Package 'Lheuristic' reference manual

Title:	Detection of scatterplots with L-shaped pattern
Description:	The Lheuristic package identifies scatterpots that follow and L-shaped, negative distribution. It can be used to identify genes regulated by methylation by integration of an expression and a methylation array. The package uses two different methods to detect expression and methyaltion L- shapped scatterplots. The parameters can be changed to detect other scatterplot patterns.
Authors:	Sanchez Pla Alex [aut, cre] (ORCID: <https://orcid.org/0000-0002-8673-7737>), Miro Cau Berta [aut] (ORCID: <https://orcid.org/0000-0001-6049-8697>)
Maintainer:	Sanchez Pla Alex <[email protected]>
License:	MIT + file LICENSE
Version:	1.5.0
Built:	2026-05-30 07:48:29 UTC
Source:	https://github.com/bioc/Lheuristic

binScore

Description

binScore can be used to score scatterplots by directly comparing the sample counts with a matrix of minimal or maximal percentages/counts to be found in each cell. It implements the three bands rule implicitly by setting threshold values.

Usage

binScore(aGrid, aReq)
binScore(aGrid, aReq)

Arguments

aGrid

A matrix of counts as computed by 'calcFreqs' function.

aReq

A matrix of minimum or maximum counts to be found in each cell if L-shape is TRUE.

Value

a score value for each scatterplot

Examples

# Generate some example data
aGrid <- matrix(c(20, 3, 0, 10, 2, 2, 20, 10, 20),
    nrow = 3, ncol = 3, byrow = TRUE
)
aReq <- matrix(c(15, 5, 0, 0, 5, 5, 10, 10, 15),
    nrow = 3, ncol = 3, byrow = TRUE
)

# Calculate the bin score
binScore(aGrid, aReq)

# Generate some example data
aGrid <- matrix(c(20, 3, 0, 10, 2, 2, 20, 10, 20),
    nrow = 3, ncol = 3, byrow = TRUE
)
aReq <- matrix(c(15, 5, 0, 0, 5, 5, 10, 10, 15),
    nrow = 3, ncol = 3, byrow = TRUE
)

# Calculate the bin score
binScore(aGrid, aReq)

calcFreqs

Description

calcFreqs Given a MultiAssayExperiment with methylation and expression data, the function overlays a grid on a YMet ~ Xmet scatterplot and returns a 3x3 matrix with point counts per grid cell based on the vertical and horizontal lines.

Usage

calcFreqs(
  mae,
  geneNum,
  x1,
  x2,
  y1 = NULL,
  y2 = NULL,
  percY1 = 1/3,
  percY2 = 2/3
)
calcFreqs(
  mae,
  geneNum,
  x1,
  x2,
  y1 = NULL,
  y2 = NULL,
  percY1 = 1/3,
  percY2 = 2/3
)

Arguments

mae

MultiAssayExperiment object containing methylation and expression matrices.

geneNum

row of expression/methylation matrix for which the frequencies will be computed.

x1, x2

Coordinates of vertical points in the X axis. Because it is expected to contain. methylation values that vary between 0 and 1, default values are 1/3 and 2/3.

y1, y2

Coordinates of vertical points on the Y-axis. If set to NULL, they will be automatically assigned as the percentiles of 'y' defined by 'percY1' and 'percY2'.

percY1, percY2

Default values for 'y1' and 'y2' when set to 'NULL'

Value

a matrix with calculated frequencies

Examples


# Methylation data
methylData <- matrix(runif(50), nrow = 10)
colnames(methylData) <- paste0(
    "samp",
    1:ncol(methylData)
)
rownames(methylData) <- paste0(
    "gene",
    1:nrow(methylData)
)
# Expression data
expresData <- matrix(rnorm(50), nrow = 10)
colnames(expresData) <- paste0(
    "samp",
    1:ncol(methylData)
)
rownames(expresData) <- paste0(
    "gene",
    1:nrow(methylData)
)
# ColData
colDat <- data.frame(
    sampleID = colnames(methylData),
    name = letters[1:ncol(methylData)]
)

rownames(colDat) <- colDat$sampleID
mae <- MultiAssayExperiment::MultiAssayExperiment(
    experiments = list(
        methylation = methylData,
        expression = expresData
    ),
    colData = colDat
)
geneRow <- 1
x1 <- 1 / 3
x2 <- 2 / 3
y1 <- NULL
y2 <- NULL
percY1 <- 1 / 3
percY2 <- 2 / 3

calcFreqs(
    mae = mae, geneNum = geneRow,
    x1, x2, y1, y2, percY1, percY2
)
# Methylation data
methylData <- matrix(runif(50), nrow = 10)
colnames(methylData) <- paste0(
    "samp",
    1:ncol(methylData)
)
rownames(methylData) <- paste0(
    "gene",
    1:nrow(methylData)
)
# Expression data
expresData <- matrix(rnorm(50), nrow = 10)
colnames(expresData) <- paste0(
    "samp",
    1:ncol(methylData)
)
rownames(expresData) <- paste0(
    "gene",
    1:nrow(methylData)
)
# ColData
colDat <- data.frame(
    sampleID = colnames(methylData),
    name = letters[1:ncol(methylData)]
)

rownames(colDat) <- colDat$sampleID
mae <- MultiAssayExperiment::MultiAssayExperiment(
    experiments = list(
        methylation = methylData,
        expression = expresData
    ),
    colData = colDat
)
geneRow <- 1
x1 <- 1 / 3
x2 <- 2 / 3
y1 <- NULL
y2 <- NULL
percY1 <- 1 / 3
percY2 <- 2 / 3

calcFreqs(
    mae = mae, geneNum = geneRow,
    x1, x2, y1, y2, percY1, percY2
)

checkPairing

Description

checkPairing function to check if two matrices have the same dimensions row and column names.

Usage

checkPairing(X, Y)
checkPairing(X, Y)

Arguments

X

matrix

Y

matrix

Value

a logical value indicating if the two matrices have the same row and column names.

Examples

(X <- round(matrix(rnorm(30) * 10, ncol = 6), 1)) + 1:10
(Y <- round(X + matrix(rnorm(30) * 10, ncol = 6), 1)) - 10:1
(rownames(X) <- rownames(Y) <- letters[1:nrow(X)])
(m1 <- checkPairing(X, Y))

(X <- round(matrix(rnorm(30) * 10, ncol = 6), 1)) + 1:10
(Y <- round(X + matrix(rnorm(30) * 10, ncol = 6), 1)) - 10:1
(rownames(X) <- rownames(Y) <- letters[1:nrow(X)])
(m1 <- checkPairing(X, Y))

correlationSelection: A vector correlation function calculator

Description

correlationSelection Uses the function matCorrs; given two matrices X (m,n), Y (m,n) this function computes Pearson and Spearman correlation coefficients and their significance p-values for every pair of row vectors.

Usage

correlationSelection(
  mae,
  type = "Spearman",
  adj = TRUE,
  pValCutoff = 0.05,
  rCutoff = 0,
  sortByCorrs = FALSE
)
correlationSelection(
  mae,
  type = "Spearman",
  adj = TRUE,
  pValCutoff = 0.05,
  rCutoff = 0,
  sortByCorrs = FALSE
)

Arguments

mae

a MultiAssayExperiment object containing the methylation

type

specifies the correlation to choose between Spearman and Pearson. Default is Spearman.

adj

logical variable indicating if the p-value returned should be adjusted or not. Default is TRUE, it returns an adjusted p-value.

pValCutoff

the upper limit used for the p-value. Default is 0.05.

rCutoff

the upper limit used for the correlation coefficient. Default is 0, no cut off.

sortByCorrs

logical; if TRUE, results are ordered by ascending p-value. Default set to FALSE.

Value

dataframe with the correlations selected

Examples


# Methylation data
methylData <- matrix(runif(50), nrow = 10)
colnames(methylData) <- paste0("samp", 1:ncol(methylData))
rownames(methylData) <- paste0("gene", 1:nrow(methylData))
# Expression data
expresData <- matrix(rnorm(50), nrow = 10)
colnames(expresData) <- paste0("samp", 1:ncol(methylData))
rownames(expresData) <- paste0("gene", 1:nrow(methylData))
# ColData
colDat <- data.frame(
    sampleID = colnames(methylData),
    name = letters[1:ncol(methylData)]
)

rownames(colDat) <- colDat$sampleID
mae <- MultiAssayExperiment::MultiAssayExperiment(
    experiments = list(
        methylation = methylData,
        expression = expresData
    ),
    colData = colDat
)
correlationSelection(mae, pValCutoff = 0.25, rCutoff = 0.1, 
type = "Spearman", sortByCorrs = TRUE)
# Methylation data
methylData <- matrix(runif(50), nrow = 10)
colnames(methylData) <- paste0("samp", 1:ncol(methylData))
rownames(methylData) <- paste0("gene", 1:nrow(methylData))
# Expression data
expresData <- matrix(rnorm(50), nrow = 10)
colnames(expresData) <- paste0("samp", 1:ncol(methylData))
rownames(expresData) <- paste0("gene", 1:nrow(methylData))
# ColData
colDat <- data.frame(
    sampleID = colnames(methylData),
    name = letters[1:ncol(methylData)]
)

rownames(colDat) <- colDat$sampleID
mae <- MultiAssayExperiment::MultiAssayExperiment(
    experiments = list(
        methylation = methylData,
        expression = expresData
    ),
    colData = colDat
)
correlationSelection(mae, pValCutoff = 0.25, rCutoff = 0.1, 
type = "Spearman", sortByCorrs = TRUE)

lhCreateMAE: Create a MultiAssayExperiment with Methylation and Expression Data

Description

This function constructs a MultiAssayExperiment object using methylation and expression data, automatically ensuring consistency in sample names, feature names, and exposing dataset names. It internally uses checkPairing to validate that the provided datasets have matching row and column names.

Usage

lhCreateMAE(
  xDat,
  yDat,
  xName = "methylation",
  yName = "expression",
  colData = NULL
)
lhCreateMAE(
  xDat,
  yDat,
  xName = "methylation",
  yName = "expression",
  colData = NULL
)

Arguments

xDat

A numeric matrix containing methylation data (features in rows, samples in columns).

yDat

A numeric matrix containing expression data (features in rows, samples in columns).

xName

A string specifying the name of the methylation dataset. Default: "methylation".

yName

A string specifying the name of the expression dataset. Default: "expression".

colData

(Optional) A DataFrame containing sample-level metadata. Default: NULL.

Value

A MultiAssayExperiment object containing the provided datasets with sample metadata.

Examples


library(MultiAssayExperiment)

# Create synthetic methylation and expression data
methylData <- matrix(runif(50), nrow = 10)
colnames(methylData) <- paste0("samp", 1:ncol(methylData))
rownames(methylData) <- paste0("gene", 1:nrow(methylData))

expresData <- matrix(rnorm(50), nrow = 10)
colnames(expresData) <- colnames(methylData)
rownames(expresData) <- rownames(methylData)

# Create sample metadata
colDat <- data.frame(sampleID = colnames(methylData),
name = letters[1:ncol(methylData)])
rownames(colDat) <- colDat$sampleID

# Construct MultiAssayExperiment
mae <- lhCreateMAE(methylData, expresData, colData = colDat)

# Display dataset names
names(experiments(mae))

library(MultiAssayExperiment)

# Create synthetic methylation and expression data
methylData <- matrix(runif(50), nrow = 10)
colnames(methylData) <- paste0("samp", 1:ncol(methylData))
rownames(methylData) <- paste0("gene", 1:nrow(methylData))

expresData <- matrix(rnorm(50), nrow = 10)
colnames(expresData) <- colnames(methylData)
rownames(expresData) <- rownames(methylData)

# Create sample metadata
colDat <- data.frame(sampleID = colnames(methylData),
name = letters[1:ncol(methylData)])
rownames(colDat) <- colDat$sampleID

# Construct MultiAssayExperiment
mae <- lhCreateMAE(methylData, expresData, colData = colDat)

# Display dataset names
names(experiments(mae))

A row-wise correlation function calculator

Description

matCorrs Given a MultiAssayExperiment object, the function computes Pearson and Spearman correlation coefficients and their significance p-values for every pair of row vectors.

Usage

matCorrs(mae)
matCorrs(mae)

Arguments

mae

A MultiAssayExperiment object containing the methylation and expression data.

Value

matrix with a correlation value for each gene

Examples

# Methylation data
methylData <- matrix(runif(50), nrow = 10)
colnames(methylData) <- paste0("samp", 1:ncol(methylData))
rownames(methylData) <- paste0("gene", 1:nrow(methylData))
# Expression data
expresData <- matrix(rnorm(50), nrow = 10)
colnames(expresData) <- paste0("samp", 1:ncol(methylData))
rownames(expresData) <- paste0("gene", 1:nrow(methylData))
# ColData
colDat <- data.frame(
    sampleID = colnames(methylData),
    name = letters[1:ncol(methylData)]
)

rownames(colDat) <- colDat$sampleID
mae <- MultiAssayExperiment::MultiAssayExperiment(
    experiments = list(
        methylation = methylData,
        expression = expresData
    ),
    colData = colDat
)
matCorrs(mae)

# Methylation data
methylData <- matrix(runif(50), nrow = 10)
colnames(methylData) <- paste0("samp", 1:ncol(methylData))
rownames(methylData) <- paste0("gene", 1:nrow(methylData))
# Expression data
expresData <- matrix(rnorm(50), nrow = 10)
colnames(expresData) <- paste0("samp", 1:ncol(methylData))
rownames(expresData) <- paste0("gene", 1:nrow(methylData))
# ColData
colDat <- data.frame(
    sampleID = colnames(methylData),
    name = letters[1:ncol(methylData)]
)

rownames(colDat) <- colDat$sampleID
mae <- MultiAssayExperiment::MultiAssayExperiment(
    experiments = list(
        methylation = methylData,
        expression = expresData
    ),
    colData = colDat
)
matCorrs(mae)

messageTitle

Description

messageTitle A wrapper function for sending messages to console.

Usage

messageTitle(aMessage, underChar = "-")
messageTitle(aMessage, underChar = "-")

Arguments

aMessage

text of the message.

underChar

character to use for underlining the message.

Value

a message to console

Examples

messageTitle("Hello world", "$")

messageTitle("Hello world", "$")

numScore

Description

numScore A function to score scatterplot using a weight matrix. The scoring does not incorporate logical conditions such as 'if xij < C ...'

Usage

numScore(aGrid, LShaped, aWeightMifL, aWeightMifNonL)
numScore(aGrid, LShaped, aWeightMifL, aWeightMifNonL)

Arguments

aGrid

A matrix of counts computed by the 'calcFreqs' function.

LShaped

A boolean indicating whether the scatterplot can be classified as "L-shaped".

aWeightMifL

A matrix of weights applied to score counts if the scatterplot is classified as "L".

aWeightMifNonL

A matrix of weights applied to score counts if the scatterplot is classified as "non-L".

Value

a numeric score for each scatterplot

Examples

# Methylation data
methylData <- matrix(runif(50), nrow = 10)
colnames(methylData) <- paste0("samp", 1:ncol(methylData))
rownames(methylData) <- paste0("gene", 1:nrow(methylData))
# Expression data
expresData <- matrix(rnorm(50), nrow = 10)
colnames(expresData) <- paste0("samp", 1:ncol(methylData))
rownames(expresData) <- paste0("gene", 1:nrow(methylData))
# ColData
colDat <- data.frame(
    sampleID = colnames(methylData),
    name = letters[1:ncol(methylData)]
)

rownames(colDat) <- colDat$sampleID
mae <- MultiAssayExperiment::MultiAssayExperiment(
    experiments = list(
        methylation = methylData,
        expression = expresData
    ),
    colData = colDat
)
trueFreq <- calcFreqs(mae, x1 = 1 / 3, x2 = 2 / 3)
LShaped <- FALSE
weightsIfL <- matrix(c(2, -1, -99, 1, 0, -1, 1, 1, 2),
    nrow = 3, byrow = TRUE
)
weightsIfNonL <- matrix(c(0, -1, -99, 0, 0, -1, 0, 0, 0),
    nrow = 3, byrow = TRUE
)
numScore(
    trueFreq, LShaped,
    weightsIfL, weightsIfNonL
)

# Methylation data
methylData <- matrix(runif(50), nrow = 10)
colnames(methylData) <- paste0("samp", 1:ncol(methylData))
rownames(methylData) <- paste0("gene", 1:nrow(methylData))
# Expression data
expresData <- matrix(rnorm(50), nrow = 10)
colnames(expresData) <- paste0("samp", 1:ncol(methylData))
rownames(expresData) <- paste0("gene", 1:nrow(methylData))
# ColData
colDat <- data.frame(
    sampleID = colnames(methylData),
    name = letters[1:ncol(methylData)]
)

rownames(colDat) <- colDat$sampleID
mae <- MultiAssayExperiment::MultiAssayExperiment(
    experiments = list(
        methylation = methylData,
        expression = expresData
    ),
    colData = colDat
)
trueFreq <- calcFreqs(mae, x1 = 1 / 3, x2 = 2 / 3)
LShaped <- FALSE
weightsIfL <- matrix(c(2, -1, -99, 1, 0, -1, 1, 1, 2),
    nrow = 3, byrow = TRUE
)
weightsIfNonL <- matrix(c(0, -1, -99, 0, 0, -1, 0, 0, 0),
    nrow = 3, byrow = TRUE
)
numScore(
    trueFreq, LShaped,
    weightsIfL, weightsIfNonL
)

plotGeneByName

Description

plotGeneByName plots points on a scatterplot with a 3x3 grid superimposed. The name of a the gene is provided jointly with the MultiAssayExperiment object and used to select the row to be plotted.

Usage

plotGeneByName(
  geneName,
  mae,
  filename = NULL,
  text4Title = NULL,
  plotGrid = TRUE,
  figs = c(2, 2)
)
plotGeneByName(
  geneName,
  mae,
  filename = NULL,
  text4Title = NULL,
  plotGrid = TRUE,
  figs = c(2, 2)
)

Arguments

geneName

The name of the gene to be plotted.

mae

A MultiAssayExperiment object containing the methylation and expression data for the specified gene.

filename

If provided, the name of the file to save the results as a PDF; defaults to NULL.

text4Title

A string used as the main title for the plot. Defaults to 'geneName' if set to NULL.

plotGrid

A boolean parameter indicating whether to pass the grid option to the 'plotGeneSel' function.

figs

A two-component vector defining the 2-dimensional structure of the plots to be generated.

Value

a pdf with scatterplots of selected by gene name

Examples

# Plot gene by name based on example data
# Methylation data
methylData <- matrix(runif(50), nrow = 10)
colnames(methylData) <- paste0("samp", 1:ncol(methylData))
rownames(methylData) <- paste0("gene", 1:nrow(methylData))
# Expression data
expresData <- matrix(rnorm(50), nrow = 10)
colnames(expresData) <- paste0("samp", 1:ncol(methylData))
rownames(expresData) <- paste0("gene", 1:nrow(methylData))
# ColData
colDat <- data.frame(
    sampleID = colnames(methylData),
    name = letters[1:ncol(methylData)]
)

rownames(colDat) <- colDat$sampleID
mae <- MultiAssayExperiment::MultiAssayExperiment(
    experiments = list(
        methylation = methylData,
        expression = expresData
    ),
    colData = colDat
)
plotGeneByName(gene = "gene7", mae = mae)

# Plot gene by name based on example data
# Methylation data
methylData <- matrix(runif(50), nrow = 10)
colnames(methylData) <- paste0("samp", 1:ncol(methylData))
rownames(methylData) <- paste0("gene", 1:nrow(methylData))
# Expression data
expresData <- matrix(rnorm(50), nrow = 10)
colnames(expresData) <- paste0("samp", 1:ncol(methylData))
rownames(expresData) <- paste0("gene", 1:nrow(methylData))
# ColData
colDat <- data.frame(
    sampleID = colnames(methylData),
    name = letters[1:ncol(methylData)]
)

rownames(colDat) <- colDat$sampleID
mae <- MultiAssayExperiment::MultiAssayExperiment(
    experiments = list(
        methylation = methylData,
        expression = expresData
    ),
    colData = colDat
)
plotGeneByName(gene = "gene7", mae = mae)

plotGeneSel

Description

plotGeneSel plots points on a scatterplot with a 3x3 grid overimposed.

Usage

plotGeneSel(
  mae,
  genePos,
  titleText,
  x1 = 1/3,
  x2 = 2/3,
  y1 = NULL,
  y2 = NULL,
  percY1 = 1/3,
  percY2 = 2/3,
  plotGrid = TRUE
)
plotGeneSel(
  mae,
  genePos,
  titleText,
  x1 = 1/3,
  x2 = 2/3,
  y1 = NULL,
  y2 = NULL,
  percY1 = 1/3,
  percY2 = 2/3,
  plotGrid = TRUE
)

Arguments

mae

A MultiAssayExperiment object containing the methylation and expression data for the specified gene.

genePos

The index of the gene to be plotted within the MultiAssayExperiment object.

titleText

plot title.

x1, x2

Coordinates of vertical points in the X axis. Because it is expected to contain methylation values that vary between 0 and 1. The default values are 1/3 and 2/3.

y1, y2

Coordinates of vertical points in the Y axis. Leaving them as NULL assigns them the percentiles of yVec defined by 'percY1' and 'percY2'.

percY1, percY2

Values used to act as default for 'y1'and 'y2' when these are set to 'NULL'

plotGrid

logical. Defautl to TRUE will plot gridlines over the scatterplot.

Value

a pdf with scatterplots for selected genes

Examples

# Methylation data
methylData <- matrix(runif(50), nrow = 10)
colnames(methylData) <- paste0("samp", 1:ncol(methylData))
rownames(methylData) <- paste0("gene", 1:nrow(methylData))
# Expression data
expresData <- matrix(rnorm(50), nrow = 10)
colnames(expresData) <- paste0("samp", 1:ncol(methylData))
rownames(expresData) <- paste0("gene", 1:nrow(methylData))
# ColData
colDat <- data.frame(
    sampleID = colnames(methylData),
    name = letters[1:ncol(methylData)]
)

rownames(colDat) <- colDat$sampleID
mae <- MultiAssayExperiment::MultiAssayExperiment(
    experiments = list(
        methylation = methylData,
        expression = expresData
    ),
    colData = colDat
)

plotGeneSel(mae, genePos = 7, titleText = "L-shaped gene")

# Methylation data
methylData <- matrix(runif(50), nrow = 10)
colnames(methylData) <- paste0("samp", 1:ncol(methylData))
rownames(methylData) <- paste0("gene", 1:nrow(methylData))
# Expression data
expresData <- matrix(rnorm(50), nrow = 10)
colnames(expresData) <- paste0("samp", 1:ncol(methylData))
rownames(expresData) <- paste0("gene", 1:nrow(methylData))
# ColData
colDat <- data.frame(
    sampleID = colnames(methylData),
    name = letters[1:ncol(methylData)]
)

rownames(colDat) <- colDat$sampleID
mae <- MultiAssayExperiment::MultiAssayExperiment(
    experiments = list(
        methylation = methylData,
        expression = expresData
    ),
    colData = colDat
)

plotGeneSel(mae, genePos = 7, titleText = "L-shaped gene")

plotGenesMat

Description

plotGenesMat wrapper function for plotting the scatterplots associated with two matrices.

Usage

plotGenesMat(
  mae,
  geneNames = NULL,
  fileName = "Scatplot_Sel_Genes.pdf",
  text4Title = NULL,
  x1 = 1/3,
  x2 = 2/3,
  y1 = NULL,
  y2 = NULL,
  percY1 = 1/3,
  percY2 = 2/3,
  plotGrid = TRUE,
  logicSc = NULL,
  saveToPDF = FALSE,
  plotsPerPage = 9
)
plotGenesMat(
  mae,
  geneNames = NULL,
  fileName = "Scatplot_Sel_Genes.pdf",
  text4Title = NULL,
  x1 = 1/3,
  x2 = 2/3,
  y1 = NULL,
  y2 = NULL,
  percY1 = 1/3,
  percY2 = 2/3,
  plotGrid = TRUE,
  logicSc = NULL,
  saveToPDF = FALSE,
  plotsPerPage = 9
)

Arguments

mae

A MultiAssayExperiment object containing the methylation and expression data.

geneNames

A character vector of gene names to plot. If NULL, all genes are plotted.

fileName

The name of the file used to save the results as a PDF. If NULL, the plot is displayed on the screen.

text4Title

An optional title for the plot, incorporating the gene name and L-shape score. Defaults to NULL.

x1

The x-coordinate of vertical points on the X-axis. Expected to contain methylation values ranging between 0 and 1, with default values set to 1/3 and 2/3.

x2

The x-coordinate of vertical points on the X-axis. Expected to contain methylation values ranging between 0 and 1, with default values set to 1/3 and 2/3.

y1

The y-coordinate of vertical points on the Y-axis. If NULL, these are set to the percentiles of yVec defined by percY1 and percY2.

y2

The y-coordinate of vertical points on the Y-axis. If NULL, these are set to the percentiles of yVec defined by percY1 and percY2.

percY1

Values used as defaults for y1 when it is set to NULL.

percY2

Values used as defaults for y2 when it is set to NULL.

plotGrid

A logical value; defaults to TRUE, indicating whether to plot gridlines on the graph.

logicSc

A numeric score representing the L-shape score. Defaults to NULL.

saveToPDF

Logical, if TRUE, saves the plots to a PDF file specified by fileName. If FALSE (default), plots are displayed interactively without saving.

plotsPerPage

A numeric value indicating the number of plots to be printed in each page. Default set to 9.

Value

a pdf with scatterplots for all genes

Examples

# Methylation data
methylData <- matrix(runif(50), nrow = 10)
colnames(methylData) <- paste0("samp", 1:ncol(methylData))
rownames(methylData) <- paste0("gene", 1:nrow(methylData))
# Expression data
expresData <- matrix(rnorm(50), nrow = 10)
colnames(expresData) <- paste0("samp", 1:ncol(methylData))
rownames(expresData) <- paste0("gene", 1:nrow(methylData))
# ColData
colDat <- data.frame(
sampleID = colnames(methylData),
name = letters[1:ncol(methylData)])

rownames(colDat) <- colDat$sampleID
mae <- MultiAssayExperiment::MultiAssayExperiment(
    experiments = list(
        methylation = methylData,
        expression = expresData
    ),
    colData = colDat
) 
selectedGenes <- c("gene1", "gene5")
plotGenesMat(mae, geneNames = selectedGenes,
saveToPDF = FALSE)

# Methylation data
methylData <- matrix(runif(50), nrow = 10)
colnames(methylData) <- paste0("samp", 1:ncol(methylData))
rownames(methylData) <- paste0("gene", 1:nrow(methylData))
# Expression data
expresData <- matrix(rnorm(50), nrow = 10)
colnames(expresData) <- paste0("samp", 1:ncol(methylData))
rownames(expresData) <- paste0("gene", 1:nrow(methylData))
# ColData
colDat <- data.frame(
sampleID = colnames(methylData),
name = letters[1:ncol(methylData)])

rownames(colDat) <- colDat$sampleID
mae <- MultiAssayExperiment::MultiAssayExperiment(
    experiments = list(
        methylation = methylData,
        expression = expresData
    ),
    colData = colDat
) 
selectedGenes <- c("gene1", "gene5")
plotGenesMat(mae, geneNames = selectedGenes,
saveToPDF = FALSE)

scoreGenesMat

Description

scoreGenesMat scores scatterplots using a binary and a numeric schemes row-wise.

Usage

scoreGenesMat(
  mae,
  x1 = 1/3,
  x2 = 2/3,
  y1 = NULL,
  y2 = NULL,
  percY1 = 1/3,
  percY2 = 2/3,
  aReqPercentsMat,
  aWeightMifL = 0.5,
  aWeightMifNonL = 0.25
)
scoreGenesMat(
  mae,
  x1 = 1/3,
  x2 = 2/3,
  y1 = NULL,
  y2 = NULL,
  percY1 = 1/3,
  percY2 = 2/3,
  aReqPercentsMat,
  aWeightMifL = 0.5,
  aWeightMifNonL = 0.25
)

Arguments

mae

MultiAssayExperiment object containing methylation and expression matrices.

x1, x2

Coordinates of vertical points on the X-axis. Expected to contain methylation values ranging between 0 and 1, with default values set to 1/3 and 2/3.

y1, y2

Coordinates of vertical points on the Y-axis. If set to NULL, they default to the percentiles of yVec as defined by 'percY1' and 'percY2'.

percY1, percY2

Values used to act as default for 'y1'and 'y2' when these are set to 'NULL'.

aReqPercentsMat

A matrix specifying the minimum and maximum percentage counts required in each cell.

aWeightMifL

A matrix of weights applied to score counts when the scatterplot is classified as "L".

aWeightMifNonL

A matrix of weights applied to score counts when the scatterplot is classified as "non-L".

Value

A data frame with two columns: 'logicSc' (logical score indicating if the gene is 'active') and 'numericSc' (a numerical score).

Examples

# Score genes based on example data

# Methylation data
methylData <- matrix(runif(50), nrow = 10)
colnames(methylData) <- paste0("samp", 1:ncol(methylData))
rownames(methylData) <- paste0("gene", 1:nrow(methylData))
# Expression data
expresData <- matrix(rnorm(50), nrow = 10)
colnames(expresData) <- paste0("samp", 1:ncol(methylData))
rownames(expresData) <- paste0("gene", 1:nrow(methylData))
# ColData
colDat <- data.frame(
    sampleID = colnames(methylData),
    name = letters[1:ncol(methylData)]
)

rownames(colDat) <- colDat$sampleID
mae <- MultiAssayExperiment::MultiAssayExperiment(
    experiments = list(
        methylation = methylData,
        expression = expresData
    ),
    colData = colDat
)
sampleSize <- ncol(MultiAssayExperiment::experiments(mae)[[1]])
reqPercentages <- matrix(c(3, 20, 5, 5, 40, 20, 4, 1, 2),
    nrow = 3, byrow = TRUE
)
(theWeightMifL <- matrix(c(2, -2, -sampleSize / 5, 1, 0, -2, 1, 1, 2),
    nrow = 3, byrow = TRUE
))
(theWeightMifNonL <- matrix(c(0, -2, -sampleSize / 5, 0, 0, -2, 0, 0, 0),
    nrow = 3, byrow = TRUE
))
scoreGenesMat(mae,
    x1 = 1 / 3, x2 = 2 / 3,
    y1 = NULL, y2 = NULL, percY1 = 1 / 3, percY2 = 2 / 3,
    aReqPercentsMat = reqPercentages,
    aWeightMifL = theWeightMifL,
    aWeightMifNonL = theWeightMifNonL
)
# Score genes based on example data

# Methylation data
methylData <- matrix(runif(50), nrow = 10)
colnames(methylData) <- paste0("samp", 1:ncol(methylData))
rownames(methylData) <- paste0("gene", 1:nrow(methylData))
# Expression data
expresData <- matrix(rnorm(50), nrow = 10)
colnames(expresData) <- paste0("samp", 1:ncol(methylData))
rownames(expresData) <- paste0("gene", 1:nrow(methylData))
# ColData
colDat <- data.frame(
    sampleID = colnames(methylData),
    name = letters[1:ncol(methylData)]
)

rownames(colDat) <- colDat$sampleID
mae <- MultiAssayExperiment::MultiAssayExperiment(
    experiments = list(
        methylation = methylData,
        expression = expresData
    ),
    colData = colDat
)
sampleSize <- ncol(MultiAssayExperiment::experiments(mae)[[1]])
reqPercentages <- matrix(c(3, 20, 5, 5, 40, 20, 4, 1, 2),
    nrow = 3, byrow = TRUE
)
(theWeightMifL <- matrix(c(2, -2, -sampleSize / 5, 1, 0, -2, 1, 1, 2),
    nrow = 3, byrow = TRUE
))
(theWeightMifNonL <- matrix(c(0, -2, -sampleSize / 5, 0, 0, -2, 0, 0, 0),
    nrow = 3, byrow = TRUE
))
scoreGenesMat(mae,
    x1 = 1 / 3, x2 = 2 / 3,
    y1 = NULL, y2 = NULL, percY1 = 1 / 3, percY2 = 2 / 3,
    aReqPercentsMat = reqPercentages,
    aWeightMifL = theWeightMifL,
    aWeightMifNonL = theWeightMifNonL
)

Expression data matrix obtained from TCGA, TCGA-COAD dataset

Description

A mxn matrix containing normalized gene expressions from TCGA

Usage

data(TCGAexpression)
data(TCGAexpression)

Format

An object of class matrix (inherits from array) with 1000 rows and 30 columns.

Value

data matrix with gene expression data

Source

TCGA.org

Methylation data matrix obtained from TCGA, TCGA-COAD dataset

Description

A mxn matrix containing normalized methylation values from TCGA

Usage

data(TCGAmethylation)
data(TCGAmethylation)

Format

An object of class matrix (inherits from array) with 1000 rows and 30 columns.

Value

data matrix with gene methylation data

Source

TCGA.org

toReqMat

Description

toReqMat can be used to turn a matrix of required percentages into a matrix of required counts to facilitate its scoring.

Usage

toReqMat(numPoints, aReqPercentMat)
toReqMat(numPoints, aReqPercentMat)

Arguments

numPoints

Number of points in a scatterplot. Used to turn the required percentages into required counts.

aReqPercentMat

Matrix of required percentages.

Value

a counts matrix

Examples

reqPercentages <- matrix(
    c(
        3, 20, 5,
        5, 40, 20,
        4, 1, 2
    ),
    nrow = 3, byrow = TRUE
)
numberOfPoints <- 100
reqMat <- toReqMat(
    numPoints = numberOfPoints,
    aReqPercentMat = reqPercentages
)

reqPercentages <- matrix(
    c(
        3, 20, 5,
        5, 40, 20,
        4, 1, 2
    ),
    nrow = 3, byrow = TRUE
)
numberOfPoints <- 100
reqMat <- toReqMat(
    numPoints = numberOfPoints,
    aReqPercentMat = reqPercentages
)

Package 'Lheuristic'

Help Index

binScore

Description

Usage

Arguments

Value

Examples

calcFreqs

Description

Usage

Arguments

Value

Examples

checkPairing

Description

Usage

Arguments

Value

Examples

correlationSelection: A vector correlation function calculator

Description

Usage

Arguments

Value

Examples

lhCreateMAE: Create a MultiAssayExperiment with Methylation and Expression Data

Description

Usage

Arguments

Value

Examples

A row-wise correlation function calculator

Description

Usage

Arguments

Value

Examples

messageTitle

Description

Usage

Arguments

Value

Examples

numScore

Description

Usage

Arguments

Value

Examples

plotGeneByName

Description

Usage

Arguments

Value

Examples

plotGeneSel

Description

Usage

Arguments

Value

Examples

plotGenesMat

Description

Usage

Arguments

Value

Examples

scoreGenesMat

Description

Usage

Arguments

Value

Examples

Expression data matrix obtained from TCGA, TCGA-COAD dataset

Description

Usage

Format

Value

Source