Last updated: 2018-07-26

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library(SCNoisyClustering)

library(inline)
library(matrixStats)
library(quadprog)
library(irlba)
library(ggplot2)
library(dplyr)
library(reshape)
library(caret)
library(fossil)
library(pracma)
library(igraph)
library(Rtsne)
library(gplots)
library(broom)
library(abind)
library(stargazer)
library(scatterplot3d)
library(diceR)
library(parallel)
library(data.table)

heat = function(S){
  ggplot(melt(S), aes(x=X1, y=X2, fill=value)) + geom_tile() +
    scale_fill_gradient2() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
    xlab("") + ylab("")
}
X = readMM('../data/unnecessary_in_building/pbmc3k/matrix.mtx')
X = as.matrix(X)
genes = read.table('../data/unnecessary_in_building/pbmc3k/genes.tsv')
rownames(X) = genes$V2

ref_file = readRDS('../data/unnecessary_in_building/pbmc3k/all_pure_select_11types.rds')
ref = t(ref_file$pure_avg)
rownames(ref) = genes$V2[ref_file$pure_use_genes]
colnames(ref) = ref_file$pure_id
#ref = scale(ref, scale=FALSE)

10X data from PBMC Cells

out_pbmc_ref = SLSL_ref(X = X, 
                        ref = ref,
                        kernel_type = "euclidean",
                        correct_detection_rate = TRUE,
                        verbose = TRUE)
[1] "data cleaning.."
[1] "computing projection.."
[1] "ref with smaller version.."
[1] "constructing kernel.."
[1] "optimizing.."
[1] "network diffusion.."
[1] "dimension reduction.."
myheatmap(out_pbmc_ref$projection, 0, 3*1e-04, "")

plot(out_pbmc_ref$result$tsne[,1:2], col = out_pbmc_ref$result$result)

out_pbmc_raw = LSLSL(X=X, 
                     klist = c(10,15,20), 
                     sigmalist = c(1,2), 
                     correct_detection_rate = TRUE, 
                     verbose = TRUE)
[1] "building pearson correlation matrix.."
[1] "      now saving following kernels"
[1] "           pearson1SLSL_kernels_tmp.txt"
[1] "           pearson2SLSL_kernels_tmp.txt"
[1] "           pearson3SLSL_kernels_tmp.txt"
[1] "           pearson4SLSL_kernels_tmp.txt"
[1] "           pearson5SLSL_kernels_tmp.txt"
[1] "           pearson6SLSL_kernels_tmp.txt"
[1] "building euclidean distance matrix.."
[1] "      now saving following kernels"
[1] "           euclidean1SLSL_kernels_tmp.txt"
[1] "           euclidean2SLSL_kernels_tmp.txt"
[1] "           euclidean3SLSL_kernels_tmp.txt"
[1] "           euclidean4SLSL_kernels_tmp.txt"
[1] "           euclidean5SLSL_kernels_tmp.txt"
[1] "           euclidean6SLSL_kernels_tmp.txt"
[1] "building euclidean distance matrix.."
[1] "      now saving following kernels"
[1] "           spearman1SLSL_kernels_tmp.txt"
[1] "           spearman2SLSL_kernels_tmp.txt"
[1] "           spearman3SLSL_kernels_tmp.txt"
[1] "           spearman4SLSL_kernels_tmp.txt"
[1] "           spearman5SLSL_kernels_tmp.txt"
[1] "           spearman6SLSL_kernels_tmp.txt"
[1] "working on the following subsets:"
[1] "consensus clustering: CSPA.."
tsne = Rtsne(t(X))

par(mfrow = c(1,2))

plot(tsne$Y, 
     col = out_pbmc_ref$result$result,
     xlab = 'dim1',
     ylab = 'dim2', main = "Using Reference Panel")
plot(tsne$Y, 
     col = out_pbmc_raw$result,
     xlab = 'dim1', ylab = 'dim2', main = "Using Raw Data")

Smart-seq data from Human Embryonic Stem Cells

load('../data/unnecessary_in_building/7_Chu_celltype.RData')
sysdata = SCNoisyClustering::reference_panel
ref = reference_panel$cell

X = Chu_celltype$X
truth = as.numeric(as.factor(Chu_celltype$label))
out_cell_ref = SLSL_ref(X                      = as.matrix(X), 
                        ref                    = as.matrix(ref),
                        correct_detection_rate = TRUE,
                        numClust               = 7,
                        verbose                = TRUE)
[1] "data cleaning.."
[1] "computing projection.."
[1] "ref with smaller version.."
[1] "constructing kernel.."
[1] "optimizing.."
[1] "network diffusion.."
[1] "dimension reduction.."
out_cell_raw = SLSL(X                      = as.matrix(X),
                    correct_detection_rate = TRUE,
                    numClust               = 7,
                    verbose                = TRUE)
[1] "constructing kernel.."
[1] "optimizing.."
[1] "network diffusion.."
[1] "dimension reduction.."
myheatmap(out_cell_ref$projection, -0.05, 0.35, "")

tsne = Rtsne(t(log(X+1)))

par(mfrow = c(1,2))
plot(tsne$Y, 
     col = rainbow(7)[out_cell_ref$result$result],
     xlab = 'dim1',
     ylab = 'dim2', 
     main = "Using Reference Panel")
plot(tsne$Y, 
     col = out_cell_raw$result,
     xlab = 'dim1', ylab = 'dim2', 
     main = "Using Raw Data")

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] data.table_1.10.4-3     diceR_0.5.1            
 [3] scatterplot3d_0.3-41    stargazer_5.2.2        
 [5] abind_1.4-5             broom_0.4.5            
 [7] gplots_3.0.1            Rtsne_0.13             
 [9] igraph_1.2.1            pracma_2.1.4           
[11] fossil_0.3.7            shapefiles_0.7         
[13] foreign_0.8-70          maps_3.3.0             
[15] sp_1.2-7                caret_6.0-80           
[17] lattice_0.20-35         reshape_0.8.7          
[19] dplyr_0.7.6             ggplot2_3.0.0          
[21] irlba_2.3.2             Matrix_1.2-14          
[23] quadprog_1.5-5          matrixStats_0.53.1     
[25] inline_0.3.15           SCNoisyClustering_0.1.0

loaded via a namespace (and not attached):
 [1] nlme_3.1-137       bitops_1.0-6       lubridate_1.7.4   
 [4] dimRed_0.1.0       rprojroot_1.3-2    tools_3.5.1       
 [7] backports_1.1.2    R6_2.2.2           KernSmooth_2.23-15
[10] rpart_4.1-13       lazyeval_0.2.1     colorspace_1.3-2  
[13] nnet_7.3-12        withr_2.1.2        tidyselect_0.2.4  
[16] mnormt_1.5-5       compiler_3.5.1     git2r_0.21.0      
[19] caTools_1.17.1     scales_0.5.0       sfsmisc_1.1-2     
[22] DEoptimR_1.0-8     psych_1.8.3.3      robustbase_0.93-0 
[25] stringr_1.3.0      digest_0.6.15      rmarkdown_1.9     
[28] R.utils_2.6.0      pkgconfig_2.0.1    htmltools_0.3.6   
[31] rlang_0.2.0        ddalpha_1.3.2      bindr_0.1.1       
[34] mclust_5.4         gtools_3.5.0       ModelMetrics_1.1.0
[37] R.oo_1.22.0        magrittr_1.5       Rcpp_0.12.16      
[40] munsell_0.4.3      R.methodsS3_1.7.1  stringi_1.1.7     
[43] whisker_0.3-2      yaml_2.1.19        MASS_7.3-50       
[46] plyr_1.8.4         recipes_0.1.2      grid_3.5.1        
[49] gdata_2.18.0       splines_3.5.1      knitr_1.20        
[52] pillar_1.2.2       reshape2_1.4.3     codetools_0.2-15  
[55] stats4_3.5.1       CVST_0.2-1         magic_1.5-8       
[58] glue_1.2.0         evaluate_0.10.1    foreach_1.4.4     
[61] gtable_0.2.0       purrr_0.2.4        tidyr_0.8.0       
[64] kernlab_0.9-25     assertthat_0.2.0   DRR_0.0.3         
[67] gower_0.1.2        prodlim_2018.04.18 class_7.3-14      
[70] survival_2.42-3    geometry_0.3-6     timeDate_3043.102 
[73] RcppRoll_0.2.2     tibble_1.4.2       iterators_1.0.9   
[76] workflowr_1.1.1    bindrcpp_0.2.2     lava_1.6.1        
[79] ipred_0.9-6

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