Last updated: 2018-08-29

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Read data.

library(data.table)
tmp = setDF(fread('data/unnecessary_in_building/GSM1626794_P14Retina_2.digital_expression.txt'))
orig = tmp[,-1]; rownames(orig) = tmp[,1]; rm(tmp)
genenames = sapply(strsplit(rownames(orig), ":"), function(x) x[3])
gc(verbose=FALSE);
            used  (Mb) gc trigger   (Mb) limit (Mb)  max used  (Mb)
Ncells   2792253 149.2    4257502  227.4         NA   4257502 227.4
Vcells 106691790 814.0  155381158 1185.5      16384 107648931 821.3

Quality Control and Cell Filter

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

Expand here to see past versions of cell_filter-1.png:
Version Author Date
f6c8902 tk382 2018-08-29 

summaryX = cellFilter(X = orig, 
                      genenames = genenames,
                      minGene = 500, 
                      maxGene = 2000, 
                      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))

Expand here to see past versions of cell_filter-2.png:
Version Author Date
f6c8902 tk382 2018-08-29

Gene filtering

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

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

Expand here to see past versions of basic_gene_filtering-1.png:
Version Author Date
f6c8902 tk382 2018-08-29 

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)

Expand here to see past versions of log_transform_and_det_correction-1.png:
Version Author Date
f6c8902 tk382 2018-08-29 

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

Dimension reduction on the data for visualization.

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

Expand here to see past versions of tsne-1.png:
Version Author Date
f6c8902 tk382 2018-08-29 

tsne = Rtsne(pc$v[,1:5], dims=2, perplexity = 100, pca=FALSE)
plot(tsne$Y, xlab = 'tsne1', ylab = 'tsne2', cex = 0.5)

Expand here to see past versions of tsne-2.png:
Version Author Date
f6c8902 tk382 2018-08-29 

rm(pc)

Run SLSL on the log.cpm matrix.

# out = SLSL(log.cpm, log=FALSE,
#                 filter = FALSE,
#                 correct_detection_rate = FALSE,
#                 klist = c(300,350,400),
#                 sigmalist = c(1,1.5,2),
#                 kernel_type = "pearson",
#                 verbose=FALSE)
# save(out, file = 'dropseq_slsl1.Rdata')
load('analysis/dropseq_slsl1.Rdata')
tab = table(out$result)
plot(tsne$Y, col=rainbow(length(tab))[out$result],
     xlab = 'tsne1', ylab='tsne2', main="SLSL", cex = 0.5)

Using SC3 with our gene and cell filter procedure and disabling the biology and filter feature of the function leads to the following result. Using SC3’s default methods lead to the error “distribution of gene expression in cells is too skewed towards 0”.

library(SingleCellExperiment)
library(SC3)
# colnames(X) = paste0("C", 1:ncol(X))
# sce = SingleCellExperiment(
#   assays = list(
#     counts = X,
#     logcounts = as.matrix(log(X+1))
#   ),
#   colData = colnames(X)
# )
# rowData(sce)$feature_symbol = genenames
# sce = sc3_prepare(sce, kmeans_nstart = 50)
# sce = sc3_estimate_k(sce)
# k = metadata(sce)$sc3$k_estimation
# N = ncol(X)
# sce = sc3(sce, ks=k, biology = FALSE, gene_filter=FALSE,
#          kmeans_nstart=10)
# col_data_selected = colData(sce)
# save(col_data_selected, file  = "analysis/dropseq_sc3_result.Rdata")
load('analysis/dropseq_sc3_result.Rdata')
plot(tsne$Y, col=rainbow(k)[col_data_selected$sc3_6_clusters],
     xlab = 'tsne1', ylab='tsne2', main="SC3", cex = 0.5)
ind = which(genenames %in% c('Chat', 'Gad1', 'Gad2', 'Slc17a8', 'Slc6a9', 'Gjd2'))
df = data.frame(tsne1 = tsne$Y[,1], tsne2 = tsne$Y[,2],
                Slc17a8 = log.cpm[ind[1], ],
                Gad2 = log.cpm[ind[2], ],
                Gad1 = log.cpm[ind[3],]
                )
g1 = ggplot(df, aes(x=tsne1, y=tsne2, col = Slc17a8)) + geom_point(size=0.1) +
  scale_colour_gradient(low="pink", high="black") + guides(color = FALSE) + ggtitle('Slc17a8')
g2 = ggplot(df, aes(x=tsne1, y=tsne2, col = Gad2)) + geom_point(size=0.1) +
  scale_colour_gradient(low="pink", high="black") + guides(color = FALSE) + ggtitle('Gad2')
g3 = ggplot(df, aes(x=tsne1, y=tsne2, col = Gad1)) + geom_point(size=0.1) +
  scale_colour_gradient(low="pink", high="black") + guides(color = FALSE) + ggtitle('Gad1')
grid.arrange(g1,g2,g3, nrow=1)

Expand here to see past versions of markers-1.png:
Version Author Date
f6c8902 tk382 2018-08-29

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          data.table_1.11.4      
 [3] gridExtra_2.3           gdata_2.18.0           
 [5] stargazer_5.2.2         abind_1.4-5            
 [7] broom_0.5.0             gplots_3.0.1           
 [9] diceR_0.5.1             Rtsne_0.13             
[11] igraph_1.2.2            scatterplot3d_0.3-41   
[13] pracma_2.1.4            fossil_0.3.7           
[15] shapefiles_0.7          foreign_0.8-71         
[17] maps_3.3.0              sp_1.3-1               
[19] caret_6.0-80            lattice_0.20-35        
[21] reshape_0.8.7           dplyr_0.7.6            
[23] ggplot2_3.0.0           irlba_2.3.2            
[25] Matrix_1.2-14           quadprog_1.5-5         
[27] inline_0.3.15           matrixStats_0.54.0     
[29] 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] foreach_1.4.4             gtable_0.2.0             
[63] purrr_0.2.5               tidyr_0.8.1              
[65] kernlab_0.9-26            assertthat_0.2.0         
[67] DRR_0.0.3                 gower_0.1.2              
[69] prodlim_2018.04.18        class_7.3-14             
[71] survival_2.42-6           geometry_0.3-6           
[73] timeDate_3043.102         RcppRoll_0.3.0           
[75] tibble_1.4.2              iterators_1.0.10         
[77] workflowr_1.1.1           lava_1.6.2               
[79] ipred_0.9-6

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