In frequency analyses, why not use a Poisson test?
TFBM frequency data do not follow a Poisson distribution, so that test would produce inaccurate p-values. The variance of TFBM frequency data often exceeds the mean frequency by 2-fold or more, whereas the Poisson distribution assumes the mean and variance are equal. We recommend using the default z-test instead, but a Poisson-based analysis is available. Q: What is the risk of a false positive result? A: The p-value for each statistical test gives the risk of a false positive error for that particular TFBM (e.g., p p-value for any single test. False positive risks are often analyzed in terms of a “false discovery rate” (FDR) — the fraction of significant results that are likely due to chance alone. FDRs depend upon several factors, including the number of genes analyzed, the number of TFBMs surveyed, the characteristics of the promoter scan (stringency and promoter size), the stringency of the statistical analysis (p p TELiS differential expression analyses provide two FDR estimates.
