CytoK {cytoKernel} | R Documentation

## 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)

[Package cytoKernel version 1.0.0 Index]