Type: Package
Title: Enhanced 'PAM50' Subtyping of Breast Cancer
Version: 1.0.3
Maintainer: Praveen-Kumar Raj-Kumar <p.rajkumar@wriwindber.org>
Description: Accurate classification of breast cancer tumors based on gene expression data is not a trivial task, and it lacks standard practices.The 'PAM50' classifier, which uses 50 gene centroid correlation distances to classify tumors, faces challenges with balancing estrogen receptor (ER) status and gene centering. The 'PCAPAM50' package leverages principal component analysis and iterative 'PAM50' calls to create a gene expression-based ER-balanced subset for gene centering, avoiding the use of protein expression-based ER data resulting into an enhanced Breast Cancer subtyping.
License: GPL (≥ 3)
Copyright: 2024 Windber Research Institute, Windber, PA - 15963. All Rights Reserved.
Encoding: UTF-8
Suggests: testthat, knitr, rmarkdown
Config/testthat/edition: 3
RoxygenNote: 7.2.3
Imports: Biobase, lattice, ComplexHeatmap, impute
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2025-01-09 16:34:45 UTC; praveen
Author: Praveen-Kumar Raj-Kumar [aut, cre, cph], Boyi Chen [aut], Ming-Wen Hu [aut], Tyler Hohenstein [aut], Jianfang Liu [aut], Craig D. Shriver [aut], Xiaoying Lin [aut, cph], Hai Hu [aut, cph]
Repository: CRAN
Date/Publication: 2025-01-09 17:10:05 UTC

Make intermediate intrinsic subtype calls

Description

This function processes clinical IHC subtyping data and preprocessed PAM50 gene expression data to form a gene expression-guided ER-balanced set.This set is created by combining IHC classification information and using principal component 1 (PC1) to guide the separation.The function computes the median for each gene in this ER-balanced set, updates a calibration file, and runs subtype prediction algorithms to generate intermediate intrinsic subtype calls based on the PAM50 method. Various diagnostics and subtyping results are returned.

Usage

makeCalls.PC1ihc(df.cln, seed = 118, mat, outDir=NULL)

Arguments

df.cln

Data frame of clinical data; It should include the columns 'PatientID' and 'IHC'.

seed

Seed for random number generation to ensure reproducibility. Default is 118.

mat

Matrix of preprocessed PAM50 expression data.

outDir

Directory for output files.If NULL, a subdirectory named "Calls.PC1ihc" within the temporary directory will be used.

Value

Returns a list containing:

Int.sbs

Data frame with integrated subtype and clinical data.

score.fl

Data frame with scores from subtype predictions.

mdns.fl

Data frame with median values for each gene in the ER-balanced set.

SBS.colr

Colors associated with each subtype from the prediction results.

outList

Detailed results from subtype prediction functions.

PC1cutoff

Cutoff values for PC1 used in subsetting.

DF.PC1

Data frame of initial PCA results merged with clinical data.

See Also

prcomp, merge, set.seed

Examples


  data_path <- system.file("extdata", "Sample_IHC_PAM_Mat.Rdat", package = "PCAPAM50")
  load(data_path) # Loads Test.ihc and Test.matrix


  # Prepare the data
  Test.ihc$ER_status <- rep("NA", length(Test.ihc$PatientID))
  Test.ihc$ER_status[grep("^L",Test.ihc$IHC)] = "pos"
  Test.ihc$ER_status[-grep("^L",Test.ihc$IHC)] = "neg"
  Test.ihc <- Test.ihc[order(Test.ihc$ER_status, decreasing = TRUE),]
  Test.matrix <- Test.matrix[, Test.ihc$PatientID]


  df.cln <- data.frame(PatientID = Test.ihc$PatientID, IHC = Test.ihc$IHC, stringsAsFactors = FALSE)


  

  # Call the function
  result <- makeCalls.PC1ihc(df.cln=df.cln, seed = 118, mat = Test.matrix, outDir=NULL)

Make a Conventional PAM50 Intrinsic Subtype Calls

Description

This function processes clinical and preprocessed PAM50 expression data to form an estrogen receptor (ER)-balanced set based on IHC classification. The ER-balanced set is created by distinguishing between ER-negative and ER-positive cases, and it produces conventional PAM50 intrinsic subtype calls.

Usage

makeCalls.ihc(df.cln, seed=118, mat, outDir=NULL)

Arguments

df.cln

Data frame of clinical data; It should include the columns 'PatientID' and 'IHC'.

seed

Seed for random number generation to ensure reproducibility. Default is 118.

mat

Matrix of preprocessed PAM50 expression data.

outDir

Directory for output files.If NULL, a subdirectory named "Calls.PAM50" within the temporary directory will be used.

Value

Returns a list containing:

Int.sbs

Data frame with integrated subtype and clinical data.

score.fl

Data frame with scores from subtype predictions.

mdns.fl

Data frame with median values for each gene in the ER-balanced set.

SBS.colr

Colors associated with each subtype from the prediction results.

outList

Detailed results from subtype prediction functions.

See Also

prcomp, merge, set.seed

Examples


  data_path <- system.file("extdata", "Sample_IHC_PAM_Mat.Rdat", package = "PCAPAM50")
  load(data_path) # Loads Test.ihc and Test.matrix


  # Prepare the data
  Test.ihc$ER_status <- rep("NA", length(Test.ihc$PatientID))
  Test.ihc$ER_status[grep("^L",Test.ihc$IHC)] = "pos"
  Test.ihc$ER_status[-grep("^L",Test.ihc$IHC)] = "neg"
  Test.ihc <- Test.ihc[order(Test.ihc$ER_status, decreasing = TRUE),]
  Test.matrix <- Test.matrix[, Test.ihc$PatientID]


  df.cln <- data.frame(PatientID = Test.ihc$PatientID, IHC = Test.ihc$IHC, stringsAsFactors = FALSE)


  

  # Call the function
  result <- makeCalls.ihc(df.cln=df.cln, seed = 118, mat = Test.matrix, outDir=NULL)

Make PCAPAM50 calls

Description

This function uses the intermediate intrinsic subtype calls and preprocessed PAM50 gene expression data to create an ER-balanced set and produces PCAPAM50 Calls.

Usage

makeCalls.v1PAM(df.pam, seed = 118, mat, outDir=NULL)

Arguments

df.pam

Data frame of PAM data; It should include the columns 'PatientID' and 'PAM50'.

seed

Seed for random number generation to ensure reproducibility.

mat

Matrix of preprocessed PAM50 expression data.

outDir

Directory for output files.If NULL, a subdirectory named "Calls.PCAPAM50" within the temporary directory will be used.

Value

Returns a list containing:

Int.sbs

Data frame with integrated subtype and clinical data.

score.fl

Data frame with scores from subtype predictions.

mdns.fl

Data frame with median values for each gene in the ER-balanced set.

SBS.colr

Colors associated with each subtype from the prediction results.

outList

Detailed results from subtype prediction functions.

See Also

prcomp, merge, set.seed

Examples


  data_path <- system.file("extdata", "Sample_IHC_PAM_Mat.Rdat", package = "PCAPAM50")
  load(data_path) # Loads Test.ihc and Test.matrix


  # Prepare the data
  Test.ihc$ER_status <- rep("NA", length(Test.ihc$PatientID))
  Test.ihc$ER_status[grep("^L",Test.ihc$IHC)] = "pos"
  Test.ihc$ER_status[-grep("^L",Test.ihc$IHC)] = "neg"
  Test.ihc <- Test.ihc[order(Test.ihc$ER_status, decreasing = TRUE),]
  Test.matrix <- Test.matrix[, Test.ihc$PatientID]

  df.cln <- data.frame(PatientID = Test.ihc$PatientID, IHC = Test.ihc$IHC, stringsAsFactors = FALSE)
  outDir <- "Call.PC1"

  # Make a secondary ER-balanced subset and derive intermediate intrinsic subtype calls
  result <- makeCalls.PC1ihc(df.cln=df.cln, seed = 118, mat = Test.matrix, outDir=outDir)

  df.pc1pam = data.frame(PatientID=result$Int.sbs$PatientID,
  			PAM50=result$Int.sbs$Int.SBS.Mdns.PC1ihc,
			IHC=result$Int.sbs$IHC,
			stringsAsFactors=FALSE) ### IHC column is optional
  
  

  # Make a tertiary ER-balanced set and PCAPAM50 calls
  res <- makeCalls.v1PAM(df.pam = df.pc1pam, seed = 118, mat = Test.matrix, outDir=NULL)

Modeling after plotPCA of DESeq

Description

Modeling after plotPCA of DESeq

Usage

my.plotPCA(x, intgroup, ablne = 0,
           colours = c("red","hotpink","darkblue", "lightblue","red3","hotpink3",
           "royalblue3","lightskyblue3"),
           LINE.V = TRUE)

Arguments

x

An ExpressionSet object, with matrix data (x) in ‘assay(x)’, produced for example by ExpressionSet(assayData=Test.matrix, phenoData=phenoData)

intgroup

Subtype condition: a character vector of names in ‘colData(x)’ to use for grouping.

ablne

An x-axis coordinate for drawing a vertical line. Default is 0.

colours

Colors for subtypes present in the condition.

LINE.V

Determines whether or not to draw line. Default is TRUE.

Value

Returns an image containing:

pcafig

The plot.

See Also

prcomp, merge, set.seed

Examples

  library("Biobase")  
  
  data_path <- system.file("extdata", "Sample_IHC_PAM_Mat.Rdat", package = "PCAPAM50")
  load(data_path) # Loads Test.ihc and Test.matrix
  
  pData = data.frame(condition=Test.ihc$IHC)
  rownames(pData) = Test.ihc$PatientID
  phenoData = new("AnnotatedDataFrame", data=pData)#, varMetadata=Metadata
  XSet      = ExpressionSet(assayData=Test.matrix, phenoData=phenoData)
  my.plotPCA(XSet, intgroup=pData$condition, ablne=2.4,
  		colours = c("hotpink","darkblue","lightblue","lightblue3","red"),
  		LINE.V = TRUE)