README.md

In raguirreg/DeconCell: DeconCell

NOTE: Development has moved to https://github.com/molgenis/systemsgenetics/tree/master/Decon2

title: "DeconCell" author: "Raúl Aguirre-Gamboa and Niek de Klein" date: "r Sys.Date()" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Vignette Title} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8}

Introduction

DeconCell is an r package containing models for predicting the proportions of circulating immune cell subpopulations using bulk gene expression data from whole blood. Models were built using an elastic net and training in 95 healthy dutch volunteers from the 500FG cohort with FACS quantification of 73 circulating cell subpopulations as described in our previous publication. For additional details on methods and results please go our manuscript.

Install the package from github

library(devtools)
install_github("raguirreg/DeconCell")

Pre-processing example data

Let's load and pre-process our example data. These are 5 samples with > ~40k genes quantified. These are gene read counts, we need to approximate the example data to a normal-like distribution and account for library sizes. In order to do this, we use the dCell.expProcessing function. This function will perform a TMM normalization (as described in the edgeRpackage) a log2(counts+1) and scale (z-transformation) per gene.

library(DeconCell)
library(edgeR)

data("count.table")
dCell.exp <- dCell.expProcessing(count.table, trim = TRUE)

Prediction of cell propotions

data("dCell.models")
prediction <- dCell.predict(dCell.exp, dCell.models, res.type = "median")
head(prediction$dCell.prediction)
head(prediction$Evaluation)

Correlation coeficient between of predicted and measured values

data("cell.proportions")
library(reshape2)
library(ggplot2)
data("dCell.names")
pData <- data.frame(PearsonCor= diag(cor(cell.proportions, prediction$dCell.prediction)), 
                    CTs = dCell.names[colnames(cell.proportions), "finalName"], 
                    Subpop = dCell.names[colnames(cell.proportions), "broadSubpopulations"])
ggplot(pData, aes(y=PearsonCor , x= CTs, fill=Subpop))+
  geom_bar(stat="identity", alpha=0.8)+
  geom_hline(yintercept = 0.5, alpha=0.5, color="red")+
  coord_flip()+
  scale_fill_brewer(palette = "Dark2")+
  theme_bw()

raguirreg/DeconCell documentation built on May 29, 2019, 8:05 a.m.