Package 'SIGN'

Title: Similarity Identification in Gene Expression
Description: Provides a classification framework to use expression patterns of pathways as features to identify similarity between biological samples. It provides a new measure for quantifying similarity between expression patterns of pathways.
Authors: Seyed Ali Madani Tonekaboni [aut], Gangesh Beri [aut], Janosch Ortmann [aut], Benjamin Haibe-Kains [aut, cre]
Maintainer: Benjamin Haibe-Kains <[email protected]>
License: GPL (>= 3)
Version: 0.1.0
Built: 2024-11-12 06:13:24 UTC
Source: https://github.com/cran/SIGN

Help Index

BubbleSort is a function for calculating bubble sort correlation between two vectors

Description

BubbleSort is a function for calculating bubble sort correlation between two vectors

Usage

BubbleSort(Vec1, Vec2)

Arguments

Vec1

Vector of values of 1st feature across samples
Vec2

Vector of values of 2nd feature across samples

Value

Bubble sort similarity between the two vectors

EventRenaming is a function for changing annotation of censored samples to 0 and dead samples to 1 for survival analysis

Description

EventRenaming is a function for changing annotation of censored samples to 0 and dead samples to 1 for survival analysis

Usage

EventRenaming(EventVec, Censored_Annot)

Arguments

EventVec

Status vector for all of the samples (patients) including both samples undergone an event or censored
Censored_Annot

Index of samples censored in the dataset

Value

Vector of events including 0 for censoring and 1 for death

ExpPhen_Matching is a function for matching samples between expression matrices and metadata matrix (clinical feature matrix)

Description

ExpPhen_Matching is a function for matching samples between expression matrices and metadata matrix (clinical feature matrix)

Usage

ExpPhen_Matching(ExpMat, MetaMat, SamID_Meta)

Arguments

ExpMat

Matrix of expression of genes (samples in columns and genes in rows)
MetaMat

Matrix of clinical features (samples in columns)
SamID_Meta

Sample ID in MetaMat

Value

List of expression matrix and metadata of the clinical information after matching patiend IDs between the expression and clinical information matrices

ExpPhen_Subdividing is a function for grouping samples based on a clinical feature available in metadata matrix (clinical feature matrix)

Description

ExpPhen_Subdividing is a function for grouping samples based on a clinical feature available in metadata matrix (clinical feature matrix)

Usage

ExpPhen_Subdividing(ExpMeta_List, SubDiv_ID)

Arguments

ExpMeta_List

List containing expression matrix and metadata matrix
SubDiv_ID

Index of the target clinical feature in metadata matrix for samples grouping

Value

List of expression and clinical information of patients grouped based on the specified clinical feature

ExpPheno_Categorize is a function for grouping samples based on their survival to 3 groups of poor, good, and intermediate

Description

ExpPheno_Categorize is a function for grouping samples based on their survival to 3 groups of poor, good, and intermediate

Usage

ExpPheno_Categorize(ExpMeta_List, Time_ID, Event_ID, Mad_Factor,
  MinNum_ExClass, Expression_Log2 = FALSE)

Arguments

ExpMeta_List

List containing expression matrix and metadata matrix
Time_ID

Index of time to death in metadata matrix
Event_ID

Index of event in metadata matrix
Mad_Factor

Threshold of mad in time to death values to determine poor survival group
MinNum_ExClass

Minimum number of samples that has to be kept in poor and good group (if number of samples is lower than this threhold, more samples will be addedd in order of survival)
Expression_Log2

Parameter for gene expsression value transformation to logarithmic scale (log2(expression value+1))

Value

List of expression matrices, and time to event as well as event for the patients within each category of poor, intermediate or good survival

GeneMatching is a function to remove uncommon genes between a list of expression matrices

Description

GeneMatching is a function to remove uncommon genes between a list of expression matrices

Usage

GeneMatching(ExpList)

Arguments

ExpList

List of expression matrices

Value

List of expression matrices restricted to the common genes between them

Genes_SimCal is a function to calculate similarity between a set of samples and 2 reference groups of samples

Description

Genes_SimCal is a function to calculate similarity between a set of samples and 2 reference groups of samples

Usage

Genes_SimCal(ExpMat_Test, ExpMat_Ref1, ExpMat_Ref2, RefIDs, TestClassIter,
  SampleIter)

Arguments

ExpMat_Test

Expression matrix for the test samples for which SIGN will indetify the similarity with the 2 reference sataset
ExpMat_Ref1

Expression matrix for the 1st reference set fo samples
ExpMat_Ref2

Expression matrix for the 2nd reference set fo samples
RefIDs

Annotations corresponding to the 2 expression matrices (1st and 2nd names are associated with the 1st and 2nd expression matrix and )
TestClassIter

Index to be matched with RefIDs for removal of test samples from reference expression matrices
SampleIter

Index of samples in the test expression matrix exist in referencece expression matrix 1 or 2

Value

Vector of similarity between the target samples and the 2 reference sets

GSVA_Calculation is a function for Calculating correlation between expression level of pathways between 2 groups using GSVA

Description

GSVA_Calculation is a function for Calculating correlation between expression level of pathways between 2 groups using GSVA

Usage

GSVA_Calculation(ExpMat1, ExpMat2, GeneVec, GeneSets,
  Name = "SampleComparison")

Arguments

ExpMat1

Expression matrix of genes in the 1st group of sampls
ExpMat2

Expression matrix of genes in the 2nd group of sampls
GeneVec

Name of genes in the same order as considered in ExpMat1 and ExpMat2
GeneSets

List of genes within pathways
Name

Name used for naming the columns of output matrix of correlation between the 2 groups

Value

Similarity of the pathway between the two expression matrices based on pearson correlation, bubble sort, and wilcoxon paaired rank test using GSVA enrichment scores of pathways

Pathway_Grouping is a function to make a pathway list from files containing genes within each pathway

Description

Pathway_Grouping is a function to make a pathway list from files containing genes within each pathway

Usage

Pathway_Grouping(PathwayDir, Pattern)

Arguments

PathwayDir

Path of directory including the files of pathways
Pattern

Pattern should be used to select the files of pathway genes from PathwayDir

Value

List of genes within the pathway

Pathway_similarity is a function for calculating correlation between expression level of pathways between 2 groups using all the available approaches in SIGN

Description

Pathway_similarity is a function for calculating correlation between expression level of pathways between 2 groups using all the available approaches in SIGN

Usage

Pathway_similarity(ExpMat1, ExpMat2, GeneVec, GeneSets, Name)

Arguments

ExpMat1

Expression matrix of genes in the 1st group of sampls
ExpMat2

Expression matrix of genes in the 2nd group of sampls
GeneVec

Name of genes in the same order as considered in ExpMat1 and ExpMat2
GeneSets

List of genes within pathways
Name

Name used for naming the columns of output matrix of correlation between the 2 groups

Value

Similarity of the pathway between the two expression matrices using pearson correlation, bubble sort, and wilcoxon paaired rank test

SIGN_Aggregate is a function to reshape the list of pathway scoring, time to death, and event and return a summary list

Description

SIGN_Aggregate is a function to reshape the list of pathway scoring, time to death, and event and return a summary list

Usage

SIGN_Aggregate(ScoreList, TimeList, EventList)

Arguments

ScoreList

List of similarity scores identified using different methodologies
TimeList

List of time to event (death) for different groups of patients
EventList

List of event vectors (death or censored) for different groups of patients

Value

List of scores identified for each sample as well as time to death and event of that sample

SIGN_Ensemble_SimCal is a function for Generating list fo similarities based on different pathway quantification methods and similarity measures

Description

SIGN_Ensemble_SimCal is a function for Generating list fo similarities based on different pathway quantification methods and similarity measures

Usage

SIGN_Ensemble_SimCal(ExpList, RefClassID, TestClassID, GeneID, PathwaySets)

Arguments

ExpList

List of expression matrices for different groups of samples used in the centroid classification scheme
RefClassID

Names of the matrices in the ExpList
TestClassID

ID of a matrix in ExpList to be used as test set
GeneID

Parameter to determine if gene annotations are provided as Symbols or EntrezIDs
PathwaySets

List of pathways containing gene annotations for each pathways

Value

List of similarities identified in both gene and pathway level

Similarities_Wrapper is wrapper to identify similarities between the expression of genes in target sample and the reference expression matrix

Description

Similarities_Wrapper is wrapper to identify similarities between the expression of genes in target sample and the reference expression matrix

Usage

Similarities_Wrapper(ExpMat_Test, ExpMat_Ref, GeneVec, PathwaySet, RefID,
  TestClassIter, SampleIter)

Arguments

ExpMat_Test

Expression matrix of test samples
ExpMat_Ref

Expression matrix of reference samples
GeneVec

Vector of gene names
PathwaySet

List of pathways containing gene annotations for each pathways
RefID

Class of the reference set
TestClassIter

Class of the test set (if it is the same as reference set, the target test sample will be removed fro the reference set)
SampleIter

Target test sample in ExpMat_Testto be used for comparison with ExpMat_Ref

Value

List of similarities between the target sample and the expression matrix of reference samples

SimSummary_2Class is a function to calculating similarity between two set of samples

Description

SimSummary_2Class is a function to calculating similarity between two set of samples

Usage

SimSummary_2Class(SimMat1, SimMat2)

Arguments

SimMat1

Matrix of similarity of the target samples with the 1st reference matrix
SimMat2

Matrix of similarity of the target samples with the 2nd reference matrix

Value

Matrix of similarities of samples

Survival_Stats is a function for building cox model using all the features and each feature as a separate model

Description

Survival_Stats is a function for building cox model using all the features and each feature as a separate model

Usage

Survival_Stats(ScoreMat, TimeVec, EventVec)

Arguments

ScoreMat

Matrix of feature values used for survival predition
TimeVec

Vectore of time to death of samples (patients)
EventVec

Vector of events for the samples (patients) as being dead or censored

Value

A list containing summary of a cox model using all of the features and separate cox models for each feature

SurvivalStat_PostProcess is a function to Extract summary statistics of the built cox model

Description

SurvivalStat_PostProcess is a function to Extract summary statistics of the built cox model

Usage

SurvivalStat_PostProcess(StatList)

Arguments

StatList

Summary lists of the cox models built using all the

Value

A list including Cindex, Cindex_std and LogTest_pval

TSC is a function to calculate transcriprtional similarity coefficient between two biological pathways

Description

TSC is a function to calculate transcriprtional similarity coefficient between two biological pathways

Usage

TSC(PathwayExp1, PathwayExp2)

Arguments

PathwayExp1

Expression matrix of genes within the chosen pathway in the 1st set of samples
PathwayExp2

Expression matrix of genes within the chosen pathway in the 2nd set of samples

Value

Transcriptional similarity coefficient

Examples

Pathway1_ExpMat <- matrix(runif(100,0,10), ncol = 10)
Pathway2_ExpMat <- matrix(runif(100,0,10), ncol = 10)
TSC(Pathway1_ExpMat, Pathway2_ExpMat)