BEST.pearson {GeneNT} | R Documentation |
This function implement the BLAST type two-stage screening procedure based on Pearson correlation coefficient. Specifying a pair of FDR and MAS criteria, and a seed gene, the algorithm screens a seeded gene cluster with controlled FDR and MAS.
BEST.pearson(gene.name, Q, cormin, method)
gene.name |
The "seed" gene name |
Q |
The significant level |
cormin |
The specified minimum acceptable strength of association measured by Pearson correlation statistic |
method |
The multiple comparison adjustment methods, eg, "BH" (for independent tests) and "BY" (for general dependent tests) |
The data matrix file must be in the right format. The first row (header) must be one shorter than the rest rows. The first column must be gene names.
The function returns a seeded gene cluster that satisfies the FDR and MAS criteria measured by Pearson correlation coefficient simultaneously.
bpG1 |
The gene pairs that passes Stage I (FDR only) screening |
bpG2 |
The gene pairs that passed both Stage I (FDR) and II (MAS) screenings |
Dongxiao Zhu (http://dongxiaozhu.googlepages.com/home)
Zhu, D., Hero, A.O., Qin, Z.S. and Swaroop, A. High throughput screening of co-expressed gene pairs with controlled False Discovery Rate (FDR) and Minimum Acceptable Strength (MAS). J Comput Biol, 12, 1027-1043.
Fisher, R.A. (1921). On the 'probable error' of a coefficient of correlation deduced from a small sample. Metron, 1, 1–32.
## Load GeneNT library library(GeneNT) ## load example dataset data(dat) ## Note, data matrix name has to be "dat" ## use (FDR, MAS) criteria (0.2, 0.5) and seed gene "GAL7" as example to screen gene pairs ## use "BY" procedure to do multiple comparison adjustment g4 <- BEST.pearson("GAL7", 0.2, 0.5, "BY") bpG1 <- g4$bpG1 ## bpG2 contains gene pairs that passed two-stage screening bpG2 <- g4$bpG2