{geneExpression} R# Documentation

geneExpression


require(GCModeller);

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

the gene expression matrix data toolkit



.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

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


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