Last updated: 2018-11-08

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✔ Environment: empty

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✔ Session information: recorded

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✔ Repository version: 92ef278
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File	Version	Author	Date	Message
Rmd	92ef278	PytrikFolkertsma	2018-11-08	wflow_publish(c(“analysis/10x-180831-first-inspection.Rmd”))
html	c8f6254	PytrikFolkertsma	2018-11-08	Build site.
Rmd	f9da1a0	PytrikFolkertsma	2018-11-08	wflow_publish(c(“analysis/10x-180831-first-inspection.Rmd”))

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

TSNE plots of the data.

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

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

Version	Author	Date
c8f6254	PytrikFolkertsma	2018-11-08

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

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

Version	Author	Date
c8f6254	PytrikFolkertsma	2018-11-08

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

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

Version	Author	Date
c8f6254	PytrikFolkertsma	2018-11-08

#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)

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

Version	Author	Date
c8f6254	PytrikFolkertsma	2018-11-08

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)

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

Version	Author	Date
c8f6254	PytrikFolkertsma	2018-11-08

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

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

Save the data with the bad quality cluster in case it is needed for other plots.

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

Figures for report

#sfig <- plot_grid(
#  t4,
#  t1,
#  t2,
#  t3,
#  nrow=2, labels='auto'
#)
#save_plot('plots/supplementary_figures/sfig_180831_bad-quality-cluster.pdf', sfig, base_width=12, base_height = 9)
#sfig

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 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

This reproducible R Markdown analysis was created with workflowr 1.1.1

R Notebook

Figures for report

Session information