PCIT.RdThe PCIT algorithm is used for reconstruction of gene co-expression networks (GCN) that combines the concept partial correlation coefficient with information theory to identify significant gene to gene associations defining edges in the reconstruction of GCN.
PCIT(input, tolType = "mean")
| input | A correlation matrix. |
|---|---|
| tolType | Type of tolerance (default: 'mean') given the 3 pairwise correlations
(see |
Returns an list with the significant correlations, raw adjacency matrix and significant adjacency matrix.
REVERTER, Antonio; CHAN, Eva KF. Combining partial correlation and an information theory approach to the reversed engineering of gene co-expression networks. Bioinformatics, v. 24, n. 21, p. 2491-2497, 2008. https://academic.oup.com/bioinformatics/article/24/21/2491/192682
# loading a simulated normalized data data('simNorm') # getting the PCIT results for first 30 genes results <- PCIT(simNorm[1:30, ]) # printing PCIT output first 15 rows head(results$tab, 15)#> gene1 gene2 corr1 corr2 #> 1 gene10495 gene10853 0.45665 0.00000 #> 2 gene10495 gene10954 -0.26362 0.00000 #> 3 gene10495 gene113 -0.00966 0.00000 #> 4 gene10495 gene11335 0.48821 0.00000 #> 5 gene10495 gene11474 -0.45507 0.00000 #> 6 gene10495 gene11529 0.24149 0.00000 #> 7 gene10495 gene117 -0.70822 -0.70822 #> 8 gene10495 gene11986 0.23068 0.00000 #> 9 gene10495 gene12969 0.32270 0.00000 #> 10 gene10495 gene13446 -0.04105 0.00000 #> 11 gene10495 gene13600 -0.32514 0.00000 #> 12 gene10495 gene13775 0.44644 0.44644 #> 13 gene10495 gene13933 0.13616 0.00000 #> 14 gene10495 gene14118 -0.52313 -0.52313 #> 15 gene10495 gene14739 0.28042 0.00000