| Title: | Identifying Master Gene Regulators from Gene Expression and DNA-Binding Data |
|---|---|
| Description: | The method 'm:Explorer' associates a given list of target genes (e.g. those involved in a biological process) to gene regulators such as transcription factors. Transcription factors that bind DNA near significantly many target genes or correlate with target genes in transcriptional (microarray or RNAseq data) are selected. Selection of candidate master regulators is carried out using multinomial regression models, likelihood ratio tests and multiple testing correction. Reference: m:Explorer: multinomial regression models reveal positive and negative regulators of longevity in yeast quiescence. Juri Reimand, Anu Aun, Jaak Vilo, Juan M Vaquerizas, Juhan Sedman and Nicholas M Luscombe. Genome Biology (2012) 13:R55 <doi:10.1186/gb-2012-13-6-r55>. |
| Authors: | Juri Reimand [aut, cre] |
| Maintainer: | Juri Reimand <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.0.0 |
| Built: | 2024-10-31 02:48:02 UTC |
| Source: | https://github.com/cran/mExplorer |
Selection of process-specific regulators from high-throughput data using multinomial regression models.
mExplorer(dframe, response, interactions = F, significance = 0.05, n_cores = 1, multitest = "BY")mExplorer(dframe, response, interactions = F, significance = 0.05, n_cores = 1, multitest = "BY")
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. |
Vector of scores, with names corresponding to predictors.
Juri Reimand <[email protected]>
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
data(yeastCCgenes) data(yeastTFdata) mExplorer(yeastTFdata, yeastCCgenes) data(mExplorer_small_test_data) small_test_results = mExplorer(small_test_dframe, small_test_response_vec)data(yeastCCgenes) data(yeastTFdata) mExplorer(yeastTFdata, yeastCCgenes) data(mExplorer_small_test_data) small_test_results = mExplorer(small_test_dframe, small_test_response_vec)
Creation of m:Explorer input data frame from GMT files
prepare_gmt_input(gmt_filename, min_genes = NA, max_genes = NA)prepare_gmt_input(gmt_filename, min_genes = NA, max_genes = NA)
gmt_filename |
Path to GMT file to convert. |
min_genes |
Numeric indicating to discard pathways with less than min_genes genes. If NA, there is no lower bound on the number of genes. Default is NA. |
max_genes |
Numeric indicating to discard pathways with more than max_genes genes. If NA, there is no upper bound on the number of genes. Default is NA. |
Data frame with pathways as columns, genes as rows. Gene/pathway combinations are marked with "pw" if that gene is in the pathway, or "." if not.
# Create m:Explorer input data frame from GMT "small_gmt.gmt," discarding # pathways with less than 5 genes and more than 1000 genes gmt_file = system.file("extdata", "small_gmt.gmt", package = "mExplorer") gmt = prepare_gmt_input(gmt_file, 5, 1000)# Create m:Explorer input data frame from GMT "small_gmt.gmt," discarding # pathways with less than 5 genes and more than 1000 genes gmt_file = system.file("extdata", "small_gmt.gmt", package = "mExplorer") gmt = prepare_gmt_input(gmt_file, 5, 1000)
Small sample of predictor data for testing m:Explorer
data(mExplorer_small_test_data)data(mExplorer_small_test_data)
A data frame with 10 observations of 18 variables
Small vector of yeast transcription factors for testing m:Explorer
data(mExplorer_small_test_data)data(mExplorer_small_test_data)
A named character vector with 4 elements
Example vector of yeast transcription factors for m:Explorer
data(yeastCCgenes)data(yeastCCgenes)
A named character vector with 186 elements
Example predictor data for m:Explorer
data(yeastTFdata)data(yeastTFdata)
A data frame with 6253 observations of 18 variables