| Title: | Differential expression using kernel-based score test |
|---|---|
| Description: | cytoKernel implements a kernel-based score test to identify differentially expressed features in high-dimensional biological experiments. This approach can be applied across many different high-dimensional biological data including gene expression data and dimensionally reduced cytometry-based marker expression data. In this R package, we implement functions that compute the feature-wise p values and their corresponding adjusted p values. Additionally, it also computes the feature-wise shrunk effect sizes and their corresponding shrunken effect size. Further, it calculates the percent of differentially expressed features and plots user-friendly heatmap of the top differentially expressed features on the rows and samples on the columns. |
| Authors: | Tusharkanti Ghosh [aut, cre], Victor Lui [aut], Pratyaydipta Rudra [aut], Souvik Seal [aut], Thao Vu [aut], Elena Hsieh [aut], Debashis Ghosh [aut, cph] |
| Maintainer: | Tusharkanti Ghosh <[email protected]> |
| License: | GPL-3 |
| Version: | 1.13.0 |
| Built: | 2024-12-18 03:24:56 UTC |
| Source: | https://github.com/bioc/cytoKernel |
The raw data (fcs files) were pre-processed using CATALYST, scuttle, scran Bioconductor packages and igraph CRAN package. The data processing package includes 4 steps and they are as follows: 1. Creating a SingleCellExperiment Object: the flowSet data object along with the metadata are converted into a SingleCellExperiment object using the CATALYST R/Bioconductor package. 2. Clustering: We apply Louvain algorithm using the R package igraph to cluster the expression values by the type markers (surface markers). 3. Median: Medians are calculated within a cluster for every signaling marker and subject. 4. Aggregating and converting the data: We convert the aggregated data into a SummarizedExperiment. The row meta-data indicates "cluster" corresponding to the cluster id for each protein marker. The colData represents the "sample_id", "condition", "patient_id", "ids" The remaining columns indicate median expression intensities for each of the 126 (14 markers * 9 clusters) cluster combination for each sample.
data(cytoHDBMW)data(cytoHDBMW)
SummarizedExperiment assay object containing 126 cluster-marker median expression intenities (features) of 8 subjects (samples).
The HDCytoData package is an extensible resource containing a set of publicly available high-dimensional flow cytometry and mass cytometry (CyTOF) benchmark datasets hosted on Bioconductor’s ExperimentHub platform.
Weber, M L, Soneson, Charlotte (2019). “HDCytoData: Collection of high-dimensional cytometry benchmark datasets in Bioconductor object formats.” F1000Research, 8(v2), 1459.
data(cytoHDBMW)data(cytoHDBMW)
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.
CytoK( object, group_factor, lowerRho = 2, upperRho = 12, gridRho = 4, alpha = 0.05, featureVars = NULL )CytoK( object, group_factor, lowerRho = 2, upperRho = 12, gridRho = 4, alpha = 0.05, featureVars = NULL )
object |
an object which is a |
group_factor |
a group level binary categorical
response associated with each sample or column in the
|
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. |
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.
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.
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
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)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)
Objects of this class store needed information to work with a CytoK object
CytoKFeatures returns the data.frame with shrunk
effect size, shrunk effect size sd, unadjusted p value and
adjusted p value for each feature,
CytoKFeaturesOrdered returns the data.frame with shrunk
effect size, shrunk effect size sd, unadjusted p value and
adjusted p value for each feature ordered by unadjusted p value
from low to high,
CytoKDEfeatures returns the percent of differentially
expressed features based on alpha (level of significance),
CytoKData returns the original data object,
CytoKalpha returns the specified level of
significance. Default is alpha=0.05.
CytoKFeatureVars returns the value of featureVars.
Default is NULL.
CytoKFeaturesCytoK features
CytoKFeaturesOrderedCytoK features ordered by adjusted p values
CytoKDEfeaturesPercent of Differentially Expressed CytoK features
CytoKDataOriginal data object passed to CytoK
CytoKalphaValue of alpha argument passed to CytoK
CytoKFeatureVarsValue of featureVars passed
to CytoK. NULL if featureVars is left blank
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 <- 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)
Accessors for the 'CytoKalpha' slot of a CytoK object.
CytoKalpha(object) ## S4 method for signature 'CytoK' CytoKalpha(object)CytoKalpha(object) ## S4 method for signature 'CytoK' CytoKalpha(object)
object |
an object of class |
Value of CytoKalpha argument passed
to CytoK
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) CytoKalpha(data_CytoK)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) CytoKalpha(data_CytoK)
Given a CytoK object, this function returns the CytoK Data
Accessors for the 'CytoKData' slot of a CytoK object.
CytoKData(object) ## S4 method for signature 'CytoK' CytoKData(object)CytoKData(object) ## S4 method for signature 'CytoK' CytoKData(object)
object |
an object of class |
Original data object passed to CytoK.
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) CytoKData(data_CytoK)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) CytoKData(data_CytoK)
Select CytoK object according to the differentially
expressed features identified by cytoKernel. Features are filtered
if their adjusted p values are greater than CytoKalpha.
CytoKDEData(object, by = c("features"))CytoKDEData(object, by = c("features"))
object |
a CytoK object from |
by |
String specifying which adjusted p values of the features to filter by. Default is "features". |
A list of data.frame's or a SummarizedExperiment.
If a data.frame was originally input into the
CytoK function, a list with two elements, DEdata,
nonDEfeatures, will be returned. If a
SummarizedExperiment was originally input, output will be a
SummarizedExperiment with the filtered assay with
one metadata object nonDEfeatures and four row meta-data
EffectSize, EffectSizeSD, pvalue and
padj.
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) CytoKDEData(data_CytoK, by = "features")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) CytoKDEData(data_CytoK, by = "features")
Accessors for the 'CytoKDEfeatures' slot of a CytoK object.
CytoKDEfeatures(object) ## S4 method for signature 'CytoK' CytoKDEfeatures(object)CytoKDEfeatures(object) ## S4 method for signature 'CytoK' CytoKDEfeatures(object)
object |
an object of class |
The percent of differentially expressed features based on alpha (level of significance).
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) CytoKDEfeatures(data_CytoK)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) CytoKDEfeatures(data_CytoK)
Given a CytoK object, this function returns the CytoK features
Accessors for the 'CytoKFeatures' slot of a CytoK object.
CytoKFeatures(object) ## S4 method for signature 'CytoK' CytoKFeatures(object)CytoKFeatures(object) ## S4 method for signature 'CytoK' CytoKFeatures(object)
object |
an object of class |
The data.frame with shrunk effect size, shrunk effect size sd, unadjusted p value and adjusted p value for each feature.
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) CytoKFeatures(data_CytoK)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) CytoKFeatures(data_CytoK)
Given a CytoK object, this function returns the CytoK features ordered by adjusted p values
Accessors for the 'CytoKFeaturesOrdered' slot of a CytoK object.
CytoKFeaturesOrdered(object) ## S4 method for signature 'CytoK' CytoKFeaturesOrdered(object)CytoKFeaturesOrdered(object) ## S4 method for signature 'CytoK' CytoKFeaturesOrdered(object)
object |
an object of class |
the data.frame with shrunk effect size, shrunk effect size sd, unadjusted p value and adjusted p value for each feature ordered by unadjusted p value from low to high.
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) CytoKFeaturesOrdered(data_CytoK)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) CytoKFeaturesOrdered(data_CytoK)
Given a CytoK object, this function returns the CytoK Feature Vars
Accessors for the 'CytoKFeatureVars' slot of a CytoK object.
CytoKFeatureVars(object) ## S4 method for signature 'CytoK' CytoKFeatureVars(object)CytoKFeatureVars(object) ## S4 method for signature 'CytoK' CytoKFeatureVars(object)
object |
an object of class |
Value of featureVars passed to CytoK. NULL
if featureVars was left blank
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) CytoKFeatureVars(data_CytoK)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) CytoKFeatureVars(data_CytoK)
This function is a helper function that
computes the shrunk effective size mean, shrunk effective size
standard deviation (sd), p value and adjusted p value of each
feature for the function CytoK.
CytoKProc(object, group_factor, lowerRho = 2, upperRho = 12, gridRho = 4)CytoKProc(object, group_factor, lowerRho = 2, upperRho = 12, gridRho = 4)
object |
an object which is a |
group_factor |
a group level binary categorical
response associated with each sample or column in the
|
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]. |
A list of CytoK statistics including
shrunkEffectSizeMean |
the shrunk effective size posterior mean per feature |
shrunkEffectSizeSD |
the shrunk effective size posterior sd per feature |
vec_pValue |
the unadjusted p value per feature |
AdjPvalue_features |
the adjusted p value per feature using the Benjamini-Hochberg procedure. |
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_CytoKProc <- CytoKProc(object=data, group_factor = rep(c(0,1), each=6), lowerRho=2, upperRho=12,gridRho=4)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_CytoKProc <- CytoKProc(object=data, group_factor = rep(c(0,1), each=6), lowerRho=2, upperRho=12,gridRho=4)
Class union allowing CytoKData slot to be
a data.frame or Summarized Experiment
CytoK and CytoKDEData function.This function plots a heatmap of the expression matrix with features (e.g., cluster-marker combinations) on the rows and samples (group factors) as the columns.
plotCytoK(object, group_factor, topK, featureVars = NULL)plotCytoK(object, group_factor, topK, featureVars = NULL)
object |
a CytoK object from |
group_factor |
a group level binary categorical
response associated with each sample or column in the
|
topK |
top K differentially expressed features. |
featureVars |
(Optional) Vector of the columns which identify features. If a 'SummarizedExperiment' is used for 'data', row variables will be used. |
A heatmap will be created showing the samples on the columns and features on the rows.
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) plotCytoK(data_CytoK_HD, group_factor = rep(c(0, 1), c(4, 4)),topK=10, featureVars = NULL)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) plotCytoK(data_CytoK_HD, group_factor = rep(c(0, 1), c(4, 4)),topK=10, featureVars = NULL)