Last updated: 2018-09-14

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Read Data : Yan

load('data/Yan.rda')
X         = as.matrix(yan)
genenames = rownames(X)
truelabel = as.numeric(as.factor(ann$cell_type1))
numClust = length(unique(truelabel))

Quality Control and Cell Filtering

summary = explore_data(X, genenames)

Gene Filtering

disp = plot_dispersion(X = X, 
                       genenames = genenames, 
                       bins=NA, 
                       median = TRUE)

X = gene_filter(X, genenames, disp, 
                mean.thresh=c(-Inf, 4000), 
                dispersion.thresh = c(0.5, Inf))

genenames = X$genenames
X = X$X

Normalization and Bias Correction

X = quantile_normalize(X)
cd = correct_detection_rate(as.matrix(X), det.rate = colMeans(X>0)/nrow(X))

Run the algorithm

Run SLSL on the log.cpm matrix.

out = SLSL(X, numClust = numClust)

adj.rand.index(out$result, truelabel)
[1] 0.8954618

Analyze : Differentially Expressed Genes

degenes = de_genes(X, genenames, out$result, top.n=100, plot=6)

head(degenes)
  de_genes   log10p
1    HDAC9 16.36138
2   STK32B 16.28383
3 FLJ40852 16.19759
4    KPNA7 16.10733
5   MFSD2A 16.10030
6     WEE2 16.02111

Markers for Each Cluster

bio.markers = find_markers(X, genenames, out$result, out$tsne$Y, top.n = 50, plot.n=3)
bio.markers$plots[[1]]

head(bio.markers$markers[[1]])
  clust1_genenames clust1_log10p
1             PDK4      17.15950
2          NEUROD1      15.94980
3         C11orf96      13.93428
4         FLJ37201      12.38675
5           HOXD11      12.18412
6           PCDH10      11.56774
bio.markers$plots[[2]]

head(bio.markers$markers[[2]])
  clust2_genenames clust2_log10p
1         C19orf33      19.08016
2        C10orf125      18.53046
3            KRT19      18.03959
4            CRIP1      18.02379
5         C9orf140      17.76193
6             BIN1      17.53935
bio.markers$plots[[3]]

head(bio.markers$markers[[3]])
  clust3_genenames clust3_log10p
1           DUX4L2      12.67983
2             DUX4      11.99765
3           DUX4L3      11.13193
4          USP17L2      10.71518
5           DUX4L5      10.49148
6     LOC100127888      10.40254

Session information

sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.5

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] parallel  stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] bindrcpp_0.2.2          gridExtra_2.3          
 [3] gdata_2.18.0            stargazer_5.2.2        
 [5] abind_1.4-5             broom_0.5.0            
 [7] gplots_3.0.1            diceR_0.5.1            
 [9] Rtsne_0.13              igraph_1.2.2           
[11] scatterplot3d_0.3-41    pracma_2.1.4           
[13] fossil_0.3.7            shapefiles_0.7         
[15] foreign_0.8-71          maps_3.3.0             
[17] sp_1.3-1                caret_6.0-80           
[19] lattice_0.20-35         reshape_0.8.7          
[21] dplyr_0.7.6             ggplot2_3.0.0          
[23] irlba_2.3.2             Matrix_1.2-14          
[25] quadprog_1.5-5          inline_0.3.15          
[27] matrixStats_0.54.0      SCNoisyClustering_0.1.0

loaded via a namespace (and not attached):
 [1] nlme_3.1-137              bitops_1.0-6             
 [3] lubridate_1.7.4           dimRed_0.1.0             
 [5] rprojroot_1.3-2           tools_3.5.1              
 [7] backports_1.1.2           R6_2.2.2                 
 [9] KernSmooth_2.23-15        rpart_4.1-13             
[11] lazyeval_0.2.1            colorspace_1.3-2         
[13] nnet_7.3-12               withr_2.1.2              
[15] tidyselect_0.2.4          compiler_3.5.1           
[17] git2r_0.23.0              labeling_0.3             
[19] caTools_1.17.1.1          scales_0.5.0             
[21] sfsmisc_1.1-2             DEoptimR_1.0-8           
[23] robustbase_0.93-2         stringr_1.3.1            
[25] digest_0.6.15             rmarkdown_1.10           
[27] R.utils_2.6.0             pkgconfig_2.0.1          
[29] htmltools_0.3.6           rlang_0.2.1              
[31] ddalpha_1.3.4             bindr_0.1.1              
[33] gtools_3.8.1              mclust_5.4.1             
[35] ModelMetrics_1.1.0        R.oo_1.22.0              
[37] magrittr_1.5              Rcpp_0.12.18             
[39] munsell_0.5.0             R.methodsS3_1.7.1        
[41] stringi_1.2.4             whisker_0.3-2            
[43] yaml_2.2.0                MASS_7.3-50              
[45] plyr_1.8.4                recipes_0.1.3            
[47] grid_3.5.1                pls_2.6-0                
[49] crayon_1.3.4              splines_3.5.1            
[51] knitr_1.20                pillar_1.3.0             
[53] reshape2_1.4.3            codetools_0.2-15         
[55] stats4_3.5.1              CVST_0.2-2               
[57] magic_1.5-8               glue_1.3.0               
[59] evaluate_0.11             RcppArmadillo_0.8.600.0.0
[61] data.table_1.11.4         foreach_1.4.4            
[63] gtable_0.2.0              purrr_0.2.5              
[65] tidyr_0.8.1               kernlab_0.9-26           
[67] assertthat_0.2.0          DRR_0.0.3                
[69] gower_0.1.2               prodlim_2018.04.18       
[71] class_7.3-14              survival_2.42-6          
[73] geometry_0.3-6            timeDate_3043.102        
[75] RcppRoll_0.3.0            tibble_1.4.2             
[77] iterators_1.0.10          workflowr_1.1.1          
[79] lava_1.6.2                ipred_0.9-6

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