tabsex4: Combined data for meta-analysis.

In metamicrobiomeR: Microbiome Data Analysis & Meta-Analysis with GAMLSS-BEZI & Random Effects

Description

Result outputs of differential abundance analysis using GAMLSS_BEZI from "taxa.compare" function combined from 4 studies for meta-analysis. The comparison was between gender adjusted for age of infants at sample collection.

Usage

data(tabsex4)

Format

A dataframe with 701 rows and 23 variables.

Source

Gordon Lab

References

Subramanian et al. Nature. 2014 Jun 19; 510(7505): 417–421. (PubMed)

Bender et al. Sci Transl Med. 2016 Jul 27; 8(349): 349ra100. (PubMed)

Pannaraj et al. JAMA Pediatr. 2017;90095(7):647–54. (PubMed)

Thompson et al. Front Cell Infect Microbiol. 2015;5:3. (PubMed)

Examples

# load saved GAMLSS-BEZI results of four studies
# for the comparison of bacterial taxa relative abundance between
# genders adjusted for breastfeeding and infant age at sample collection
data(tabsex4)
#select only taxonomies of a small phylum for meta-analysis example
# (to save running time)
tlm<-tabsex4$id[grep("k__bacteria.p__fusobacteria",tabsex4$id)]
# meta-analysis
metab.sex<-meta.taxa(taxcomdat=tabsex4[tabsex4$id %in% tlm,],
summary.measure="RR", pool.var="id", studylab="study",
backtransform=FALSE, percent.meta=0.5, p.adjust.method="fdr")
#show results by table and plot
#phylum table
metatab.show(metatab=metab.sex$random,com.pooled.tab=tabsex4[tabsex4$id %in% tlm,],
tax.lev="l2",showvar="genderMale",p.cutoff.type="p", p.cutoff=1,display="table")

metamicrobiomeR documentation built on Nov. 9, 2020, 5:06 p.m.