README.md

In Ghoshlab/cytoKernel: Differential expression using kernel-based score test

cytoKernel

cytoKernel: An R/Bioconductor package for differential expression using kernel-based score test for high-dimensional biological data.

cytoKernel computes the feature-wise p values and their corresponding adjusted p values in high-dimensional biological experiments.

Method

Liu D, Lin X, Ghosh D."Semiparametric regression of multi-dimensional genetic pathway data: least-squares kernel machines and linear mixed models". Biometrics. 2007;63(4):1079-1088.

Zhan X, Patterson AD, Ghosh D. "Kernel approaches for differential expression analysis of mass spectrometry-based metabolomics data". BMC Bioinformatics. 2015;16:77. Published 2015 Mar 11.

Liu D, Ghosh D, Lin X. "Estimation and testing for the effect > of a genetic pathway on a disease outcome using logistic kernel machine regression via logistic mixed models". BMC Bioinf. 2008; 9(1):292. https://doi.org/10.1186/1471-2105-9-292

Installing cytoKernel

The R-package cytoKernel can be installed from GitHub using the R package devtools:

Use to install the latest version of cytoKernel from GitHub:

if (!require("devtools")) install.packages("devtools")
devtools::install_github("Ghoshlab/cytoKernel")

It can also be installed using Bioconductor:

# install BiocManager from CRAN (if not already installed)
if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")

# install cytoKernel package
BiocManager::install("cytoKernel")

After installation, the package can be loaded into R.

library(cytoKernel)

Using cytoKernel

The main function in the cytoKernel package is CytoK(). The CytoK() function needs two required objects and three optional objects: (1) object: a data frame or a matrix or a Summarized Experiment with one assay object with observations (e.g., cluster-marker combinations or genes) on the rows. and samples as the columns (e.g. let’s call it dataSE). (2) group_factor: a binary categorical response variable that represents the group condition for each sample. For example if the samples represent two different groups or conditions (e.g., before stimulation and after stimulation), provide CytoK() with a phenotype representing which columns in the object are different groups. (e.g. let’s call it groupSamples). (3) lowerRho (optional) a positive value that represents the lower bound of the kernel parameter. Default is 2. (4) upperRho (optional) a positive value that represents the upper bound of the kernel parameter. Default is 12. (5) gridRho (optional) a positive value that represents the number of grid points in the interval of upper and bound of the kernel parameter. Default is 4. (6) alpha (optional) level of significance to control the False Discovery rate (FDR). Default is 0.05. (7) featureVars (optional) Vector of the columns which identify features. If a SummarizedExperiment is used for data, row variables will be used. Default is NULL.

To run the CytoK() function,

CytoKOutput <- CytoK(object = dataSE,
group_factor =groupSamples, lowerRho=2, upperRho=12, gridRho=4,
alpha = 0.05, featureVars = NULL)

Individual slots can be extracted using accessor methods:

CytoKFeatures(CytoKOutput) # extracts the data.frame with shrunken effect size, shrunken effect size sd, unadjusted p value and adjusted p value for each feature

 CytoKFeaturesOrdered(CytoKOutput) # extracts the data.frame with shrunken effect size, shrunken effect size sd, unadjusted p value and adjusted p value for each feature ordered by unadjusted p value from low to high

 CytoKDEfeatures(CytoKOutput) # extracts the percent of differentially expressed features

CytoKData(CytoKOutput) # extracts the original data object

CytoKalpha(CytoKOutput) # extracts the specified level of significance

CytoKFeatureVars(CytoKOutput) # extracts the value of featureVars

The heatmap of the expressed matrix of features on rows ordered by the adjusted p values from low to high can be directly plotted using the plotCytoK() function.

 plotCytoK(object = CytoKOutput,
 group_factor =groupSamples,topK=K,...)

For more details, see vignettes.

Bug reports

Report bugs as issues on the GitHub repository new issue

Contributors

• Tusharkanti Ghosh
• Victor Lui
• Pratyaydipta Rudra
• Souvik Seal
• Thao Vu
• Elena Hsieh
• Debashis Ghosh

Ghoshlab/cytoKernel documentation built on Dec. 17, 2021, 9:32 p.m.