runAnalysis {CeTF} | R Documentation

## Whole analysis of Regulatory Impact Factors (RIF) and Partial Correlation and Information Theory analysis (PCIT)

### Description

This function uses RIF and PCIT algorithms to run the whole pipeline analysis. The pipeline is composed by 4 steps:

1. Step 1: Data adjustment;

2. Step 2: Differential expression analysis;

3. Step 3: Regulatory Impact Factors analysis;

4. Step 4: Partial Correlation and Information Theory analysis.

### Usage

    runAnalysis(
      mat,
      conditions = NULL,
      lfc = 2.57,
      padj = 0.05,
      TFs = NULL,
      nSamples1 = NULL,
      nSamples2 = NULL,
      tolType = "mean",
      diffMethod = "Reverter",
      data.type = NULL
    )

### Arguments

`mat` | Count data where the rows are genes and coluns the samples (conditions).
`conditions` | A vector of characters identifying the names of conditions (i.e. c('normal', 'tumor')).
`lfc` | logFoldChange module threshold to define a gene as differentially expressed (default: 2.57).
`padj` | Significance value to define a gene as differentially expressed (default: 0.05).
`TFs` | A vector of character with all transcripts factors of specific organism.
`nSamples1` | Number of samples that correspond to first condition.
`nSamples2` | Number of samples that correspond to second condition.
`tolType` | Tolerance calculation type (see `tolerance`) (default: 'mean').
`diffMethod` | Method to calculate Differentially Expressed (DE) genes (see `expDiff`) (default: 'Reverter')
`data.type` | Type of input data. If is expression (FPKM, TPM, etc) or counts.

### Value

Returns an CeTF class object with output variables of each step of analysis.

### See Also

`CeTF-class`

### Examples

    data('simCounts')
    out <- runAnalysis(mat = simCounts,
                       conditions=c('cond1', 'cond2'),
                       lfc = 3,
                       padj = 0.05,
                       TFs = paste0('TF_', 1:1000),
                       nSamples1 = 10,
                       nSamples2= 10,
                       tolType = 'mean',
                       diffMethod = 'Reverter',
                       data.type = 'counts')

[Package CeTF version 1.2.4 Index]