QP_SigPvalue: Computes the p-value for a statistically significant score.

In QuaternaryProd: Computes the Quaternary Dot Product Scoring Statistic for Signed and Unsigned Causal Graphs

Description

This function computes the right sided p-value for the Quaternary Dot Product Scoring Statistic for statistically significant scores.

Usage

QP_SigPvalue(score, q_p, q_m, q_z, q_r, n_p, n_m, n_z, epsilon = 1e-16, sig_level = 0.05)

Arguments

score	The score for which the p-value will be computed.
q_p	Expected number of positive predictions.
q_m	Expected number of negative predictions.
q_z	Expected number of nil predictions.
q_r	Expected number of regulated predictions.
n_p	Number of positive predictions from experiments.
n_m	Number of negative predictions from experiments.
n_z	Number of nil predictions from experiments.
epsilon	Threshold for probabilities of matrices. Default value is 1e-16.
sig_level	Significance level of test hypothesis. Default value is 0.05.

Details

Setting epsilon to zero will compute the probability mass function without ignoring any matrices with probabilities smaller than epsilon*D_max (D_max is the numerator associated with the matrix of highest probability for the given constraints). The default value of 1e-16 is experimentally validated to be a very reasonable threshold. Setting the threshold to higher values which are smaller than 1 will lead to understimating the probabilities of each score since more tables will be ignored. If the score is not statistically significant, then a value of -1 will be returned.

Value

This function returns a numerical value, where the numerical value is the p-value of a score if the score is statistically significant otherwise it returns -1.

Author(s)

Carl Tony Fakhry, Ping Chen and Kourosh Zarringhalam

References

Carl Tony Fakhry, Parul Choudhary, Alex Gutteridge, Ben Sidders, Ping Chen, Daniel Ziemek, and Kourosh Zarringhalam. Interpreting transcriptional changes using causal graphs: new methods and their practical utility on public networks. BMC Bioinformatics, 17:318, 2016. ISSN 1471-2105. doi: 10.1186/s12859-016-1181-8.

Franceschini, A (2013). STRING v9.1: protein-protein interaction networks, with increased coverage and integration. In:'Nucleic Acids Res. 2013 Jan;41(Database issue):D808-15. doi: 10.1093/nar/gks1094. Epub 2012 Nov 29'.

See Also

QP_Pvalue

Examples

# Computing The p-value of score 50 
# for the given table margins. 
pval <- QP_SigPvalue(50,50,50,50,0,50,50,50)

QuaternaryProd documentation built on Nov. 8, 2020, 8:23 p.m.