In DKundnani/FeatureCorr: Finding associations through prime feature and pairwise correlations

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FeatureCorr

An R package to study feature correlations aided with data transformation for Next Generation sequencing and microarray data

Installation

The development version from GitHub with:

# install.packages("devtools")
devtools::install_github("DKundnani/FeatureCorr")

Example

Following is an example of function usage by utilizing sample data provided in the package. It contains Gene-Tissue Expression dataset with ~8000 genes(features) expression in 53 tissues, sampled from a larger dataset containing >42K genes.

Another dataset is from The Cancer Genome Atlas Pan Cancer dataset randomly selected 2000 samples from >10K samples, with information for selected 40 features(genes)

library(FeatureCorr) #Loading the library

Data Transformation using FeatureCorr::data_transform function

#Data Transformation
transdf<- data_transform(df=GTEX[-1],transformation='log2', featurelist=GTEX$Description, medianthres=1)

Data Transformation using FeatureCorr::data_transform function Prime Feature Correlation using FeatureCorr::primefeature_corr function

# Prime Feature(RNASEH2A gene) Correlation in Gene-Tissue EXpression Dataset
inputdf<-transdf[[1]] #First item in the list returned is the transformed Data
primefcorr <- primefeature_corr(df=inputdf,featurelist=rownames(inputdf) ,primefeature="RNASEH2A")

Visualizing Single Pair Correlation using using FeatureCorr::pair_scatter function

#Single Pair Scatter on Original RNA-seq Dataset vs log2 transformed RNA-seq Dataset
pair_scatter(df=TCGA40,featurelist=rownames(TCGA40),feature1="RNASEH2A",feature2="PCNA", corrmeth='pearson')[[1]]
pair_scatter(df=logTCGA40,featurelist=rownames(logTCGA40),feature1="RNASEH2A", feature2="PCNA", corrmeth='pearson')[[1]]

Visualizing Multiple Pair-wise Correlation using FeatureCorr::pairwise_corr function

#Multiple pairwise correlation for 40 genes in ~10K samples in The Cancer Genome Atlas-Pan Cancer dataset
corr_pair<-pairwise_corr(df=logTCGA40,featurelist=rownames(logTCGA40), visorder="hclust", clustno=2)
# Separating feature of interest for visualization 
# In This technique you can group features of differen types. For example if I have methylation and transcription data in the same dataframe for same set of feature identfiers
featgroup<-grepl( "RNASE",rownames(logTCGA40)) #optional, a set of features to separated
corr_pair<-pairwisecorr <- pairwise_corr(df=logTCGA40,featurelist=rownames(logTCGA40),featuregroup=featgroup)

DKundnani/FeatureCorr documentation built on April 24, 2022, 7:59 p.m.