Last updated: 2019-01-01

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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:  code/mixture_cluster_gsea.R
      Untracked:  tables/10x-180504-stressed-cells-analysis.txt
      Untracked:  tables/GSEA_mixture-cluster.txt
      Untracked:  tables/Revigo_input_mixture-cluster.txt
      Untracked:  tables/Revigo_mixture-cluster.csv
      Untracked:  tables/Revigo_mixture-cluster_cleaned.txt
  
  Unstaged changes:
      Modified:   analysis/10x-180504-general-analysis.Rmd
      Modified:   analysis/10x-180831-general-analysis.Rmd
      Modified:   plots/180504_mixture.pdf
      Modified:   plots/supplementary_figures/sfig_180831_bad-quality-cluster.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.

library(Seurat)
data <- readRDS('output/10x-180831-notcleaned')

TSNE plots of the data.

t1 <- TSNEPlot(data, group.by='timepoint', pt.size=0.1)

t2 <- TSNEPlot(data, group.by='Phase', pt.size=0.1)

t3 <- FeaturePlot(data, features.plot=c('nUMI'), cols.use=c('gray', 'blue'), no.legend = F)

t4 <- DimPlot(data, reduction.use='tsne', cells.highlight = rownames(data@meta.data)[data@meta.data$res.0.5 == 1], cols.highlight = 'blue', cols.use='gray')

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

Remove bad quality cluster 1 from the data.

data_cleaned <- SubsetData(data, cells.use=rownames(data@meta.data)[data@meta.data$res.0.5 != 1])

TSNEPlot(data_cleaned, group.by='res.0.5', pt.size=0.1)

Save the cleaned up Seurat object to run preprocessing again (new PCA, clustering and tSNE).

#saveRDS(data_cleaned, 'output/10x-180831')

sfig <- plot_grid(
 t4,
 t1,
 t2,
 t3$nUMI,
 nrow=2, labels='auto'
)

#save_plot('plots/supplementary_figures/sfig_180831_bad-quality-cluster.pdf', sfig, base_width=12, base_height = 9)
sfig

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 Seurat_2.3.4   Matrix_1.2-14  cowplot_0.9.3 
[5] 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      dplyr_0.7.6        
 [43] Rcpp_0.12.18        trimcluster_0.1-2.1 gdata_2.18.0       
 [46] ape_5.1             nlme_3.1-137        iterators_1.0.10   
 [49] fpc_2.1-11.1        gbRd_0.4-11         lmtest_0.9-36      
 [52] stringr_1.3.1       irlba_2.3.2         gtools_3.8.1       
 [55] DEoptimR_1.0-8      MASS_7.3-50         zoo_1.8-3          
 [58] scales_1.0.0        doSNOW_1.0.16       parallel_3.4.3     
 [61] RColorBrewer_1.1-2  yaml_2.2.0          reticulate_1.10    
 [64] pbapply_1.3-4       gridExtra_2.3       rpart_4.1-13       
 [67] segmented_0.5-3.0   latticeExtra_0.6-28 stringi_1.2.4      
 [70] foreach_1.4.4       checkmate_1.8.5     caTools_1.17.1.1   
 [73] bibtex_0.4.2        Rdpack_0.9-0        SDMTools_1.1-221   
 [76] rlang_0.2.2         pkgconfig_2.0.2     dtw_1.20-1         
 [79] prabclus_2.2-6      bitops_1.0-6        evaluate_0.11      
 [82] lattice_0.20-35     ROCR_1.0-7          purrr_0.2.5        
 [85] bindr_0.1.1         labeling_0.3        htmlwidgets_1.2    
 [88] bit_1.1-14          tidyselect_0.2.4    plyr_1.8.4         
 [91] magrittr_1.5        R6_2.2.2            snow_0.4-2         
 [94] gplots_3.0.1        Hmisc_4.1-1         pillar_1.3.0       
 [97] whisker_0.3-2       foreign_0.8-70      withr_2.1.2        
[100] fitdistrplus_1.0-9  mixtools_1.1.0      survival_2.42-6    
[103] nnet_7.3-12         tsne_0.1-3          tibble_1.4.2       
[106] crayon_1.3.4        hdf5r_1.0.0         KernSmooth_2.23-15 
[109] rmarkdown_1.10      grid_3.4.3          data.table_1.11.4  
[112] git2r_0.23.0        metap_1.0           digest_0.6.15      
[115] diptest_0.75-7      tidyr_0.8.1         R.utils_2.7.0      
[118] stats4_3.4.3        munsell_0.5.0      

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