Package 'lmQCM'

Title: An Algorithm for Gene Co-Expression Analysis
Description: Implementation based on Zhang, Jie & Huang, Kun (2014) <doi:10.4137/CIN.S14021> Normalized ImQCM: An Algorithm for Detecting Weak Quasi-Cliques in Weighted Graph with Applications in Gene Co-Expression Module Discovery in Cancers. Cancer informatics, 13, CIN-S14021.
Authors: Zhi Huang [aut, cre], Jie Zhang [aut, ctb], Kun Huang [aut, ctb], Zhi Han [aut, ctb]
Maintainer: Zhi Huang <[email protected]>
License: MIT + file LICENSE
Version: 0.2.4
Built: 2025-02-25 04:32:15 UTC
Source: https://github.com/huangzhii/lmqcm

Help Index

fastFilter: Subroutine for filtering expression matrix

Description

Author: Zhi Huang

Usage

fastFilter(
  RNA,
  lowest_percentile_mean = 0.2,
  lowest_percentile_variance = 0.2,
  var.func = "var"
)

Arguments

RNA

an expression matrix (rows: genes; columns: samples)
lowest_percentile_mean

a float value range 0-1
lowest_percentile_variance

a float value range 0-1
var.func

specify variance function

Value

An filtered expression matrix

lmQCM: Main Routine for Gene Co-expression Analysis

Description

Author: Zhi Huang

Usage

lmQCM(
  data_in,
  gamma = 0.55,
  t = 1,
  lambda = 1,
  beta = 0.4,
  minClusterSize = 10,
  CCmethod = "pearson",
  positiveCorrelation = F,
  normalization = F
)

Arguments

data_in

real-valued expression matrix with rownames indicating gene ID or gene symbol
gamma

gamma value (default = 0.55)
t

t value (default = 1)
lambda

lambda value (default = 1)
beta

beta value (default = 0.4)
minClusterSize

minimum length of cluster to retain (default = 10)
CCmethod

Methods for correlation coefficient calculation (default = "pearson"). Users can also pick "spearman".
positiveCorrelation

This determines if correlation matrix should convert to positive (with abs function) or not.
normalization

Determine if normalization is needed on massive correlation coefficient matrix.

Value

QCMObject - An S4 Class with lmQCM results

Examples

library(lmQCM)
library(Biobase)
data(sample.ExpressionSet)
data = assayData(sample.ExpressionSet)$exprs
data = fastFilter(data, 0.2, 0.2)
lmQCM(data)

localMaximumQCM: Subroutine for Creating Gene Clusters

Description

Author: Zhi Huang

Usage

localMaximumQCM(cMatrix, gamma = 0.55, t = 1, lambda = 1)

Arguments

cMatrix

a correlation matirx
gamma

gamma value (default = 0.55)
t

t value (default = 1)
lambda

lambda value (default = 1)

Value

An unmerged clusters group 'C'

merging_lmQCM: Subroutine for Merging Gene Clusters

Description

Author: Zhi Huang

Usage

merging_lmQCM(C, beta = 0.4, minClusterSize = 10)

Arguments

C

Resulting clusters
beta

beta value (default = 0.4)
minClusterSize

minimum length of cluster to retain (default = 10)

Value

mergedCluster - An merged clusters group