, mad, sd, var, xtabs

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

    anyDuplicated, append, as.data.frame, basename, cbind,
    colMeans, colnames, colSums, dirname, do.call, duplicated,
    eval, evalq, Filter, Find, get, grep, grepl, intersect,
    is.unsorted, lapply, lengths, Map, mapply, match, mget, order,
    paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind,
    Reduce, rowMeans, rownames, rowSums, sapply, setdiff, sort,
    table, tapply, union, unique, unsplit, which, which.max,
    which.min

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Loading required package: VGAM

Loading required package: stats4

Loading required package: splines

Loading required package: DDRTree

Loading required package: irlba

library(colorspace)

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

Colors

hcl_palettes(plot = TRUE)

Timepoint colors

plot_grid(
  TSNEPlot(seurobj, group.by='timepoint', pt.size=1, colors.use=rev(sequential_hcl(5, palette='TealGrn'))),
  TSNEPlot(seurobj, group.by='timepoint', pt.size=1, colors.use=rev(sequential_hcl(5, palette='BluGrn'))),
  TSNEPlot(seurobj, group.by='timepoint', pt.size=1, colors.use=rev(sequential_hcl(5, palette='Blue Yellow'))),
  TSNEPlot(seurobj, group.by='timepoint', pt.size=1, colors.use=rev(sequential_hcl(5, palette='PurpOr')))
)

Custom colors timepoints

plot_grid(
  TSNEPlot(seurobj, group.by='timepoint', pt.size=1, colors.use=rev(c('#0263A1', '#008FB0', '#4CB6BC', '#8ED7CA', '#CBF1DE'))),
  TSNEPlot(seurobj, group.by='timepoint', pt.size=1, colors.use=rev(c('#4847A1', '#0087BE', '#00BED1', '#3DEFDA', '#B0FFDF'))),
  TSNEPlot(seurobj, group.by='timepoint', pt.size=1, colors.use=rev(c('#00589C','#008DA5', '#00B9A1', '#6DDD95', '#D7F797'))),
  TSNEPlot(seurobj, group.by='timepoint', pt.size=1, colors.use=rev(c('#0077B5', '#00A7C3', '#00CAC2', '#42E0B4', '#8CE599'))),
  TSNEPlot(seurobj, group.by='timepoint', pt.size=1, colors.use=rev(c('#005090','#007C98','#00A39B','#00C598','#86E094'))),
   TSNEPlot(seurobj, group.by='timepoint', pt.size=1, colors.use=c('#f5eb82','#c8d29b','#bca986','#bf877f','#a8cec2')), ncol=2, labels=c('custom1', 'custom2', 'custom3', 'custom4', 'custom5', 'custom6')
)

Depots

plot_grid(
  TSNEPlot(seurobj, group.by='depot', pt.size=1, colors.use=c('#825c2a', '#ebd1ac', '#5c3724', '#eab476')),
  TSNEPlot(seurobj, group.by='depot', pt.size=1, colors.use=c('#82643a', '#d1a567', '#6c4431', '#e3c78a')),
  TSNEPlot(seurobj, group.by='depot', pt.size=1, colors.use=c('#825c2a', '#ebd1ac', '#5c3724', '#d1a567')),
  TSNEPlot(seurobj, group.by='depot', pt.size=1, colors.use=c('brown', 'orange', '#5c3724', '#d1a567')),
  ncol=2
)

Monocle

cds <- readRDS('output/monocle/180831/monocle_T1T2T3_T4T5_res1.5/10x-180831-monocle-metadata')

plot_grid(
  plot_cell_trajectory(cds, color_by='timepoint') + scale_color_manual(values=rev(c('#0077B5', '#00A7C3', '#00CAC2', '#42E0B4', '#8CE599')), name = "Timepoint"),
  plot_cell_trajectory(cds, color_by='timepoint') + geom_point(color='white', size=5) + geom_point(aes(colour=timepoint), alpha=0.1) + scale_color_manual(values=rev(c('#0077B5', '#00A7C3', '#00CAC2', '#42E0B4', '#8CE599')), name = "Timepoint"),
  plot_cell_trajectory(cds, color_by='timepoint') + scale_color_manual(values=rev(sequential_hcl(5, palette='TealGrn')), name = "Timepoint"),
  plot_cell_trajectory(cds, color_by='timepoint') + geom_point(color='white', size=5) + geom_point(aes(colour=timepoint), alpha=0.1) + scale_color_manual(values=rev(sequential_hcl(5, palette='TealGrn')), name = "Timepoint"),
  plot_cell_trajectory(cds),
  plot_cell_trajectory(cds) + scale_color_manual(values=c('#fbea7d', '#e27268', '#7ba2c3'), name='State'),
  plot_cell_trajectory(cds, color_by='type') + scale_color_manual(values=c('#825c2a', '#ebd1ac'), name='Type'),
  plot_cell_trajectory(cds, color_by='depot') + scale_color_manual(values=c('#82643a', '#d1a567', '#6c4431', '#e3c78a'), name='Depot'),
  ncol=2
)

Monocle predictions in Seurat tSNE

plot_grid(
  TSNEPlot(seurobj, group.by='State.old', pt.size=1, colors.use=c('#ecdd83', '#e27268', '#93c8bc')),
  TSNEPlot(seurobj, group.by='State.old', pt.size=1, colors.use=c('#fbea7d', '#e27268', '#93c8bc')),
  TSNEPlot(seurobj, group.by='State.old', pt.size=1, colors.use=c('#f6776f', '#1bb840', '#649efc')),
  TSNEPlot(seurobj, group.by='State.old', pt.size=1, colors.use=c('#66cc7d', '#f6776f', '#649efc')),
  ncol=2
)

Final figures

plot_grid(
  TSNEPlot(seurobj, group.by='depot', pt.size=1, colors.use=c('#82643a', '#d1a567', '#6c4431', '#e3c78a')),
  TSNEPlot(seurobj, group.by='timepoint', pt.size=1, colors.use=rev(c('#0077B5', '#00A7C3', '#00CAC2', '#42E0B4', '#8CE599'))),
  plot_cell_trajectory(cds, color_by='depot') + scale_color_manual(values=c('#82643a', '#d1a567', '#6c4431', '#e3c78a'), name='Depot'),
  plot_cell_trajectory(cds, color_by='timepoint') + geom_point(color='white', size=5) + geom_point(aes(colour=timepoint), alpha=0.1) + scale_color_manual(values=rev(c('#0077B5', '#00A7C3', '#00CAC2', '#42E0B4', '#8CE599')), name = "Timepoint"),
  TSNEPlot(seurobj, group.by='State.labels', pt.size=1, colors.use=c('#93c8bc', '#fbea7d', '#e27268')),
  ncol=2
)

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] splines   stats4    parallel  stats     graphics  grDevices utils    
 [8] datasets  methods   base     

other attached packages:
 [1] colorspace_1.4-1    monocle_2.8.0       DDRTree_0.1.5      
 [4] irlba_2.3.3         VGAM_1.1-1          Biobase_2.42.0     
 [7] BiocGenerics_0.28.0 Seurat_2.3.4        Matrix_1.2-17      
[10] cowplot_0.9.4       ggplot2_3.1.0      

loaded via a namespace (and not attached):
  [1] Rtsne_0.15             class_7.3-15           modeltools_0.2-22     
  [4] ggridges_0.5.1         mclust_5.4.3           rprojroot_1.3-2       
  [7] htmlTable_1.13.1       base64enc_0.1-3        fs_1.2.7              
 [10] rstudioapi_0.10        proxy_0.4-23           npsurv_0.4-0          
 [13] ggrepel_0.8.0          flexmix_2.3-15         bit64_0.9-7           
 [16] mvtnorm_1.0-10         codetools_0.2-16       R.methodsS3_1.7.1     
 [19] docopt_0.6.1           lsei_1.2-0             robustbase_0.93-4     
 [22] knitr_1.22             jsonlite_1.6           Formula_1.2-3         
 [25] workflowr_1.2.0        ica_1.0-2              cluster_2.0.7-1       
 [28] kernlab_0.9-27         png_0.1-7              R.oo_1.22.0           
 [31] pheatmap_1.0.12        compiler_3.5.3         httr_1.4.0            
 [34] backports_1.1.3        assertthat_0.2.1       lazyeval_0.2.2        
 [37] limma_3.36.5           lars_1.2               acepack_1.4.1         
 [40] htmltools_0.3.6        tools_3.5.3            igraph_1.2.4          
 [43] gtable_0.3.0           glue_1.3.1             reshape2_1.4.3        
 [46] RANN_2.6.1             dplyr_0.8.0.1          Rcpp_1.0.1            
 [49] slam_0.1-45            trimcluster_0.1-2.1    gdata_2.18.0          
 [52] ape_5.3                nlme_3.1-137           iterators_1.0.10      
 [55] fpc_2.1-11.1           gbRd_0.4-11            lmtest_0.9-36         
 [58] xfun_0.5               stringr_1.4.0          gtools_3.8.1          
 [61] DEoptimR_1.0-8         MASS_7.3-51.1          zoo_1.8-5             
 [64] scales_1.0.0           doSNOW_1.0.16          RColorBrewer_1.1-2    
 [67] yaml_2.2.0             reticulate_1.11.1      pbapply_1.4-0         
 [70] gridExtra_2.3          rpart_4.1-13           segmented_0.5-3.0     
 [73] fastICA_1.2-1          latticeExtra_0.6-28    stringi_1.4.3         
 [76] foreach_1.4.4          checkmate_1.9.1        caTools_1.17.1.2      
 [79] densityClust_0.3       bibtex_0.4.2           matrixStats_0.54.0    
 [82] Rdpack_0.10-1          SDMTools_1.1-221       rlang_0.3.2           
 [85] pkgconfig_2.0.2        dtw_1.20-1             prabclus_2.2-7        
 [88] bitops_1.0-6           qlcMatrix_0.9.7        evaluate_0.13         
 [91] lattice_0.20-38        ROCR_1.0-7             purrr_0.3.2           
 [94] labeling_0.3           htmlwidgets_1.3        bit_1.1-14            
 [97] tidyselect_0.2.5       plyr_1.8.4             magrittr_1.5          
[100] R6_2.4.0               snow_0.4-3             gplots_3.0.1.1        
[103] Hmisc_4.2-0            combinat_0.0-8         pillar_1.3.1          
[106] foreign_0.8-71         withr_2.1.2            fitdistrplus_1.0-14   
[109] mixtools_1.1.0         survival_2.43-3        nnet_7.3-12           
[112] tsne_0.1-3             tibble_2.1.1           crayon_1.3.4          
[115] hdf5r_1.1.1            KernSmooth_2.23-15     rmarkdown_1.12        
[118] viridis_0.5.1          grid_3.5.3             data.table_1.12.0     
[121] FNN_1.1.3              git2r_0.25.2           sparsesvd_0.1-4       
[124] HSMMSingleCell_0.114.0 metap_1.1              digest_0.6.18         
[127] diptest_0.75-7         tidyr_0.8.3            R.utils_2.8.0         
[130] munsell_0.5.0          viridisLite_0.3.0

R Notebook

Colors

Final figures