In mkarikom/DURIAN: Deconvolution and Multitask Regression-Based Imputation

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title: "Module Selection" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Module Selection} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8}

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DURIAN supports the integration of custom deconvolution modules, even modules written in other languages, such as Julia. Module selection is made using the deconv_method argument to DURIAN::run_durian and the default is deconv_method = "MuSiC".

Current deconvolution alternatives include:

• fast, NNLS regression using MuSiC (R)
• distributed Monte Carlo inference using dsLDA (Julia)

Load the package and data

library(DURIAN)

We will run DURIAN with two different deconvolution modules: LDA-based topic model and NNLS-based regression.

We select a random subset of 1000 genes present in both bulk and single-cell data, as well as a subset of 5 patients from the original 20, to speed up the example.

Preprocess the data

set.seed(42)
B.sub = B[,1:5]
C.sub = as.data.frame(C %>% sample_n(1000))
comgenes = intersect(rownames(C.sub),rownames(B.sub))
C.sub = DURIAN::subsetsc(scremoutlier(C.sub),geneids=comgenes,return_obj=TRUE,nsd=3)
B.sub = B.sub[comgenes,]
pDataC.sub = pDataC[colnames(C.sub),]

Run imputation using dsLDA deconvolution (julia).

Run imputation on the single-cell data, using the bulk data for supervision.

library(JuliaCall)
julia_library("DistributedStwdLDA")
julia_library("Distributed")
julia_call("procsN",as.integer(ncol(B.sub)+1))
julia_command("@everywhere using DistributedStwdLDA")

impresult_list_lda=run_durian(
      scrabble_parameters = c(1,1e-6,1e-4),
      nEM = 5,
      scdata = C.sub,
      metadata = pDataC.sub,
      bulkdata = B.sub,
      deconv_method = "dsLDA",
      MCNITER = as.integer(2000),
      MINCELLSTOPICCORP=as.integer(1),
      MCNPARTITIONS=ncol(B.sub),
      MCNCHAINS=as.integer(1),
      MCTHINNING=as.integer(1),
      MCBURNRATE=0.5,
      LDASCALEBLK="lognorm",
      LDASCALESC="column",
      LDASCALEFACBLK=10000,
      nIter_outer = 10,
      nIter_inner = 10,
      nSDCIters = 500000,
      DECONVGENETHRESH=-0.01,
      SCRGENETHRESH=-0.01,
      outerStats = FALSE,
      durianEps=1e-3,
      saveImputationLog = FALSE,
      saveDeconvolutionLog = FALSE,
      saveImputedStep=FALSE)
  impresult_lda = impresult_list_lda[["C"]]

Run imputation using MuSiC deconvolution (R). Note: by default, the mtSCRABBLE backend for DURIAN imputation (regardless of deconvolution module) calls irlba::irlba() for speed when computing matrix norms for the DURIAN objective. Currently, irlba does not gracefully handle the case where a randomly chosen starting singular vector is close to the null space. When the corresponding error is encountered

Run imputation on the single-cell data, using the bulk data for supervision.

impresult_list=run_durian(
      scrabble_parameters = c(1,1e-6,1e-4),
      nEM = 1,
      scdata = C.sub,
      metadata = pDataC.sub,
      bulkdata = B.sub,
      deconv_method = "MuSiC",
      nIter_outer = 10,
      nIter_inner = 10,
      nSDCIters = 500000,
      DECONVGENETHRESH=-0.01,
      SCRGENETHRESH=-0.01,
      outerStats = FALSE,
      durianEps=1e-3,
      saveImputationLog = FALSE,
      saveDeconvolutionLog = FALSE,
      saveImputedStep=FALSE)
  impresult = impresult_list[["C"]]

Construct a low-dimensional UMAP embedding from imputed and original data

library(umap)
library(ggplot2)
library(reshape2)
common_cells = base::intersect(base::intersect(colnames(impresult_lda),colnames(impresult)),colnames(C))
umap_imputed_lda = umap(t(impresult_lda[,common_cells]),preserve.seed=TRUE)
umap_imputed = umap(t(impresult[,common_cells]),preserve.seed=TRUE)
umap_orig = umap(t(as.matrix(C[,common_cells])),preserve.seed=TRUE)
imputed_df_lda = cbind(umap_imputed_lda$layout,pDataC.sub[common_cells,])
imputed_df_lda$status = "Imputed [LDA deconv.]"
imputed_df = cbind(umap_imputed$layout,pDataC.sub[common_cells,])
imputed_df$status = "Imputed [W-NNLS deconv.]"
orig_df = cbind(umap_orig$layout,pDataC.sub[common_cells,])
orig_df$status = "Unimputed"
df = rbind(imputed_df,imputed_df_lda,orig_df)
colnames(df) = c("UMAP1","UMAP2","cellID","cellType","sampleID","status")
df$status = factor(df$status,levels=c("Imputed [W-NNLS deconv.]","Imputed [LDA deconv.]", "Unimputed"))

Plot the original and imputed data

ggplot(df,aes(x=UMAP1, y=UMAP2,color=cellType)) + 
      geom_point(size=1)+
      facet_grid(~status,scales="free") + theme_bw()

mkarikom/DURIAN documentation built on May 21, 2022, 1:18 p.m.