lmQCM: lmQCM: Main Routine for Gene Co-expression Analysis

In huangzhii/lmQCM: An Algorithm for Gene Co-Expression Analysis

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)

huangzhii/lmQCM documentation built on Oct. 14, 2022, 2:07 a.m.