geneExpression


        require(GCModeller);
                               

#' the gene expression matrix data toolkit
imports "geneExpression" from "phenotype_kit";

.NET clr function exports
exp
tr	do matrix transpose
dims	get summary information about the HTS matrix dimensions
as.expr_list	convert the matrix into row gene list
setTag	set a new tag string to the matrix
setZero	set the expression value to zero if the expression value is less than a given threshold
setSamples	set new sample id list to the matrix columns
setFeatures	set new gene id list to the matrix rows
filterZeroSamples	filter out all samples columns which its expression vector is ZERO!
filterZeroGenes	removes the rows which all gene expression result is ZERO
filterNaNMissing	set the NaN missing value to default value
impute_missing
load.expr	load an expressin matrix data
load.expr0	read the binary matrix data file
load.matrixView	Load the HTS matrix into a lazy matrix viewer
matrix_info	get matrix summary information
write.expr_matrix	write the gene expression data matrix file
filter	Filter the geneID rows
as.generic	cast the HTS matrix object to the general dataset
average	calculate average value of the gene expression for each sample group. this method can be apply for reduce data size when create some plot for visualize the gene expression patterns across the sample groups.
z_score	Z-score normalized of the expression data matrix To avoid the influence of expression level to the clustering analysis, z-score transformation can be applied to covert the expression values to z-scores by performing the following formula: `z = (x - u) / sd` x is value to be converted (e.g., a expression value of a genomic feature in one condition), µ is the population mean (e.g., average expression value Of a genomic feature In different conditions), σ Is the standard deviation (e.g., standard deviation of expression of a genomic feature in different conditions).
pca	do PCA on a gene expressin matrix
totalSumNorm	normalize data by sample column
relative	normalize data by feature rows
expression.cmeans_pattern	This function performs clustering analysis of time course data. Calculate gene expression pattern by cmeans algorithm.
expression.cmeans3D	run cmeans clustering in 3 patterns
savePattern	save the cmeans expression pattern result to local file
readPattern	read the cmeans expression pattern result from file
cmeans_matrix	get cluster membership matrix
pattern_representatives	get the top n representatives genes in each expression pattern
split.cmeans_clusters	split the cmeans cluster output split the cmeans cluster output into multiple parts based on the cluster tags
peakCMeans	clustering analysis of time course data This function performs clustering analysis of time course data
expr_ranking
deg.t.test	do t-test across specific analysis comparision
log	log scale of the HTS raw matrix
geneId	get gene Id list
as.deg	create gene expression DEG model
deg.class
joinSample	do matrix join by samples
aggregate	merge row or column where the tag is identical
add_gauss	add random gauss noise to the matrix

geneExpression

split the cmeans cluster output

clustering analysis of time course data