Package 'GSDA'

Title: Gene Set Distance Analysis (GSDA)
Description: The gene-set distance analysis of omic data is implemented by generalizing distance correlations to evaluate the association of a gene set with categorical and censored event-time variables.
Authors: Xueyuan Cao [aut, cre], Stanley Pounds [aut]
Maintainer: Xueyuan Cao <[email protected]>
License: GPL (>= 2)
Version: 1.0
Built: 2024-12-05 03:26:24 UTC
Source: https://github.com/xueyuancao/gsda

Help Index

Gene Set Distance Analysis (GSDA)

Description

The gene-set distance analysis of omic data is implemented by generalizing distance correlations to evaluate the association of a gene set with categorical and censored event-time variables.

Details

The DESCRIPTION file:

Package: GSDA
Type: Package
Title: Gene Set Distance Analysis (GSDA)
Version: 1.0
Date: 2021-01-014
Authors@R: c(person("Xueyuan", "Cao", email = "[email protected]", role = c("aut", "cre")), person("Stanley", "Pounds", email = "[email protected]", role = c("aut")))
Description: The gene-set distance analysis of omic data is implemented by generalizing distance correlations to evaluate the association of a gene set with categorical and censored event-time variables.
Depends: R (>= 3.5.0),msigdbr
License: GPL (>= 2)
biocViews: Microarray, Bioinformatics, Gene expression
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
LazyLoad: yes
Repository: https://xueyuancao.r-universe.dev
RemoteUrl: https://github.com/xueyuancao/gsda
RemoteRef: HEAD
RemoteSha: d7f71cb8b75caa0e7aae2c65795ee87326f5da94
Author: Xueyuan Cao [aut, cre], Stanley Pounds [aut]
Maintainer: Xueyuan Cao <[email protected]>

Index of help topics: 

GSDA-package            Gene Set Distance Analysis (GSDA)
U.center                U Centering
best.dist.corr          Best Distance Correlattion
cat.dist                Distance for a Categorical Variable
dist.corr               Distance Correlattion
gsda                    Gene-Set Distance Analsysis (GSDA)
kegg.ml.gsets           KEGG gene set data for the AML and CML pathways
prep.gsda               GSDA Data Preparation
prep.msigdb             Preparation of MSigDB for GSDA
print.bdc               Print Method for Best Distance Correlation
print.dcor              Print Method for Distance Correlation
print.gsda.result       Print Method for GSDA Result
surv.dist               Distance of a Survival Endpoint
target.aml.clin         Clinical outcomes for AML TARGET Project
target.aml.expr         RNA-seq expression from the AML TARGET project
uc.dist                 U-centered Distance Matrix
write.gsda.csv.file     Write GSDA Result to a Comma Delimited File

Further information is available in the following vignettes:

GSDA An Introduction to the GSDA Package (source, pdf)

Author(s)

Xueyuan Cao [aut, cre], Stanley Pounds [aut]

Maintainer: Xueyuan Cao <[email protected]>

References

Cao X and Pounds S (2021) Gene-Set Distance Associations (GSDA): A Powerful Tool for Gene-Set Association Analysis.

Examples

data(target.aml.clin)
data(target.aml.expr)
data(kegg.ml.gsets)
res=gsda(target.aml.expr,
         target.aml.clin,
         kegg.ml.gsets,
         "Chloroma","oe","ct")

Best Distance Correlattion

Description

Use a backward elimination procedure to identify a subset of variables in X most strongly associated with Y according to the distance correlation p-value.

Usage

best.dist.corr(X, Y, x.dist = "oe", y.dist = "oe")

Arguments

X

The omic numeric data matrix with subjects in rows and variables in columns. Note that this is the TRANSPOSE of the omic data matrix for some other omic data analysis packages and for the gsda function of this package.
Y

Numeric data matrix, vector, or data.frame with each row representing a subject. The function assumes the same set of subjects are represent in the same order in X and Y.
x.dist

The distance metric for omic data (X), may be "oe" (overall Euclidean), "me" (marginal Euclidean), "om" (overall Manhattan), or "mm" (marginal Manhattan).
y.dist

The distance metric for clinical data, may be "oe" (overall Euclidean), "me" (marginal Euclidean), "om" (overall Manhattan), or "mm" (marginal Manhattan), same options as for X

Details

This function computes dist.corr for X and Y. It then determines which column of X may be dropped to give the smallest p-value in dist.corr. This process is repeated until X has been reduced to only one variable. In this way, a dist.corr p-value is obtained after dropping each X variable. The subset of X variables giving the smallest p-value in this series of analyses is returned with additional result details.

Value

A list with the following components:

rX

reduced X matrix
best.res

best result by backward elimination
all.res

all backward elimination results: the first column has the index of the column of X that was dropped; the second column has the negative log10 p-value of the resulting X matrix
X

echoes input X
Y

echoes input Y

Author(s)

Xueyuan Cao [email protected] and Stanley Pounds [email protected]

References

Cao X and Pounds S (2021) Gene-Set Distance Associations (GSDA): A Powerful Tool for Gene-Set Association Analysis.

See Also

dist.corr

Examples

data(target.aml.clin)
data(target.aml.expr)
target.aml.expr=sqrt(target.aml.expr)
target.aml.expr=t(target.aml.expr)

bdc.chl=best.dist.corr(target.aml.expr,
                       target.aml.clin$Chloroma,
                       "oe","ct")

Distance for a Categorical Variable

Description

A function to calculate the distance for a categorical variable.

Usage

cat.dist(X)

Arguments

X

vector of category designations

Details

This function calculates distance function for a categorical variable. The result is a square n by n matrix in which entry (i,j) has value 1 if entry i and entry j of the input vector X are not equal and entry (i,j) of the result matrix has value 0 if entry i and entry j of the input vector are equal. The distance between subject i and subject j is zero if the two subjects have the same categorical designation. The distance between subject i and subject j is one if the two subjects do not have the same categorical designation.

Value

A square matrix with each dimension equal to the length of X.

Author(s)

Xueyuan Cao [email protected] and Stanley Pounds [email protected]

References

Cao X and Pounds S (2021) Gene-Set Distance Associations (GSDA): A Powerful Tool for Gene-Set Association Analysis.

See Also

surv.dist

Examples

data(target.aml.clin)
cd=cat.dist(target.aml.clin$Chloroma)
cd[1:5,1:5]

Distance Correlattion

Description

Calculate the distance correlation for a gene set's omic data matrix with another variable.

Usage

dist.corr(X, Y, x.dist = "me", y.dist = "me")

Arguments

X

The omic numeric data matrix with subjects as rows and variables as columns. Note this is the TRANSPOSE of how some omic data analysis packages represent omic data and how the omic data is represented in the gsda function of this package.
Y

Numeric data matrix, vector, or data.frame. The rows of X and rows of Y must represent the same set of subjects in the same order.
x.dist

The distance metric for omic data (X), may be "oe" (overall Euclidean), "me" (marginal Euclidean), "om" (overall Manhattan), or "mm" (marginal Manhattan).
y.dist

The distance metric for clinical data, may be "oe" (overall Euclidean), "me" (marginal Euclidean), "om" (overall Manhattan), or "mm" (marginal Manhattan), same options as for X

Details

The function calculates distance matrix for X and Y using one of the four methods "oe" (overall Euclidean), "me" (marginal Euclidean), "om" (overall Manhattan), or "mm" (marginal Manhattan). Then, the distance matrices are centered by U-centering and distance correlation is calculated as the inner product of the two U-centered distance matrices over the squared of inner product of each of the two U-centered distance matrices. The distance correlation t-statistics follows a t-distribution with n*(n-3)/2 degree of freedom according to Zhu et al.(2020).

Value

A list with the following components:

odCor

overall distance correlation statistic
t.odCor

t-stat for overall distance correlation statistic
p.odCor

p-value for overall distance correlation statistic
dCor

distance-based correlation matrix for each pair of variables.
t.dCor

t-stat for distance-based correlation matrix
p.dCor

p-value for distance-based correlation matrix
X

echo input data matrice X
Y

echo input data matrice Y
x.dist

echo input distance metric for X
y.dist

echo input distance metric for Y

Author(s)

Xueyuan Cao [email protected] and Stanley Pounds [email protected]

References

Cao X and Pounds S (2021) Gene-Set Distance Associations (GSDA): A Powerful Tool for Gene-Set Association Analysis.

Zhu C, Yao S, Zhang X and Shao X (2020) Distance-based and RKHS-based Dependence Metrics in High Dimension. arXiv:1902.03291

See Also

best.dist.corr

Examples

data(target.aml.clin)
data(target.aml.expr)
target.aml.expr=sqrt(target.aml.expr)
target.aml.expr=t(target.aml.expr)

dc.chl=dist.corr(target.aml.expr,
                 target.aml.clin$Chloroma,
                 "oe","ct")

Gene-Set Distance Analsysis (GSDA)

Description

This function impletements the gene-set distance analysis (GSDA) of omic data by generalizing distance correlations to evaluate the association of each of a series gene sets with numeric, categorical, and censored event-time variables.

Usage

gsda(omic.data, clin.data, vset.data, clin.vars, omic.dist, clin.dist)

Arguments

omic.data

The genomic data matrix with features as rows and subjects as columns. The column names of omic.data are assumed to be observation identifiers. The gsda function calls the function prep.gsda to merge omic.data (by column name) and clin.data (by the column named "ID") before performing the GSDA procedure.
clin.data

A data frame of clinical data. Each row is a subject and each column is a variable. The "ID" column of clin.data includes observation identifiers. The gsda function calls the function prep.gsda to merge omic.data (by column name) and clin.data (by the column named "ID") before performing the GSDA procedure.

vset.data

Variable set data. Each row assigns a variable (column named vID) to a variable set (column named vset).
clin.vars

Column name(s) of clinical variable(s) to be associated with the gene-sets.
omic.dist

The distance metric for omic data, may be "oe" (overall Euclidean), "me" (marginal Euclidean), "om" (overall Manhattan), or "mm" (marginal Manhattan)
clin.dist

The distance metric for clinical data, may be "oe" (overall Euclidean), "me" (marginal Euclidean), "om" (overall Manhattan), or "mm" (marginal Manhattan)

Details

This function performs the GSDA method described by Cao and Pounds (2020) through generalizing distance correlations to evaluate the association of a gene set with categorical and censored event-time variables. The distance matrices are centered by U-centering and distance correlation is the inner product of the two U-centered distance matrices over the squared of inner product of each of the two U-centered distance matrices. The distance correlation t-statistics asymptotically follows a t-distribution with n*(n-3)/2 degree of freedom according to Zhu et al. (2020).

Value

A data.frame with the following columns:

vset

The name of variable set (gene-set).
vIDs

The list of variables in the variable set (gene-set).
dCor

The distance association statistics for the variable set.
p.vset

The p-value.
comp.time

Computation time for each set.

Author(s)

Xueyuan Cao [email protected] and Stanley Pounds [email protected]

References

Cao X and Pounds S (2021) Gene-Set Distance Associations (GSDA): A Powerful Tool for Gene-Set Association Analysis.

Zhu C, Yao S, Zhang X and Shao X (2020) Distance-based and RKHS-based Dependence Metrics in High Dimension. arXiv:1902.03291

See Also

GSDA

Examples

data(target.aml.clin)
data(target.aml.expr)
data(kegg.ml.gsets)
res=gsda(target.aml.expr,
         target.aml.clin,
         kegg.ml.gsets,
         "Chloroma","oe","ct")

KEGG gene set data for the AML and CML pathways

Description

A data set with the list of ensemble gene identifiers for the acute myeloid leukemia (AML) and chronic myeloid leukemia pathways as defined in the KEGG pathway database

Usage

data("kegg.ml.gsets")

Format

A data frame with 128 rows describing the pairings between the following 2 variables.

vset

KEGG pathway name

vID

Ensemble gene (ENSG) identifier

Details

A dataset with assignments of ensemble gene identifiers (ENSG) to KEGG pathway names

Source

http://software.broadinstitute.org/gsea/msigdb/cards/KEGG_CHRONIC_MYELOID_LEUKEMIA

http://software.broadinstitute.org/gsea/msigdb/cards/KEGG_ACUTE_MYELOID_LEUKEMIA

Merged with information from ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/GENE_INFO/Mammalia/Homo_sapiens.gene_info.gz to translate gene symbols to Ensemble gene (ENSG) identifiers

Examples

data(kegg.ml.gsets)

GSDA Data Preparation

Description

A function to prepare omic data, clinical data and variable set into an ordered matched format for GSDA analysis.

Usage

prep.gsda(data.mtx, clin.data, vset.data = NULL)

Arguments

data.mtx

Numeric data matrix with column names giving subject identifiers.
clin.data

Data.frame with column named "ID" with subject identifiers matching column names of data.mtx.
vset.data

data.frame of variable-set assignments with columns named "vID" for variable identifier and "vset" for name or identifier of a variable set (gene-set).

Details

The gsda function uses prep.gsda to prepare the omic data matrix, clinical dataframe and variable set (gene set) into ordered and matched format, which is then used for GSDA analysis.

Value

A list with the following components:

omic.data

data matrix with columns in the same order as clin.data$ID.
clin.data

data.frame with ID column in same order as columns of omic.data.
vset.data

variable set ordered by name of variable set.
vset.index

simple data.frame showing first and last row of vset.data for each variable set.

Author(s)

Xueyuan Cao [email protected] and Stanley Pounds [email protected]

References

Cao X and Pounds S (2021) Gene-Set Distance Associations (GSDA): A Powerful Tool for Gene-Set Association Analysis.

See Also

gsda

Examples

data(target.aml.clin)
data(target.aml.expr)
data(kegg.ml.gsets)
gsdaprep=prep.gsda(target.aml.expr,
         target.aml.clin,
         kegg.ml.gsets)

Preparation of MSigDB for GSDA

Description

This function prepares the gene sets of a species in MsigDB for gene-set distance analysis.

Usage

prep.msigdb(species = "Homo sapiens", vset = "gs_name", vID = "gene_symbol")

Arguments

species

Name of species in MSigDB.
vset

Name of MSigDB column to use as vset in gsda, default is "gs_name".
vID

Name of MSigDB column to use as vID in gsda, default is "gene_symbol".

Details

Take a species from MsigDB (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp), extract gene set definiton and prepare a data frame with gene sets and genes to be used as vset.data in the gsda function.

Value

A two-column data.frame with the columns vset and vID

Author(s)

Xueyuan Cao [email protected] and Stanley Pounds [email protected]

References

Cao X and Pounds S (2021) Gene-Set Distance Associations (GSDA): A Powerful Tool for Gene-Set Association Analysis.

Examples

gsets=prep.msigdb()
head(gsets)

Print Method for Best Distance Correlation

Description

Print the result of the best distance correlation (best.dist.corr)

Usage

## S3 method for class 'bdc'
print(x,...)

Arguments

x

a class of bdc
...

further arguments passed to or from other methods

Details

Print the summary of result of best distance correlation to stdout.

Value

No return value, called for side effects

Author(s)

Xueyuan Cao [email protected] and Stanley Pounds [email protected]

References

Cao X and Pounds S (2021) Gene-Set Distance Associations (GSDA): A Powerful Tool for Gene-Set Association Analysis.

See Also

print.dcor, print.gsda.result

Print Method for Distance Correlation

Description

Print the summary of result of distance correlation (dist.corr function).

Usage

## S3 method for class 'dcor'
print(x,...)

Arguments

x

result of dist.corr, class dcor
...

further arguments passed to or from other methods

Details

Print the summary of result of distance correlation to stdout.

Value

No return value, called for side effects

Author(s)

Xueyuan Cao [email protected] and Stanley Pounds [email protected]

References

Cao X and Pounds S (2021) Gene-Set Distance Associations (GSDA): A Powerful Tool for Gene-Set Association Analysis.

See Also

print.bdc, print.gsda.result

Print Method for GSDA Result

Description

Print the of result of gene-set distance analysis (gsda function).

Usage

## S3 method for class 'gsda.result'
print(x,...)

Arguments

x

result of gene-set distance analysis (gsda function)
...

further arguments passed to or from other methods

Details

Print the result of gene-set distance analysis to stdout.

Value

No return value, called for side effects

Author(s)

Xueyuan Cao [email protected] and Stanley Pounds [email protected]

References

Cao X and Pounds S (2021) Gene-Set Distance Associations (GSDA): A Powerful Tool for Gene-Set Association Analysis.

See Also

print.dcor, print.bdc

Distance of a Survival Endpoint

Description

A function to calculate the distance for a survivial endpoint.

Usage

surv.dist(stime.evnt)

Arguments

stime.evnt

A data frame with time in first column and censor in second column.

Details

This function calculates the distance matrix for a censored event-time variable. The calculation is based on the formula in Section 2.4 of Cao and Pounds (2021). The distance metric for censored event-time data is based on the rank-based association statistic for this type of data proposed by Jung et al (2005).

Value

A square matrix with nrow and ncol equal to the nrow of stime.evnt. Entry (i,j) of the result matrix gives the survival distance between subjects represented in rows i and j of stime.evnt.

Author(s)

Xueyuan Cao [email protected] and Stanley Pounds [email protected]

References

Cao X and Pounds S (2021) Gene-Set Distance Associations (GSDA): A Powerful Tool for Gene-Set Association Analysis.

Jung SH, Owzar K, and George SL (2005) A mutiple testing procedure to associate gene expression levels with survival. Statistics in Medicine 24: 3077-88.

See Also

cat.dist

Examples

data(target.aml.clin)
srv.dist=surv.dist(target.aml.clin[,c("efs.time","efs.evnt")])

Clinical outcomes for AML TARGET Project

Description

A dataset with subject identifier, survival time, and death indicator for 123 pediatric AML patients

Usage

data("target.aml.clin")

Format

A data frame with 123 observations of the following 5 variables.

ID

subject identifier, a character vector

Chloroma

a character vector

logWBC

a numeric vector

efs.time

event-free survival time, a numeric vector

efs.evnt

event indicator (0 = censored, 1 = event) for efs.time, a numeric vector

Details

A dataset with clinical data for each of 123 pediatric AML patients

Source

obtained from https://target-data.nci.nih.gov/Public/AML/clinical/harmonized/

Examples

data(target.aml.clin)

RNA-seq expression from the AML TARGET project

Description

A matrix of RNA-seq gene expression values for 123 pediatric AML patients from the TARGET project for genes in the KEGG AML and CML pathways

Usage

data("target.aml.expr")

Format

Each row contains the expression values of one 94 Ensemble genes for all 123 patients. Each column contains the expression values of all 94 Ensemble genes for one patient. The rownames give the Ensemble identifiers for the genes. The columns give the patient identifiers.

Details

A RNA-seq dataset with expression levels in 94 ensemble gene identifiers for 123 pediatric AML patients

Source

https://target-data.nci.nih.gov/Public/AML/mRNA-seq/L3/expression/

Examples

data(target.aml.expr)

U Centering

Description

U-center the distance matrix in preparation of computing distance correlations.

Usage

U.center(d)

Arguments

d

A square numeric data matrix

Details

This funtion centers the distance matrix according to U-centering formula on page 6 of arXiv 1902.03291 paper

Value

A centered data matrix

Author(s)

Xueyuan Cao [email protected] and Stanley Pounds [email protected]

References

Cao X and Pounds S (2021) Gene-Set Distance Associations (GSDA): A Powerful Tool for Gene-Set Association Analysis.

Zhu C, Yao S, Zhang X and Shao X. Distance-based and RKHS-based Dependence Metrics in High Dimension. arXiv:1902.03291

See Also

uc.dist

Examples

data(target.aml.clin)
cd=cat.dist(target.aml.clin$Chloroma)
ud=U.center(cd)
ud[1:5,1:5]

U-centered Distance Matrix

Description

The function calculates the U-centered distance matrix for a variable.

Usage

uc.dist(X, dmeth = "me")

Arguments

X

vector, matrix, or data.frame to compute a distance matrix
dmeth

Distance method to use, options include "oe" for overall Euclidean, "me" for marginal Euclidean, "om" for overall Manhattan, "mm" for marginal Manhattan, "ct" for categorical, and "st" for censored survival time.

Details

A distance matrix is first calculated for a scale or data frame of a variable. The distance matrix is then centered according to U-centering formula on page 6 of arXiv 1902.03291 paper.

Value

For distance methods "oe", "om", "ct", and "st", one matrix of overall distances computed using data from all variables. For distance methods "me" and "mm", an array of distance matrices, one distance matrix per variable.

Author(s)

Xueyuan Cao [email protected] and Stanley Pounds [email protected]

References

Cao X and Pounds S (2021) Gene-Set Distance Associations (GSDA): A Powerful Tool for Gene-Set Association Analysis.

Zhu C, Yao S, Zhang X and Shao X. Distance-based and RKHS-based Dependence Metrics in High Dimension. arXiv:1902.03291

See Also

U.center, prep.gsda

Examples

data(target.aml.expr)
target.aml.expr=sqrt(target.aml.expr)
target.aml.expr=t(target.aml.expr)
oe.dist=uc.dist(target.aml.expr,"oe") # overall Euclidean

Write GSDA Result to a Comma Delimited File

Description

Write a gene-set distance analysis result to a comma delimited file (.csv)

Usage

write.gsda.csv.file(gsda.result, out.file)

Arguments

gsda.result

A class of gene-set distance analysis result
out.file

A .csv file name with directory

Value

A saved .csv file.

Author(s)

Xueyuan Cao [email protected] and Stanley Pounds [email protected]