Last updated: 2018-11-07

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```
Ignored files:
    Ignored:    output/monocle/

Unstaged changes:
    Modified:   plots/180504_alignment.pdf
    Modified:   plots/supplementary_figures/sfig_180504_alignment-discardedcells.pdf
    Modified:   plots/supplementary_figures/sfig_180504_biweightplots.pdf
    Modified:   plots/supplementary_figures/sfig_180504_qcplots.pdf
```
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File	Version	Author	Date	Message
Rmd	1c178af	PytrikFolkertsma	2018-11-07	wflow_publish(c(“analysis/10x-180504-alignment.Rmd”, “analysis/10x-180504-beforeQC.Rmd”,
html	3b4a19e	PytrikFolkertsma	2018-11-07	Build site.
Rmd	039a705	PytrikFolkertsma	2018-11-06	monocle

Notebook for Monocle trajectories of the 180504 dataset.

To investigate the robustness of the trajectories, the dataset was randomly downsampled on 1000 cells per sample. 4 different types of regressions were used when running the DDRTree algorithm.

library(Seurat)
library(monocle)

Loading all datasets.

cds.11 <- readRDS('output/monocle/180504/10x-180504-monocle-11')
cds.cc.11 <- readRDS('output/monocle/180504/10x-180504-monocle-cc-11')
cds.pm.umi.11 <- readRDS('output/monocle/180504/10x-180504-monocle-pm-umi-11')
cds.pm.umi.cc.11 <- readRDS('output/monocle/180504/10x-180504-monocle-pm-umi-cc-11')

cds.27 <- readRDS('output/monocle/180504/10x-180504-monocle-27')
cds.cc.27 <- readRDS('output/monocle/180504/10x-180504-monocle-cc-27')
cds.pm.umi.27 <- readRDS('output/monocle/180504/10x-180504-monocle-pm-umi-27')
cds.pm.umi.cc.27 <- readRDS('output/monocle/180504/10x-180504-monocle-pm-umi-cc-27')

cds.33 <- readRDS('output/monocle/180504/10x-180504-monocle-33')
cds.cc.33 <- readRDS('output/monocle/180504/10x-180504-monocle-cc-33')
cds.pm.umi.33 <- readRDS('output/monocle/180504/10x-180504-monocle-pm-umi-33')
cds.pm.umi.cc.33 <- readRDS('output/monocle/180504/10x-180504-monocle-pm-umi-cc-33')

cds.53 <- readRDS('output/monocle/180504/10x-180504-monocle-53')
cds.cc.53 <- readRDS('output/monocle/180504/10x-180504-monocle-cc-53')
cds.pm.umi.53 <- readRDS('output/monocle/180504/10x-180504-monocle-pm-umi-53')
cds.pm.umi.cc.53 <- readRDS('output/monocle/180504/10x-180504-monocle-pm-umi-cc-53')

Trajectories of one subset

Trajectories of one subset, coloured by depot and cell cycle phase. Topleft: no regression. Topright: cell cycle regression. Bottom left: percent.mito + nUMI regression. Bottom right: percent.mito + nUMI + cell cycle regression.

plot_grid(
  plot_cell_trajectory(cds.11, color_by='depot'),
  plot_cell_trajectory(cds.cc.11, color_by='depot'),
  plot_cell_trajectory(cds.pm.umi.11, color_by='depot'),
  plot_cell_trajectory(cds.pm.umi.cc.11, color_by='depot'),
  nrow=2)

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

Version	Author	Date
3b4a19e	PytrikFolkertsma	2018-11-07

plot_grid(
  plot_cell_trajectory(cds.11, color_by='Phase'),
  plot_cell_trajectory(cds.cc.11, color_by='Phase'),
  plot_cell_trajectory(cds.pm.umi.11, color_by='Phase'),
  plot_cell_trajectory(cds.pm.umi.cc.11, color_by='Phase'),
  nrow=2)

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

Version	Author	Date
3b4a19e	PytrikFolkertsma	2018-11-07

Trajectories, no regression

Trajectories of the 4 subsets with no variables regressed out. The ordering of the cells seems to be influenced by the cell cycle state (though not completely). Interestingly in three of the trajectories there is a branch split.

plot_grid(
  plot_cell_trajectory(cds.11, color_by='Phase'),
  plot_cell_trajectory(cds.27, color_by='Phase'),
  plot_cell_trajectory(cds.33, color_by='Phase'),
  plot_cell_trajectory(cds.53, color_by='Phase'),
  nrow=2)

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

Version	Author	Date
3b4a19e	PytrikFolkertsma	2018-11-07

Does the branch split discriminate between depots?

plot_grid(
  plot_cell_trajectory(cds.11, color_by='depot'),
  plot_cell_trajectory(cds.27, color_by='depot'),
  plot_cell_trajectory(cds.33, color_by='depot'),
  plot_cell_trajectory(cds.53, color_by='depot'),
  nrow=2)

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

Version	Author	Date
3b4a19e	PytrikFolkertsma	2018-11-07

Trajectories, cell cycle regression

Trajectories of the 4 subsets with cell cycle effects regressed out. Topleft, bottom left and bottom right look similar but all have different number of branching points.

plot_grid(
  plot_cell_trajectory(cds.cc.11, color_by='Phase'),
  plot_cell_trajectory(cds.cc.27, color_by='Phase'),
  plot_cell_trajectory(cds.cc.33, color_by='Phase'),
  plot_cell_trajectory(cds.cc.53, color_by='Phase'),
  nrow=2)

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

Version	Author	Date
3b4a19e	PytrikFolkertsma	2018-11-07

Coloured on depot. Branches do not

plot_grid(
  plot_cell_trajectory(cds.cc.11, color_by='depot'),
  plot_cell_trajectory(cds.cc.27, color_by='depot'),
  plot_cell_trajectory(cds.cc.33, color_by='depot'),
  plot_cell_trajectory(cds.cc.53, color_by='depot'),
  nrow=2)

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

Version	Author	Date
3b4a19e	PytrikFolkertsma	2018-11-07

Trajectories, percent.mito + nUMI regression

In general all cells seem to follow one direction, and the general shape of the trajectories are similar. The branch splits in the trajectories above (no regression and cell cycle regression) could be the effects of differences in percent.mito and nUMI, and that’s why we don’t see that here.

plot_grid(
  plot_cell_trajectory(cds.pm.umi.11, color_by='Phase'),
  plot_cell_trajectory(cds.pm.umi.27, color_by='Phase'),
  plot_cell_trajectory(cds.pm.umi.33, color_by='Phase'),
  plot_cell_trajectory(cds.pm.umi.53, color_by='Phase'),
  nrow=2)

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

Version	Author	Date
3b4a19e	PytrikFolkertsma	2018-11-07

Trajectories, percent.mito + nUMI + cell cycle regression

Trajectories with percent.mito, nUMI and cell cycle effects regressed out. Doesn’t look very convincing.

plot_grid(
  plot_cell_trajectory(cds.pm.umi.cc.11, color_by='depot'),
  plot_cell_trajectory(cds.pm.umi.cc.27, color_by='depot'),
  plot_cell_trajectory(cds.pm.umi.cc.33, color_by='depot'),
  plot_cell_trajectory(cds.pm.umi.cc.53, color_by='depot'),
  nrow=2)

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

Version	Author	Date
3b4a19e	PytrikFolkertsma	2018-11-07

Conclusions

Differences between depots or fat type were not captured by Monocle. The preadipocytes are at this timepoint likely very similar too each other.
Regressions affect the resulting trajectories a lot.

Figures for report

fig <- plot_grid(
  plot_cell_trajectory(cds.11, color_by='Phase'),
  plot_cell_trajectory(cds.cc.11, color_by='depot'),
  nrow=1, labels='auto')
save_plot("plots/180504_monocle_noreg-ccreg.pdf", fig, base_width=12, base_height=5)
fig

Expand here to see past versions of fig9-1.png:

Version	Author	Date
3b4a19e	PytrikFolkertsma	2018-11-07

noreg <- plot_grid(
  plot_cell_trajectory(cds.11, color_by='Phase', cell_size = 0.5),
  plot_cell_trajectory(cds.27, color_by='Phase', cell_size = 0.5),
  plot_cell_trajectory(cds.33, color_by='Phase', cell_size = 0.5),
  plot_cell_trajectory(cds.53, color_by='Phase', cell_size = 0.5),
  nrow=1)

ccreg <- plot_grid(
  plot_cell_trajectory(cds.cc.11, color_by='Phase', cell_size = 0.5),
  plot_cell_trajectory(cds.cc.27, color_by='Phase', cell_size = 0.5),
  plot_cell_trajectory(cds.cc.33, color_by='Phase', cell_size = 0.5),
  plot_cell_trajectory(cds.cc.53, color_by='Phase', cell_size = 0.5),
  nrow=1)

pmumireg <- plot_grid(
  plot_cell_trajectory(cds.pm.umi.11, color_by='Phase', cell_size = 0.5),
  plot_cell_trajectory(cds.pm.umi.27, color_by='Phase', cell_size = 0.5),
  plot_cell_trajectory(cds.pm.umi.33, color_by='Phase', cell_size = 0.5),
  plot_cell_trajectory(cds.pm.umi.53, color_by='Phase', cell_size = 0.5),
  nrow=1)

pmumiccreg <- plot_grid(
  plot_cell_trajectory(cds.pm.umi.cc.11, color_by='Phase', cell_size = 0.5),
  plot_cell_trajectory(cds.pm.umi.cc.27, color_by='Phase', cell_size = 0.5),
  plot_cell_trajectory(cds.pm.umi.cc.33, color_by='Phase', cell_size = 0.5),
  plot_cell_trajectory(cds.pm.umi.cc.53, color_by='Phase', cell_size = 0.5),
  nrow=1)

sfig <- plot_grid(
  noreg,
  ccreg,
  pmumireg,
  pmumiccreg,
  labels='auto', nrow=4
)

save_plot("plots/supplementary_figures/sfig_180504_monocle.pdf", sfig, base_width=12, base_height=12)
sfig

Expand here to see past versions of fig10-1.png:

Version	Author	Date
3b4a19e	PytrikFolkertsma	2018-11-07

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

other attached packages:
 [1] monocle_2.6.4       DDRTree_0.1.5       irlba_2.3.2        
 [4] VGAM_1.0-6          Biobase_2.38.0      BiocGenerics_0.24.0
 [7] Seurat_2.3.4        Matrix_1.2-14       cowplot_0.9.3      
[10] 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           ggrepel_0.8.0         
 [13] flexmix_2.3-14         bit64_0.9-7            mvtnorm_1.0-8         
 [16] codetools_0.2-15       R.methodsS3_1.7.1      docopt_0.6            
 [19] robustbase_0.93-2      knitr_1.20             Formula_1.2-3         
 [22] jsonlite_1.5           workflowr_1.1.1        ica_1.0-2             
 [25] cluster_2.0.7-1        kernlab_0.9-27         png_0.1-7             
 [28] R.oo_1.22.0            pheatmap_1.0.10        compiler_3.4.3        
 [31] httr_1.3.1             backports_1.1.2        assertthat_0.2.0      
 [34] lazyeval_0.2.1         limma_3.34.9           lars_1.2              
 [37] acepack_1.4.1          htmltools_0.3.6        tools_3.4.3           
 [40] bindrcpp_0.2.2         igraph_1.2.2           gtable_0.2.0          
 [43] glue_1.3.0             RANN_2.6               reshape2_1.4.3        
 [46] dplyr_0.7.6            Rcpp_0.12.18           slam_0.1-43           
 [49] trimcluster_0.1-2.1    gdata_2.18.0           ape_5.1               
 [52] nlme_3.1-137           iterators_1.0.10       fpc_2.1-11.1          
 [55] gbRd_0.4-11            lmtest_0.9-36          stringr_1.3.1         
 [58] gtools_3.8.1           DEoptimR_1.0-8         MASS_7.3-50           
 [61] zoo_1.8-3              scales_1.0.0           doSNOW_1.0.16         
 [64] RColorBrewer_1.1-2     yaml_2.2.0             reticulate_1.10       
 [67] pbapply_1.3-4          gridExtra_2.3          rpart_4.1-13          
 [70] segmented_0.5-3.0      fastICA_1.2-1          latticeExtra_0.6-28   
 [73] stringi_1.2.4          foreach_1.4.4          checkmate_1.8.5       
 [76] caTools_1.17.1.1       densityClust_0.3       bibtex_0.4.2          
 [79] matrixStats_0.54.0     Rdpack_0.9-0           SDMTools_1.1-221      
 [82] rlang_0.2.2            pkgconfig_2.0.2        dtw_1.20-1            
 [85] prabclus_2.2-6         bitops_1.0-6           qlcMatrix_0.9.7       
 [88] evaluate_0.11          lattice_0.20-35        ROCR_1.0-7            
 [91] purrr_0.2.5            bindr_0.1.1            labeling_0.3          
 [94] htmlwidgets_1.2        bit_1.1-14             tidyselect_0.2.4      
 [97] plyr_1.8.4             magrittr_1.5           R6_2.2.2              
[100] snow_0.4-2             gplots_3.0.1           Hmisc_4.1-1           
[103] combinat_0.0-8         pillar_1.3.0           whisker_0.3-2         
[106] foreign_0.8-71         withr_2.1.2            fitdistrplus_1.0-9    
[109] mixtools_1.1.0         survival_2.42-6        nnet_7.3-12           
[112] tsne_0.1-3             tibble_1.4.2           crayon_1.3.4          
[115] hdf5r_1.0.0            KernSmooth_2.23-15     rmarkdown_1.10        
[118] viridis_0.5.1          grid_3.4.3             data.table_1.11.4     
[121] FNN_1.1.2.1            git2r_0.23.0           sparsesvd_0.1-4       
[124] HSMMSingleCell_0.112.0 metap_1.0              digest_0.6.16         
[127] diptest_0.75-7         tidyr_0.8.1            R.utils_2.7.0         
[130] munsell_0.5.0          viridisLite_0.3.0

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