Description Usage Arguments Value See Also Examples
This function uses RIF and PCIT algorithms to run the whole pipeline analysis. The pipeline is composed by 4 steps:
Step 1: Data adjustment;
Step 2: Differential expression analysis;
Step 3: Regulatory Impact Factors analysis;
Step 4: Partial Correlation and Information Theory analysis.
1 2 3 4 5 6 7 8 9 10 11 12 | runAnalysis(
mat,
conditions = NULL,
lfc = 2.57,
padj = 0.05,
TFs = NULL,
nSamples1 = NULL,
nSamples2 = NULL,
tolType = "mean",
diffMethod = "Reverter",
data.type = NULL
)
|
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 |
diffMethod |
Method to calculate Differentially Expressed (DE) genes (see |
data.type |
Type of input data. If is expression (FPKM, TPM, etc) or counts. |
Returns an CeTF class object with output variables of each step of analysis.
1 2 3 4 5 6 7 8 9 10 11 | 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')
|
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