mExplorer: Selection of process-specific regulators from high-throughput...

In mExplorer: Identifying Master Gene Regulators from Gene Expression and DNA-Binding Data

Description

Selection of process-specific regulators from high-throughput data using multinomial regression models.

Usage

mExplorer(dframe, response, interactions = F, significance = 0.05,
  n_cores = 1, multitest = "BY")

Arguments

dframe	Data frame of predictors. Row and column names are required for identifying samples (genes) and predictors (gene regulators), respectively.
response	Vector of factors. Names of vector need to correspond to rownames in dframe.
interactions	If enabled, pairs of predictors as interactions will be evaluated (much slower).
significance	Significance cutoff for p-values from log likelihood ratio tests.
n_cores	Number of processor cores to engage in computation. Use all available cores by default (n_cores=0).
multitest	Method to perform multiple testing correction for p-values from predictor evaluation. See p.adjust() for details.

Value

Vector of scores, with names corresponding to predictors.

Author(s)

Juri Reimand <[e-mail]>

References

m:Explorer - multinomial regression models reveal positive and negative regulators of longevity in yeast quiescence (2012, Genome Biology) by Juri Reimand, Anu Aun, Jaak Vilo, Juan M. Vaquerizas, Juhan Sedman, and Nicholas M. Luscombe

Examples

data(yeastCCgenes)
data(yeastTFdata)
mExplorer(yeastTFdata, yeastCCgenes)

data(mExplorer_small_test_data)
small_test_results = mExplorer(small_test_dframe, small_test_response_vec)

mExplorer documentation built on May 1, 2019, 9:54 p.m.