Last updated: 2018-11-02

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    html eaa7e4a PytrikFolkertsma 2018-11-02 Build site.
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    Rmd 120215c PytrikFolkertsma 2018-11-02 general analysis + alignment

Notebook for alignment analsyis of the 180504 data.

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(ggplot2)
load('output/10x-180504-aligned-metageneplot')
all10x.aligned <- readRDS('output/10x-180504-aligned')
all10x.aligned.ccregout <- readRDS('output/10x-180504-ccregout-aligned')
all10x.aligned.discardedcells <- readRDS('output/10x-180504-cca-discardedcells')
all10x.ccregout.aligned.discardedcells <- readRDS('output/10x-180504-ccregout-cca-discardedcells')

Alignment of the data with and without cell cycle effects regressed out. Both were aligned on 30 subspaces, tSNE was performed on the first 15 CCs.

Shared correlation per CC

Normal alignment

p1
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

Expand here to see past versions of unnamed-chunk-3-1.png:
Version Author Date
eaa7e4a PytrikFolkertsma 2018-11-02

Alignment with cell cycle effects regressed out

p2
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

Expand here to see past versions of unnamed-chunk-4-1.png:
Version Author Date
eaa7e4a PytrikFolkertsma 2018-11-02

Alignment without cell cycle effects regressed out

tSNE of the aligned data.

TSNEPlot(all10x.aligned, group.by='sample_name', pt.size=0.1)

Expand here to see past versions of unnamed-chunk-5-1.png:
Version Author Date
eaa7e4a PytrikFolkertsma 2018-11-02

tSNE of the aligned data coloured on cell cycle phase.

TSNEPlot(all10x.aligned, group.by='Phase', pt.size=0.1)

Expand here to see past versions of unnamed-chunk-6-1.png:
Version Author Date
eaa7e4a PytrikFolkertsma 2018-11-02

Alignment with cell cycle regressed out

tSNE of the aligned data with cell cycle effects regressed out.

TSNEPlot(all10x.aligned.ccregout, group.by='sample_name', pt.size=0.1)

Expand here to see past versions of unnamed-chunk-7-1.png:
Version Author Date
eaa7e4a PytrikFolkertsma 2018-11-02

tSNE of the aligned data with cell cycle effects regressed out, colored by phase.

TSNEPlot(all10x.aligned.ccregout, group.by='Phase', pt.size=0.1)

Expand here to see past versions of unnamed-chunk-8-1.png:
Version Author Date
eaa7e4a PytrikFolkertsma 2018-11-02

tSNE of the aligned data with cell cycle effects regressed out, colored by subtissue

TSNEPlot(all10x.aligned.ccregout, group.by='depot', pt.size=0.1)

Expand here to see past versions of unnamed-chunk-9-1.png:
Version Author Date
eaa7e4a PytrikFolkertsma 2018-11-02

Discarded cells from alignment

Before aligning the samples, sample-specific cells were discarded. Below: discarded cells from normal alignment.

discarded <- as.data.frame(table(all10x.aligned.discardedcells@meta.data$sample_name))

names(discarded) <- c('Sample', 'Frequency')

p_discarded <-ggplot(data=discarded, aes(x=Sample, y=Frequency)) +
  geom_bar(stat="identity") +
  theme(axis.text.x = element_text(angle=45, hjust=1)) 

p_discarded

Expand here to see past versions of unnamed-chunk-10-1.png:
Version Author Date
eaa7e4a PytrikFolkertsma 2018-11-02

Discarded cells from alignment with cell cycle effects regressed out.

discarded.ccregout <- as.data.frame(table(all10x.ccregout.aligned.discardedcells@meta.data$sample_name))

names(discarded.ccregout) <- c('Sample', 'Frequency')

p_discarded.ccregout <-ggplot(data=discarded.ccregout, aes(x=Sample, y=Frequency)) +
  geom_bar(stat="identity") +
  theme(axis.text.x = element_text(angle=45, hjust=1)) 

p_discarded.ccregout

Expand here to see past versions of unnamed-chunk-11-1.png:
Version Author Date
eaa7e4a PytrikFolkertsma 2018-11-02

Figures for report

fig1

Expand here to see past versions of fig1-1.png:
Version Author Date
eaa7e4a PytrikFolkertsma 2018-11-02

Biweight midcorrelation plots.

sfig1 <- plot_grid(
  p1,
  p2,
  labels=c('a', 'b'),
  nrow=1
)
`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using method = 'loess' and formula 'y ~ x'
save_plot("plots/supplementary_figures/sfig_180504_biweightplots.pdf", sfig1, base_width=12, base_height=4)
sfig1

Expand here to see past versions of fig2-1.png:
Version Author Date
eaa7e4a PytrikFolkertsma 2018-11-02

Discarded cells.

sfig2 <- plot_grid(
  p_discarded,
  p_discarded.ccregout,
  labels=c('a', 'b'),
  nrow=1
)
save_plot("plots/supplementary_figures/sfig_180504_alignment-discardedcells.pdf", sfig2, base_width=12, base_height=4)
sfig2

Expand here to see past versions of fi3-1.png:
Version Author Date
eaa7e4a PytrikFolkertsma 2018-11-02

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] Seurat_2.3.4  Matrix_1.2-14 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] bindrcpp_0.2.2      igraph_1.2.2        gtable_0.2.0       
 [40] glue_1.3.0          RANN_2.6            reshape2_1.4.3     
 [43] dplyr_0.7.6         Rcpp_0.12.18        trimcluster_0.1-2.1
 [46] gdata_2.18.0        ape_5.1             nlme_3.1-137       
 [49] iterators_1.0.10    fpc_2.1-11.1        gbRd_0.4-11        
 [52] lmtest_0.9-36       stringr_1.3.1       irlba_2.3.2        
 [55] gtools_3.8.1        DEoptimR_1.0-8      MASS_7.3-50        
 [58] zoo_1.8-3           scales_1.0.0        doSNOW_1.0.16      
 [61] parallel_3.4.3      RColorBrewer_1.1-2  yaml_2.2.0         
 [64] reticulate_1.10     pbapply_1.3-4       gridExtra_2.3      
 [67] rpart_4.1-13        segmented_0.5-3.0   latticeExtra_0.6-28
 [70] stringi_1.2.4       foreach_1.4.4       checkmate_1.8.5    
 [73] caTools_1.17.1.1    bibtex_0.4.2        Rdpack_0.9-0       
 [76] SDMTools_1.1-221    rlang_0.2.2         pkgconfig_2.0.2    
 [79] dtw_1.20-1          prabclus_2.2-6      bitops_1.0-6       
 [82] evaluate_0.11       lattice_0.20-35     ROCR_1.0-7         
 [85] purrr_0.2.5         bindr_0.1.1         labeling_0.3       
 [88] htmlwidgets_1.2     bit_1.1-14          tidyselect_0.2.4   
 [91] plyr_1.8.4          magrittr_1.5        R6_2.2.2           
 [94] snow_0.4-2          gplots_3.0.1        Hmisc_4.1-1        
 [97] pillar_1.3.0        whisker_0.3-2       foreign_0.8-70     
[100] withr_2.1.2         fitdistrplus_1.0-9  mixtools_1.1.0     
[103] survival_2.42-6     nnet_7.3-12         tsne_0.1-3         
[106] tibble_1.4.2        crayon_1.3.4        hdf5r_1.0.0        
[109] KernSmooth_2.23-15  rmarkdown_1.10      grid_3.4.3         
[112] data.table_1.11.4   git2r_0.23.0        metap_1.0          
[115] digest_0.6.15       diptest_0.75-7      tidyr_0.8.1        
[118] R.utils_2.7.0       stats4_3.4.3        munsell_0.5.0

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