Last updated: 2018-08-29

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Read Data : Yan

load('data/Yan.rda')
X         = as.matrix(yan)
genenames = rownames(X)
truelabel = as.character(ann$cell_type1)
numClust  = 6
rm(ann, yan)

Quality Control and Cell Filter

nGene = colSums(X > 0)
hist(nGene)

summaryX = cellFilter(X = X, 
                      genenames = rownames(X),
                      minGene = -Inf, 
                      maxGene = Inf, 
                      maxMitoProp = 0.1)
tmpX = summaryX$X
nUMI = summaryX$nUMI
nGene = summaryX$nGene
percent.mito = summaryX$percent.mito
det.rate = summaryX$det.rate

par(mfrow = c(1,4))
boxplot(nUMI, main='nUMI'); 
boxplot(nGene, main='nGene'); 
boxplot(percent.mito, main='mitochondrial gene', ylim=c(0,0.5)); 
boxplot(det.rate, main='detection rate', ylim=c(0,0.1))

Gene filtering

X = X[rowSums(X) > 0, ]
genenames = genenames[rowSums(X) > 0]

#gene filter by dispersion
disp = dispersion(X, bins = 20)
plot(disp$z ~ disp$genemeans,
     xlab = "mean expression",
     ylab = "normalized dispersion")

select = which(abs(disp$z) > 1)
X = X[select, ]
genenames = genenames[select]

UMI Normalization

Use quantile-normalization to make the distribution of each cell the same.

nX = quantile_normalize(as.matrix(X))

Correct Detection Rate

After normalization, the linear relationship between the first PC and the detection rate usually disappears. The plots show that even without correction, the two are not heavily correlated. So we do not regress out the detection rate.

#take log
logX = as.matrix(log(nX + 1))

#check dependency
out = correct_detection_rate(logX, det.rate)

#regress out
log.cpm = out$residual
# log.cpm = logX

Dimension reduction and visualization.

pc = irlba(log.cpm, 20)
plot(pc$d, ylab = "singular values")

tsne = Rtsne(pc$v[,1:15], dims=2, perplexity = 10, pca=FALSE)
df = data.frame(tsne1 = tsne$Y[,1], tsne2 = tsne$Y[,2], truelabel = truelabel)
ggplot(df, aes(x=tsne1, y=tsne2, col = truelabel)) + geom_point()+
  ggtitle("True Label")

rm(pc)

Run the algorithm

Run SLSL on the log.cpm matrix.

out = SLSL(log.cpm, log=FALSE,
                filter = FALSE,
                correct_detection_rate = FALSE,
                klist = c(5,10,15),
                sigmalist = c(1,1.5,2),
                kernel_type = "combined",
                verbose=FALSE)
df$SLSL = as.factor(out$result)
ggplot(df, aes(x=tsne1, y=tsne2, col=SLSL))+geom_point()

adj.rand.index(out$result, as.numeric(as.factor(truelabel)))
[1] 0.8681222

Analyze

S = as.matrix(out$S)
palette.gr.marray <- colorRampPalette(c("ivory", "pink", "red", "brown"))(30)
labRow = rep("", 90)
labRow[c(3, 10,18, 35, 52, 76) ] = c("zygote",
                                    "2cell", "4cell", "8cell", "16cell",
                                    "blast")
heatmap.2(S,
          trace = "none",
          col = palette.gr.marray,
          Colv = F,
          Rowv = F, 
          rowsep = which(truelabel[1:89] != truelabel[2:90]),
          colsep = which(truelabel[1:89] != truelabel[2:90]),
          sepcolor = "gray",
          dendrogram = "none",
          labRow = labRow,
          labCol = labRow,
          key = F,
          breaks = seq(min(S), max(S),length=31),
          cexRow = 1,
          symbreaks = T)

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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