deg.specific: Predict the patient-specific DEGs using bi-clustering...

Description Usage Arguments Details Value Author(s) Examples

View source: R/deg.specific.R

Description

This function is use the output of bi.deg as input to predict the differentially expressed genes (DEGs) for each patient by cross-validation of multiple patients.

Usage

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deg.specific(deg, test.patients = NULL, min.genes = 50, min.patients = 5,
  overlap = 0.85, cores = 1)

Arguments

deg

a 'deg' object. This should be the output of bi.deg

test.patients

the patients to test. Only the patients in 'test.patients' are used as seed for bi-clustering analysis

min.genes

the minimum number of genes

min.patients

the minimum number of patients. It includes the patients as seeds (see details).

overlap

the minimum similarity for selected DEGs from two or more patients

cores

the thread number

Details

The DEGs from bi.deg are mixed with noises, e.g. the DEGs not associated with disease. This is especially true when the differential expression analysis tests are done using one variable again references. The assumption behind his analysis is that the disease associated DEGs will be observed in other patients. This function implements a bi-clustering algorithm to find the DEGs shared by 'min.patients' in the binary DEG matrix. In this process, each patient is used as seed and its DEGs are gradually excluded to find if there is a DEG list which is observed in 'min.patients' when the similarity is greater is 'overlap'.

'test.patients' option is used to find the cross-validated DEGs for some interest patients. Otherwise, all the patients will be used as seeds. 'overlap' is the threshold to determine the minimum similarity between neighbor and seed patients.

Value

A 'deg.specific' or 'deg.specific.test' object. It has a key of "decd.input", which stores the binary DEG matrix, genes, patients and used parameter setting. Other keys are the patients IDs and they store the cross-validated DEGs.

Author(s)

Guofeng Meng

Examples

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## Not run: 
# the DEGs has at least 100 genes and validated by 5 other patients
res.deg <- deg.specific(deg, min.genes=100, min.patients=5, overlap=0.85)

## End(Not run)

menggf/decd documentation built on Jan. 2, 2020, 12:53 a.m.