R: This function runs a causal relation engine by computing the...

RunCRE_HSAStringDB {QuaternaryProd}

R Documentation

This function runs a causal relation engine by computing the Quaternary Dot Product Scoring Statistic, Ternary Dot Product Scoring Statistic or the Enrichment test over the Homo Sapien STRINGdb causal network (version 10 provided under the Creative Commons license: https://creativecommons.org/licenses/by/3.0/).

Description

This function runs a causal relation engine by computing the Quaternary Dot Product Scoring Statistic, Ternary Dot Product Scoring Statistic or the Enrichment test over the Homo Sapien STRINGdb causal network (version 10 provided under the Creative Commons license: https://creativecommons.org/licenses/by/3.0/).

Usage

RunCRE_HSAStringDB(gene_expression_data, method = "Quaternary", 
                    fc.thresh = log2(1.3), pval.thresh = 0.05, 
                    only.significant.pvalues = FALSE, significance.level = 0.05,
                    epsilon = 1e-16)

Arguments

`gene_expression_data`	A data frame for gene expression data. The `gene_expression_data` data frame must have three columns `entrez`, `fc` and `pvalue`. `entrez` denotes the entrez id of a given gene, `fc` denotes the fold change of a gene, and `pvalue` denotes the p-value. The `entrez` column must be of type integer or character, and the `fc` and `pvalue` columns must be numeric values.
`method`	Choose one of `Quaternary`, `Ternary` or `Enrichment`. Default is `Quaternary`.
`fc.thresh`	Threshold for fold change in `gene_expression_data` data frame. Any row in gene_expression_data with abosolute value of `fc` smaller than `fc.thresh` will be ignored. Default value is `fc.thresh = log2(1.3)`.
`pval.thresh`	Threshold for p-values in `gene_expression_data` data frame. All rows in `gene_expression_data` with p-values greater than `pval.thresh` will be ingnored. Default value is `pval.thresh = 0.05`.
`only.significant.pvalues`	If `only.significant.pvalues = TRUE` then only p-values for statistically significant regulators are computed otherwise uncomputed p-values are set to -1. The default value is `only.significant.pvalues = FALSE`.
`significance.level`	When `only.significant.pvalues = TRUE`, only p-values which are less than or equal to `significance.level` are computed. The default value is `significance.level = 0.05`.
`epsilon`	Threshold for probabilities of matrices. Default value is 1e-16.

Value

This function returns a data frame containing parameters concerning the method used. The p-values of each of the regulators is also computed, and the data frame is in increasing order of p-values of the goodness of fit score for the given regulators. The column names of the data frame are:

uid The regulator in the STRINGdb network.
symbol Symbol of the regulator.
regulation Direction of regulation of the regulator.
correct.pred Number of correct predictions in gene_expression_data when compared to predictions made by the network.
incorrect.pred Number of incorrect predictions in gene_expression_data when compared to predictions made by the network.
score The number of correct predictions minus the number of incorrect predictions.
total.reachable Total Number of children of the given regulator.
significant.reachable Number of children of the given regulator that are also present in gene_expression_data.
total.ambiguous Total number of children of the given regulator which are regulated by the given regulator without knowing the direction of regulation.
significant.ambiguous Total number of children of the given regulator which are regulated by the given regulator without knowing the direction of regulation and are also present in gene_expression_data.
unknown Number of target nodes in the STRINGdb causal network which do not interact with the given regulator.
pvalue P-value of the score computed according to the selected method. If only.significant.pvalues = TRUE and the pvalue of the regulator is greater than significance.level, then the p-value is not computed and is set to a value of -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'.

Examples


# Get gene expression data
e2f3 <- system.file("extdata", "e2f3_sig.txt", package = "QuaternaryProd")
e2f3 <- read.table(e2f3, sep = "\t", header = TRUE, stringsAsFactors = FALSE)

# Rename column names appropriately and remove duplicated entrez ids
names(e2f3) <- c("entrez", "pvalue", "fc")
e2f3 <- e2f3[!duplicated(e2f3$entrez),]

# Compute the Quaternary Dot Product Scoring statistic for statistically significant
# regulators in the STRINGdb network
quaternary_results <- RunCRE_HSAStringDB(e2f3, method = "Quaternary",
                             fc.thresh = log2(1.3), pval.thresh = 0.05,
                             only.significant.pvalues = TRUE)
# Get FDR corrected p-values
quaternary_results["qvalue"] <- p.adjust(quaternary_results$pvalue, method = "fdr")
quaternary_results[1:4, c("uid","symbol","regulation","pvalue","qvalue")]

[Package QuaternaryProd version 1.14.0 Index]