CytoK: CytoK

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

CytoK

Description

This function applies a kernel-based score test for identifying differentially expressed features in high-throughput experiments, called the the CytoK procedure. This function also defines the CytoK class and constructor.

Usage

CytoK(
  object,
  group_factor,
  lowerRho = 2,
  upperRho = 12,
  gridRho = 4,
  alpha = 0.05,
  featureVars = NULL
)

Arguments

object	an object which is a matrix or data.frame with features (e.g. cluster-marker combinations or genes) on the rows and samples as the columns. Alternatively, a user can provide a SummarizedExperiment object and the assay(object) will be used as input for the CytoK procedure.
group_factor	a group level binary categorical response associated with each sample or column in the object. The order of the group_factor must match the order of the columns in object.
lowerRho	(Optional) lower bound of the kernel parameter.
upperRho	(Optional) upper bound of the kernel parameter.
gridRho	(Optional) number of grid points in the interval [lowerRho, upperRho].
alpha	(Optional) level of significance to control the False Discovery Rate (FDR). Default is 0.05.
featureVars	(Optional) Vector of the columns which identify features. If a 'SummarizedExperiment' is used for 'data', row variables will be used.

Details

CytoK (Kernel-based score test in biological feature differential analysis) is a nonlinear approach, which identifies differentially expressed features in high-dimensional biological experiments. This approach can be applied across many different high-dimensional biological data including Flow/Mass Cytometry data and other variety of gene expression data. The CytoK procedure employs a kernel-based score test to identify differentially expressed features. This procedure can be easily applied to a variety of measurement types since it uses a Gaussian distance based kernel.

This function computes the feature-wise p values and their corresponding adjusted p values. Additionally, it also computes the feature-wise shrunk effect sizes and their corresponding shrunk effect size sd's. Further, it calculates the percent of differentially expressed features. See the vignette for more details.

Value

A object of the class CytoK that contains a data.frame of the CytoK features in the CytoKFeatures slot, a data.frame of the CytoK features in the CytoKFeaturesOrdered slot ordered by adjusted p values from low to high, a numeric value of the CytoK differentially expressed features CytoKDEfeatures slot, a data.frame or SummarizedExperiment original data objject in the CytoKData slot, a numeric value of the level of significance in the CytoKalpha slot and (optional) a vector of the columns which identify features in the CytoKfeatureVars slot.

References

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.

Zhan X, Ghosh D. Incorporating auxiliary information for improved prediction using combination of kernel machines. Stat Methodol. 2015; 22:47–57.

Zhan, X., Patterson, A.D. & Ghosh, D. Kernel approaches for differential expression analysis of mass spectrometry-based metabolomics data. BMC Bioinformatics 16, 77 (2015). https://doi.org/10.1186/s12859-015-0506-3

Matthew Stephens, False discovery rates: a new deal, Biostatistics, Volume 18, Issue 2, April 2017, Pages 275–294, https://doi.org/10.1093/biostatistics/kxw041

Examples

data <- cbind(matrix(rnorm(1200,mean=2, sd=1.5),
nrow=200, ncol=6), matrix(rnorm(1200,mean=5, sd=1.9),
nrow=200, ncol=6))
data_CytoK <- CytoK(object=data,
group_factor = rep(c(0,1), each=6), lowerRho=2,
upperRho=12,gridRho=4,alpha = 0.05,
featureVars = NULL)
data("cytoHDBMW")
data_CytoK_HD <- CytoK(object=cytoHDBMW,
group_factor = rep(c(0, 1), c(4, 4)), lowerRho=2,
upperRho=12,gridRho=4,alpha = 0.05,
featureVars = NULL)

Ghoshlab/cytoKernel documentation built on Nov. 24, 2024, 9:17 a.m.