Weighted p‐Value Adjustments for Animal Carcinogenicity Trend Test

Abstract

A typical animal carcinogenicity experiment routinely analyzes approximately 10-30 tumor sites. Comparisons of tumor responses between dosed and control groups and dose-related trend tests are often evaluated for each individual tumor site/type separately. p-Value adjustment approaches have been proposed for controlling the overall Type I error rate or familywise error rate (FWE). However, these adjustments often result in reducing the power to detect a dose effect. This paper proposes using weighted adjustments by assuming that each tumor can be classified as either class A or class B based on prior considerations. The tumors in class A, which are considered as more critical endpoints, are given less adjustment. Two weighted methods of adjustments are presented, the weighted p adjustment and weighted alpha adjustment. A Monte Carlo simulation shows that both weighted adjustments control the FWE well. Furthermore, the power increases if a treatment-dependent tumor is analyzed as in class A tumors and the power decreases if it is analyzed as in class B tumors. A data set from a National Toxicology Program (NTP) 2-year animal carcinogenicity experiment with 13 tumor types/sites observed in male mice was analyzed using the proposed methods. The modified poly-3 test was used to test for increased carcinogenicity since it has been adopted by the NTP as a standard test for a dose-related trend. The unweighted adjustment analysis concluded that there was no statistically significant dose-related trend. Using the Food and Drug Administration classification scheme for the weighted adjustment analyses, two rare tumors (with background rates of 1% or less) were analyzed as class A tumors and 11 common tumors (with background rates higher than 1%) as class B. Both weighted analyses showed a significant dose-related trend for one rare tumor.

Genome-Wide Significance Levels and Weighted Hypothesis Testing

“…The first such weighting scheme appears to be Holm (1979). Related ideas can be found in Benjamini and Hochberg (1997), Chen et al (2000), Genovese, Roeder and Wasserman (2006), Kropf et al (2004), Rosenthal and Rubin (1983), Schuster, Kropf and Roeder (2004), Westfall and Krishen (2001), Westfall, Kropf and Finos (2004), Blanchard and Roquain (2008) and Roquain and van de Wiel (2008), among others. Several of these approaches use data dependent weights and yet maintain familywise error control.…”

Transcriptome analysis of alternative splicing events regulated by SRSF10 reveals position-dependent splicing modulation

“…All junction reads must meet two criteria: (i) at least 6 nt of a read must match perfectly to each of the two flanking regions of a potential junction site, and (ii) a junction site that has >3 non-redundant reads in both the WT and KO samples must be filtered. Afterwards, a series of Java programs implemented into the software named ASD (AS detector) were developed to fulfill the following tasks: (i) to reconstruct exon-clusters based on the aforementioned reannotated chicken transcriptome for identification of five common modes of AS events for each exon-cluster, (ii) to count the number of junction reads that align either to the inclusion or exclusion isoforms in both the WT and KO samples, respectively, and calculate a P -value using junction read-counts between KO and WT samples by Fisher exact test, (iii) to calculate read coverage for the alternative exon and its corresponding gene in both WT and KO samples, respectively, and calculate a second P -value by Fisher exact test based on the alternative exon read coverage relative to its gene read coverage between WT and KO samples, (iv) to combine the two P -values to get an adjusted P -value using a weighted arithmetic equation (6,15) (see Supplementary Method) for assessing the statistical difference of AS between the two samples. ASD is available at http://www.novelbio.com/asd/ASD.html.…”

Global regulation of alternative RNA splicing by the SR-rich protein RBM39

“…As a major class of non-snRNP splicing factors, the serine/arginine-rich (SR-rich) RNA-binding proteins are structurally and functionally conserved and are involved in spliceosome formation as well as in multiple steps of the splicing reaction [7]. The splice sites flanking an alternative exon are used by SR-rich splicing factors to generate a mature mRNA isoform [8][9][10]. The modular domains of SR-rich splicing factors consist of one or two RNA-recognition motifs (RRMs) and a C-terminal arginine-and serine-rich domain (RS domain) [11].…”