geneExpression


        require(GCModeller);
                               

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

.NET clr function exports
as.data.frame.impactresult	ImpactResult:
as.data.frame.limmatable	LimmaTable: limma `topTable` output dataframe.
as.data.frame.degmodel	DEGModel: A generic model for different expression molecule
exp	power of the expression value in the matrix
tr	do matrix transpose
dims	get summary information about the HTS matrix dimensions
as.expr_list	convert the matrix into row gene list
expression_vector	get gene expression vector data
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
sample_id	get/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	set the zero value to the half of the min positive value
is_empty	check that the given expression matrix object is empty or not
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
project	make matrix samples column projection
filter	Filter the geneID rows
as.generic	cast the HTS matrix object to the general dataset
mad	evaluate the MAD value for each gene features
sort_mad	take top n expression feature by rank expression MAD value desc
aggregate_samples	calculate the sum 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.
aggregate_genes	merge the duplicated gene feature rows via the sum value
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
time_pattern_label	Make time pattern label for a specific cmeans expression pattern
peakCMeans	clustering analysis of time course data This function performs clustering analysis of time course data
expr_ranking	make the abundance ranking of the gene features in each sample group
deg.t.test	do t-test across specific analysis comparision
limma	The limma algorithm (Linear Models for Microarray Data) is a widely used statistical framework in R/Bioconductor for differential expression (DE) analysis of RNA-seq data. Originally designed for microarray studies, its flexibility and robustness have extended its utility to RNA-seq through the voomtransformation.
read_limma	read the limma result table from a given csv table file
limma_impactsort	make the impact sort of the limma differential expression analysis result
limma_table	build limma table model from the dataframe columns
log	log scale of the HTS raw matrix
minmax01Norm	min max normalization (row - min(row)) / (max(row) - min(row)) this normalization method is usually used for the metabolomics data
take_shuffle	random takes a subset of the gene features from the expression matrix
geneId	get gene Id list or byref set of the gene id alias set.
as.deg	create gene expression DEG model
deg.class	set deg class label
joinSample	do matrix join by samples
joinFeatures	merge multiple gene expression matrix by gene features
aggregate	merge row or column where the tag is identical
sample_auc	Calculate the sum of the sample data with time-series information across all time points to obtain the area under the curve (AUC) of the time-series curve.
add_gauss	add random gauss noise to the matrix
as.abundance_matrix	create the abundance matrix from a collection of the metagenomics abundance data

geneExpression

split the cmeans cluster output

clustering analysis of time course data