probe_ranking

String indicating the name of the text file containing
the gene expression matrix.

input

Positive integer indicating the number of gene probes
to be selected as determined by the number_probes function.

probe_number

String indicating the way in which number of
probes were selected for. Options include "Fixed_Probe_Num",
"Percent_Probe_Num", and "Adaptive_Probe_Num".

probe_num_selection

The object containing the original gene expression matrix.
This matrix is outputted by the input_file function.

data.exp

A string indicating the gene probe ranking method to use.
Possible options include "CV_Rank", "CV_Guided", "SD_Rank", and "Poly".
The default is set to "SD_Rank".

method

Function to select for genes using one of the available gene
probe ranking options.

Whole transcriptomic profiles are useful for studying the
    expression levels of thousands of genes across samples. Clustering
    algorithms are used to identify patterns in these profiles to determine
    clinically relevant subgroups. Feature selection is a critical integral
    part of the process. Currently, there are many feature selection and
    clustering methods to identify the relevant genes and perform clustering
    of samples. However, choosing the appropriate methods is difficult as
    recent work demonstrates that no method is the clear winner. Hence, we
    present an R-package called `multiClust` that allows researchers to
    experiment with the choice of combination of methods for gene selection
    and clustering with ease. In addition, using multiClust, we present the
    merit of gene selection and clustering methods in the context of clinical
    relevance of clustering, specifically clinical outcome. Our integrative R-
    package contains: 1. A function to read in gene expression data and
    format appropriately for analysis in R. 2. Four different ways to select
    the number of genes a. Fixed b. Percent c. Poly d. GMM 3. Four gene
    ranking options that order genes based on different statistical criteria
    a. CV_Rank b. CV_Guided c. SD_Rank d. Poly 4. Two ways to determine the
    cluster number a. Fixed b. Gap Statistic 5. Two clustering algorithms
    a. Hierarchical clustering b. K-means clustering 6. A function to
    calculate average gene expression in each sample cluster 7. A function
    to correlate sample clusters with clinical outcome Order of Function
    use: 1. input_file, a function to read-in the gene expression file and
    assign gene probe names as the rownames. 2. number_probes, a function to
    determine the number of probes to select for in the gene feature selection
    process. 3. probe_ranking, a function to select for gene probes using one
    of the available gene probe ranking options. 4. number_clusters, a
    function to determine the number of clusters to be used to cluster genes
    and samples. 5. cluster_analysis, a function to perform Kmeans or
    Hierarchical clustering analysis of the selected gene expression data.
    6. avg_probe_exp, a function to produce a matrix containing the average
    expression of each gene probe within each sample cluster.
    7. surv_analysis, a function to produce Kaplan-Meier Survival Plots
    of selected gene expression data.

probe_ranking: Function to select for genes using one of the available gene probe ranking options.

Description

Usage

Arguments

Value

See Also

Examples