Package 'metamicrobiomeR' reference manual

Title:	an R package for analysis of microbiome relative abundance data using zero inflated beta GAMLSS and meta-analysis across studies using random effect model
Description:	The metamicrobiomeR package implements Generalized Additive Model for Location, Scale and Shape (GAMLSS) with zero inflated beta (BEZI) family for analysis of microbiome relative abundance data (with various options for data transformation/normalization to address compositional effects) and random effect meta-analysis models for meta-analysis pooling estimates across microbiome studies. Random Forest model to predict microbiome age based on relative abundances of shared bacterial genera with the Bangladesh data (Subramanian et al 2014), comparison of multiple diversity indexes using linear/linear mixed effect models and some data display/visualization are also implemented.
Authors:	Nhan Ho [aut, cre]
Maintainer:	Nhan Ho <[email protected]>
License:	GPL-2
Version:	1.1
Built:	2024-10-27 04:05:36 UTC
Source:	https://github.com/nhanhocu/metamicrobiomer

Compare multiple alpha diversity indexes between groups

Description

This function calculates average of alpha diversity indexes for a specific rarefaction depth, standardize diversity indexes and compare between groups using linear/linear mixed effect model and adjust for covariates.

Usage

alpha.compare(datlist, depth, mapfile, mapsampleid, comvar, adjustvar,
  personid = "personid", longitudinal = "yes", age.limit = 1e+06,
  standardize = FALSE, ...)
alpha.compare(datlist, depth, mapfile, mapsampleid, comvar, adjustvar,
  personid = "personid", longitudinal = "yes", age.limit = 1e+06,
  standardize = FALSE, ...)

Arguments

`datlist`	the list of your dataframe.
`depth`	the rarefaction depth of choice. Depth can be "max" (highest depth) or an order (number) of the depth in the list generated by alpha_rarefaction.py
`mapfile`	mapping file
`mapsampleid`	sample id in the mapping file
`comvar`	variable for comparison
`adjustvar`	variables that need to be adjusted in the model
`personid`	name of variable for person id. Default is "personid" (applicable when longitudinal="yes").
`longitudinal`	longitudinal data or one time data. Options are c("yes","no"). Default is "yes".
`age.limit`	age upper limit for included samples. Default is 1000000 months (~no upper limit).
`standardize`	standardization of diversity indexes before comparison or not. Default is FALSE.

Value

list of comparison result matrices and mean diversity of all indexes for each samples (with or without standardization)

Examples

#Read in outputs from "alpha_rarefaction.py" in QIIME
patht<-system.file("extdata/QIIME_outputs/Bangladesh/alpha_div_collated", package = "metamicrobiomeR", mustWork = TRUE)
alpha.rm<-read.multi(patht=patht,patternt=".txt",assignt="no",study="Bangladesh")
#Bangladesh extra metadata
data(sam.rm)
samfile<-merge(samde, he50[,c("child.id","gender","month.exbf","month.food")],by="child.id")
samfile$age.sample<-samfile$age.months
samfile$bf<-factor(samfile$bf,levels=c("ExclusiveBF","Non_exclusiveBF","No_BF"))
samfile$personid<-samfile$child.id
samfile$sampleid<-tolower(samfile$fecal.sample.id)
#comparison of standardized alpha diversity indexes between genders adjusting for breastfeeding and infant age at sample collection in infants <=6 months of age
alphacom6.rm.sexsg<-alpha.compare(datlist=alpha.rm,depth=3,mapfile=samfile,mapsampleid="fecal.sample.id",comvar="gender",adjustvar=c("age.sample","bf"),longitudinal="yes",age.limit=6,standardize=TRUE)
alphacom6.rm.sexsg$alphasum[,1:5]
#Read in outputs from "alpha_rarefaction.py" in QIIME
patht<-system.file("extdata/QIIME_outputs/Bangladesh/alpha_div_collated", package = "metamicrobiomeR", mustWork = TRUE)
alpha.rm<-read.multi(patht=patht,patternt=".txt",assignt="no",study="Bangladesh")
#Bangladesh extra metadata
data(sam.rm)
samfile<-merge(samde, he50[,c("child.id","gender","month.exbf","month.food")],by="child.id")
samfile$age.sample<-samfile$age.months
samfile$bf<-factor(samfile$bf,levels=c("ExclusiveBF","Non_exclusiveBF","No_BF"))
samfile$personid<-samfile$child.id
samfile$sampleid<-tolower(samfile$fecal.sample.id)
#comparison of standardized alpha diversity indexes between genders adjusting for breastfeeding and infant age at sample collection in infants <=6 months of age
alphacom6.rm.sexsg<-alpha.compare(datlist=alpha.rm,depth=3,mapfile=samfile,mapsampleid="fecal.sample.id",comvar="gender",adjustvar=c("age.sample","bf"),longitudinal="yes",age.limit=6,standardize=TRUE)
alphacom6.rm.sexsg$alphasum[,1:5]

Nice meta-analysis plots.

Description

This function displays meta-analysis results of relative abundance as a nice combined heatmap and forest plot. More flexibility/options for plot will be added.

Usage

meta.niceplot(metadat, sumtype = "taxa", level = "main", p, p.adjust,
  phyla.col = c("rainbow", "select"),
  phyla.select = c("actinobacteria", "bacteroidetes", "cyanobacteria",
  "firmicutes", "fusobacteria", "proteobacteria", "verrucomicrobia",
  ".thermi."), col.select = c("#dd1c77", "#31a354", "#91003f", "#d95f0e",
  "#636363", "#2ef0e7", "#862ef0", "#000"), est.break = c(-Inf, -1, -0.5,
  -0.1, 0, 0.1, 0.5, 1, Inf), est.break.label = c("<-1", "[-1,-0.5)",
  "[-0.5,-0.1)", "[-0.1,0)", "[0,0.1)", "[0.1,0.5)", "[0.5,1)", ">=1"),
  neg.palette = "PuBu", pos.palette = "YlOrRd", p.sig.heat = "no",
  p.break = c(0, 1e-04, 0.05, 1), p.break.label = c("**", "*", ""),
  p.pool.break = c(0, 0.05, 1), p.pool.break.label = c("[0,0.05)",
  "[0.05,1]"), padjust.pool.break = c(0, 0.1, 1),
  padjust.pool.break.label = c("[0,0.1)", "[0.1,1]"),
  forest.est.shape = c("17", "16"), forest.est.col = c("red", "black"),
  forest.col = c("by.pvalue", "by.estimate"), leg.key.size = 1,
  leg.text.size = 8, heat.text.x.size = 8, heat.text.x.angle = 0,
  forest.axis.text.y = 8, forest.axis.text.x = 8,
  heat.forest.width.ratio = c(1, 1), point.ratio = c(3, 1),
  line.ratio = c(2, 1))
meta.niceplot(metadat, sumtype = "taxa", level = "main", p, p.adjust,
  phyla.col = c("rainbow", "select"),
  phyla.select = c("actinobacteria", "bacteroidetes", "cyanobacteria",
  "firmicutes", "fusobacteria", "proteobacteria", "verrucomicrobia",
  ".thermi."), col.select = c("#dd1c77", "#31a354", "#91003f", "#d95f0e",
  "#636363", "#2ef0e7", "#862ef0", "#000"), est.break = c(-Inf, -1, -0.5,
  -0.1, 0, 0.1, 0.5, 1, Inf), est.break.label = c("<-1", "[-1,-0.5)",
  "[-0.5,-0.1)", "[-0.1,0)", "[0,0.1)", "[0.1,0.5)", "[0.5,1)", ">=1"),
  neg.palette = "PuBu", pos.palette = "YlOrRd", p.sig.heat = "no",
  p.break = c(0, 1e-04, 0.05, 1), p.break.label = c("**", "*", ""),
  p.pool.break = c(0, 0.05, 1), p.pool.break.label = c("[0,0.05)",
  "[0.05,1]"), padjust.pool.break = c(0, 0.1, 1),
  padjust.pool.break.label = c("[0,0.1)", "[0.1,1]"),
  forest.est.shape = c("17", "16"), forest.est.col = c("red", "black"),
  forest.col = c("by.pvalue", "by.estimate"), leg.key.size = 1,
  leg.text.size = 8, heat.text.x.size = 8, heat.text.x.angle = 0,
  forest.axis.text.y = 8, forest.axis.text.x = 8,
  heat.forest.width.ratio = c(1, 1), point.ratio = c(3, 1),
  line.ratio = c(2, 1))

Arguments

`metadat`	output data from metatab.show.
`sumtype`	Either "taxa" for taxa and "path" for pathway.
`level`	"main" for main level such as phylum or "sub" for higher level such as species. Default is "main".
`p`	name of variable for p-values
`p.adjust`	name of variable for multiple testing adjusted p-values
`phyla.col`	type of color for main level (phylum). Options are "rainbow" (default) or "select".
`phyla.select`	selected phyla for selected colors (only when phyla.col="select"). Default are c("actinobacteria","bacteroidetes","cyanobacteria","firmicutes","fusobacteria","proteobacteria","verrucomicrobia",".thermi.").
`col.select`	selected colors for selected phyla (only when phyla.col="select"). Corresponding default are c("#dd1c77","#31a354","#91003f","#d95f0e","#636363","#2ef0e7","#862ef0","#000").
`est.break`	breaks for estimates to generate color categories on heatmap. Default are c(-Inf, -1,-0.5,-0.1,0,0.1,0.5,1, Inf). For pathway, recommended breaks are c(-Inf, -0.5,-0.1,-0.05,0,0.05,0.1,0.5, Inf).
`est.break.label`	labels for corresponding color categories on heatmap generated by est.break. Default corresponding to default est.break are c("<-1","[-1,-0.5)","[-0.5,-0.1)","[-0.1,0)", "[0,0.1)", "[01,0.5)", "[0.5,1)", ">=1"). For pathway, corresponding recommended labels are c("<-0.5)", "[-0.5,-0.1)","[-0.1,-0.05)","[-0.05,0)","[0,0.05)","[0.05,0.1)", "[0.1,0.5)", ">=0.5").
`neg.palette`	color palette for negative estimate values. Default is "PuBu". Use display.brewer.all() of RColorBrewer package for other options.
`pos.palette`	color palette for positive estimate values. Default is "YlOrRd". Use display.brewer.all() of RColorBrewer package for other options.
`p.sig.heat`	whether or not show significant p values on heatmap. Default is "yes".
`p.break`	breaks for significant levels of p values. Default is c(0, 0.0001,0.05, 1).
`p.break.label`	labels to be showed on heatmap for different levels of p-values from p.break. Default is c("*", "","") for p breaks at c(0, 0.0001,0.05, 1).
`p.pool.break`	breaks for pooled p-values to be distinguished in forest plot. Default are c(0,0.05,1).
`p.pool.break.label`	labels for pooled p-value breaks. Corresponding default are c("[0,0.05)","[0.05,1]").
`padjust.pool.break`	breaks for multiple testing adjusted p-values to be distinguished in forest plot. Default are c(0,0.1,1).
`padjust.pool.break.label`	labels for multiple testing adjusted p-value breaks. Corresponding default are c("[0,0.1)","[0.1,1]").
`forest.est.shape`	point shape of pooled estimates in forest plot. Default are c("17","16") for corresponding significant and non-significant pooled estimates.
`forest.est.col`	colors of point (pooled estimates) and 95 CI bars in forest plot. Default are c("red", "black") for significant and non-significant estimates.
`forest.col`	Color of forest plot (point estimates and 95 CI). Options are "by.pvalue" (distinguished by signficant vs. non-significant p-value) or "by.estimate" (color scaled similarly to heatmap color).
`leg.key.size`	legdend key size for heatmap.
`leg.text.size`	legend text size for heatmap.
`heat.text.x.size`	heatmap x label text size.
`heat.text.x.angle`	heatmap x label text angle.
`forest.axis.text.y`	forest plot y label text size.
`forest.axis.text.x`	forest plot x label text size.
`heat.forest.width.ratio`	ratio of width between heatmap and forest plot to be used in grid.arrange. Dedault is c(1,1).
`point.ratio`	ratio of point size between significant pooled estimate and non-significant pooled estimate. Default is c(3,1).
`line.ratio`	ratio of error bar line size between significant pooled estimate and non-significant pooled estimate. Default is=c(2,1).

Value

combined heatmap forest plot.

Examples

#Load saved results of four studies for the comparison of bacterial taxa relative abundance between genders adjusted for breastfeeding and infant age at sample collection
data(taxacom.rm.sex.adjustbfage)
data(taxacom.ha.sex.adjustbfage)
data(taxacom6.zi.usbmk.sex.adjustbfage)
data(taxacom6.unc.sex.adjustedbfage)
taxacom6.zi.rm.sex.adjustbfage$study<-"Subramanian et al 2014 (Bangladesh)"
taxacom6.zi.rm.sex.adjustbfage$pop<-"Bangladesh"
taxacom.zi.ha.sex.adjustbfage$study<-"Bender et al 2016 (Haiti)"
taxacom.zi.ha.sex.adjustbfage$pop<-"Haiti"
taxacom6.zi.usbmk.sex.adjustbfage$study<-"Pannaraj et al 2017 (USA(CA_FL))"
taxacom6.zi.usbmk.sex.adjustbfage$pop<-"USA(CA_FL)"
taxacom6.zi.unc.sex.adjustedbfage$study<-"Thompson et al 2015 (USA(NC))"
taxacom6.zi.unc.sex.adjustedbfage$pop<-"USA(NC)"
tabsex4<-plyr::rbind.fill(taxacom6.zi.rm.sex.adjustbfage,taxacom.zi.ha.sex.adjustbfage,taxacom6.zi.usbmk.sex.adjustbfage,taxacom6.zi.unc.sex.adjustedbfage)
#Meta-analysis (take time to run)
metab.sex<-meta.taxa(taxcomdat=tabsex4,summary.measure="RR",pool.var="id",studylab="study",backtransform=FALSE,percent.meta=0.5,p.adjust.method="fdr")
#nice plot phylum level
metadat<-metatab.show(metatab=metab.sex$random,com.pooled.tab=tabsex4,tax.lev="l2",showvar="genderMale",p.cutoff.type="p", p.cutoff=1,display="data")
meta.niceplot(metadat=metadat,sumtype="taxa",level="main",p="p",p.adjust="p.adjust",phyla.col="rainbow",leg.key.size=1,leg.text.size=8,heat.text.x.size=7,heat.text.x.angle=0,forest.axis.text.y=8,forest.axis.text.x=7)
#nice plot family level
metadat<-metatab.show(metatab=metab.sex$random,com.pooled.tab=tabsex4,tax.lev="l5",showvar="genderMale",p.cutoff.type="p", p.cutoff=1,display="data")
meta.niceplot(metadat=metadat,sumtype="taxa",level="sub",p="p",p.adjust="p.adjust",phyla.col="rainbow",leg.key.size=1,leg.text.size=8,heat.text.x.size=7,forest.axis.text.y=8,forest.axis.text.x=7)
#Load saved results of four studies for the comparison of bacterial taxa relative abundance between genders adjusted for breastfeeding and infant age at sample collection
data(taxacom.rm.sex.adjustbfage)
data(taxacom.ha.sex.adjustbfage)
data(taxacom6.zi.usbmk.sex.adjustbfage)
data(taxacom6.unc.sex.adjustedbfage)
taxacom6.zi.rm.sex.adjustbfage$study<-"Subramanian et al 2014 (Bangladesh)"
taxacom6.zi.rm.sex.adjustbfage$pop<-"Bangladesh"
taxacom.zi.ha.sex.adjustbfage$study<-"Bender et al 2016 (Haiti)"
taxacom.zi.ha.sex.adjustbfage$pop<-"Haiti"
taxacom6.zi.usbmk.sex.adjustbfage$study<-"Pannaraj et al 2017 (USA(CA_FL))"
taxacom6.zi.usbmk.sex.adjustbfage$pop<-"USA(CA_FL)"
taxacom6.zi.unc.sex.adjustedbfage$study<-"Thompson et al 2015 (USA(NC))"
taxacom6.zi.unc.sex.adjustedbfage$pop<-"USA(NC)"
tabsex4<-plyr::rbind.fill(taxacom6.zi.rm.sex.adjustbfage,taxacom.zi.ha.sex.adjustbfage,taxacom6.zi.usbmk.sex.adjustbfage,taxacom6.zi.unc.sex.adjustedbfage)
#Meta-analysis (take time to run)
metab.sex<-meta.taxa(taxcomdat=tabsex4,summary.measure="RR",pool.var="id",studylab="study",backtransform=FALSE,percent.meta=0.5,p.adjust.method="fdr")
#nice plot phylum level
metadat<-metatab.show(metatab=metab.sex$random,com.pooled.tab=tabsex4,tax.lev="l2",showvar="genderMale",p.cutoff.type="p", p.cutoff=1,display="data")
meta.niceplot(metadat=metadat,sumtype="taxa",level="main",p="p",p.adjust="p.adjust",phyla.col="rainbow",leg.key.size=1,leg.text.size=8,heat.text.x.size=7,heat.text.x.angle=0,forest.axis.text.y=8,forest.axis.text.x=7)
#nice plot family level
metadat<-metatab.show(metatab=metab.sex$random,com.pooled.tab=tabsex4,tax.lev="l5",showvar="genderMale",p.cutoff.type="p", p.cutoff=1,display="data")
meta.niceplot(metadat=metadat,sumtype="taxa",level="sub",p="p",p.adjust="p.adjust",phyla.col="rainbow",leg.key.size=1,leg.text.size=8,heat.text.x.size=7,forest.axis.text.y=8,forest.axis.text.x=7)

Meta-analysis of taxa/pathway abundance comparison.

Description

This function does meta-analysis based on estimates and standard errors from taxa/pathway abundance comparison using random effect and fixed effect meta-analysis models.

Usage

meta.taxa(taxcomdat, estimate.pattern = "Estimate.",
  se.pattern = "Std. Error.", summary.measure = "RR",
  pool.var = "id", studylab = "study", backtransform = FALSE,
  percent.meta = 0.5, p.adjust.method = "fdr", ...)
meta.taxa(taxcomdat, estimate.pattern = "Estimate.",
  se.pattern = "Std. Error.", summary.measure = "RR",
  pool.var = "id", studylab = "study", backtransform = FALSE,
  percent.meta = 0.5, p.adjust.method = "fdr", ...)

Arguments

`taxcomdat`	matrice of estimates and SE of all taxa/pathways combined from all included studies.
`estimate.pattern`	string pattern for estimates. Default is "Estimate.".
`se.pattern`	string pattern for standard error. Default is "Std. Error.".
`summary.measure`	"RR" for estimates from GAMLSS with BEZI family and "RD" for estimates from Linear/linear mixed effect model. Default is "RR"
`pool.var`	name of id variable for meta-analysis. Default is "id".
`studylab`	name of variable characterizing included studies. Default is "study".
`backtransform`	whether or not to perform backtransformation of the estimates. Default is FALSE.
`percent.meta`	the threshold percentage of number of studies that a taxa is available to do meta-analysis. Default is 0.5
`p.adjust.method`	method for multiple testing adjustment (available methods of the function p.adjust). Default is "fdr".

Value

a list of matrices of results for all variables in the comparison models.

Examples

#Load saved results of four studies for the comparison of bacterial taxa relative abundance between genders adjusted for breastfeeding and infant age at sample collection
data(taxacom.rm.sex.adjustbfage)
data(taxacom.ha.sex.adjustbfage)
data(taxacom6.zi.usbmk.sex.adjustbfage.)
data(taxacom6.unc.sex.adjustedbfage)
taxacom6.zi.rm.sex.adjustbfage$study<-"Subramanian et al 2014 (Bangladesh)"
taxacom6.zi.rm.sex.adjustbfage$pop<-"Bangladesh"
taxacom.zi.ha.sex.adjustbfage$study<-"Bender et al 2016 (Haiti)"
taxacom.zi.ha.sex.adjustbfage$pop<-"Haiti"
taxacom6.zi.usbmk.sex.adjustbfage$study<-"Pannaraj et al 2017 (USA(CA_FL))"
taxacom6.zi.usbmk.sex.adjustbfage$pop<-"USA(CA_FL)"
taxacom6.zi.unc.sex.adjustedbfage$study<-"Thompson et al 2015 (USA(NC))"
taxacom6.zi.unc.sex.adjustedbfage$pop<-"USA(NC)"
tabsex4<-plyr::rbind.fill(taxacom6.zi.rm.sex.adjustbfage,taxacom.zi.ha.sex.adjustbfage,taxacom6.zi.usbmk.sex.adjustbfage,taxacom6.zi.unc.sex.adjustedbfage)
#Meta-analysis (take time to run)
metab.sex<-meta.taxa(taxcomdat=tabsex4,summary.measure="RR",pool.var="id",studylab="study",backtransform=FALSE,percent.meta=0.5,p.adjust.method="fdr")
metatab.show(metatab=metab.sex$random,com.pooled.tab=tabsex4,tax.lev="l2",showvar="genderMale",p.cutoff.type="p", p.cutoff=0.2,display="table")
#Load saved results of four studies for the comparison of bacterial taxa relative abundance between genders adjusted for breastfeeding and infant age at sample collection
data(taxacom.rm.sex.adjustbfage)
data(taxacom.ha.sex.adjustbfage)
data(taxacom6.zi.usbmk.sex.adjustbfage.)
data(taxacom6.unc.sex.adjustedbfage)
taxacom6.zi.rm.sex.adjustbfage$study<-"Subramanian et al 2014 (Bangladesh)"
taxacom6.zi.rm.sex.adjustbfage$pop<-"Bangladesh"
taxacom.zi.ha.sex.adjustbfage$study<-"Bender et al 2016 (Haiti)"
taxacom.zi.ha.sex.adjustbfage$pop<-"Haiti"
taxacom6.zi.usbmk.sex.adjustbfage$study<-"Pannaraj et al 2017 (USA(CA_FL))"
taxacom6.zi.usbmk.sex.adjustbfage$pop<-"USA(CA_FL)"
taxacom6.zi.unc.sex.adjustedbfage$study<-"Thompson et al 2015 (USA(NC))"
taxacom6.zi.unc.sex.adjustedbfage$pop<-"USA(NC)"
tabsex4<-plyr::rbind.fill(taxacom6.zi.rm.sex.adjustbfage,taxacom.zi.ha.sex.adjustbfage,taxacom6.zi.usbmk.sex.adjustbfage,taxacom6.zi.unc.sex.adjustedbfage)
#Meta-analysis (take time to run)
metab.sex<-meta.taxa(taxcomdat=tabsex4,summary.measure="RR",pool.var="id",studylab="study",backtransform=FALSE,percent.meta=0.5,p.adjust.method="fdr")
metatab.show(metatab=metab.sex$random,com.pooled.tab=tabsex4,tax.lev="l2",showvar="genderMale",p.cutoff.type="p", p.cutoff=0.2,display="table")

Display meta-analysis results.

Description

This function displays meta-analysis results of relative abundance as heatmap, forest plot, table or data.

Usage

metatab.show(metatab, com.pooled.tab, sumvar = "taxa", tax.lev = "l2",
  showvar, estimate.pattern = "Estimate.", se.pattern = "Std. Error.",
  p.pattern = "Pr(>|t|)", readjust.p = FALSE, p.cutoff.type = "p",
  p.cutoff = 0.05, display = "plot", plot = "heatmap",
  fill.value = "log(OR)", grid = FALSE, digit = 2, p.digit = 4,
  ...)
metatab.show(metatab, com.pooled.tab, sumvar = "taxa", tax.lev = "l2",
  showvar, estimate.pattern = "Estimate.", se.pattern = "Std. Error.",
  p.pattern = "Pr(>|t|)", readjust.p = FALSE, p.cutoff.type = "p",
  p.cutoff = 0.05, display = "plot", plot = "heatmap",
  fill.value = "log(OR)", grid = FALSE, digit = 2, p.digit = 4,
  ...)

Arguments

`metatab`	matrice of taxa/pathway abundance comparison meta-analysis results generated from meta.taxa.
`com.pooled.tab`	matrice of taxa/pathway abundance comparison generated from taxa.compare or pathway.compare combined from all included studies.
`sumvar`	Either "taxa" for taxa and "path" for pathway.
`tax.lev`	taxa level to be displayed. Options are from "l2" (phylum) to "l7" (species). Default is "l2".
`showvar`	variable (string pattern) in the model to be displayed.
`estimate.pattern`	string pattern for estimates. Default is "Estimate.".
`se.pattern`	string pattern for standard error. Default is "Std. Error.".
`readjust.p`	multiple testing re-adjustment for only the level to be displayed (TRUE) or keep original multiple testing adjustment for all taxa of all levels (FALSE).Default is FALSE.
`p.cutoff.type`	type of p-value for cutoff. Options are "p" for p-value or "p.adjust" for multiple testing adjusted p-value. Default is "p".
`p.cutoff`	cutoff p-value to be displayed. Default is 0.05.
`display`	type of display. Options are display=c("plot","table","data")
`plot`	type of plot. Options are plot=c("heatmap","forest").
`fill.value`	name of legend.
`grid`	whether multiple plots will be displayed alongside. Default is FALSE.
`digit`	digit for estimates and 95 CI. Default is 2.
`p.digit`	digit for p-values. Default is 4.

Value

plot table or data.

Examples

#Load saved results of four studies for the comparison of bacterial taxa relative abundance between genders adjusted for breastfeeding and infant age at sample collection
data(taxacom.rm.sex.adjustbfage)
data(taxacom.ha.sex.adjustbfage)
data(taxacom6.zi.usbmk.sex.adjustbfage.)
data(taxacom6.unc.sex.adjustedbfage)
taxacom6.zi.rm.sex.adjustbfage$study<-"Subramanian et al 2014 (Bangladesh)"
taxacom6.zi.rm.sex.adjustbfage$pop<-"Bangladesh"
taxacom.zi.ha.sex.adjustbfage$study<-"Bender et al 2016 (Haiti)"
taxacom.zi.ha.sex.adjustbfage$pop<-"Haiti"
taxacom6.zi.usbmk.sex.adjustbfage$study<-"Pannaraj et al 2017 (USA(CA_FL))"
taxacom6.zi.usbmk.sex.adjustbfage$pop<-"USA(CA_FL)"
taxacom6.zi.unc.sex.adjustedbfage$study<-"Thompson et al 2015 (USA(NC))"
taxacom6.zi.unc.sex.adjustedbfage$pop<-"USA(NC)"
tabsex4<-plyr::rbind.fill(taxacom6.zi.rm.sex.adjustbfage,taxacom.zi.ha.sex.adjustbfage,taxacom6.zi.usbmk.sex.adjustbfage,taxacom6.zi.unc.sex.adjustedbfage)
#Meta-analysis (take time to run)
metab.sex<-meta.taxa(taxcomdat=tabsex4,summary.measure="RR",pool.var="id",studylab="study",backtransform=FALSE,percent.meta=0.5,p.adjust.method="fdr")
metatab.show(metatab=metab.sex$random,com.pooled.tab=tabsex4,tax.lev="l2",showvar="genderMale",p.cutoff.type="p", p.cutoff=0.2,display="table")
metatab.show(metatab=metab.sex$random,com.pooled.tab=tabsex4,tax.lev="l2",showvar="genderMale",p.cutoff.type="p", p.cutoff=1,display="plot",plot="heatmap")
#Load saved results of four studies for the comparison of bacterial taxa relative abundance between genders adjusted for breastfeeding and infant age at sample collection
data(taxacom.rm.sex.adjustbfage)
data(taxacom.ha.sex.adjustbfage)
data(taxacom6.zi.usbmk.sex.adjustbfage.)
data(taxacom6.unc.sex.adjustedbfage)
taxacom6.zi.rm.sex.adjustbfage$study<-"Subramanian et al 2014 (Bangladesh)"
taxacom6.zi.rm.sex.adjustbfage$pop<-"Bangladesh"
taxacom.zi.ha.sex.adjustbfage$study<-"Bender et al 2016 (Haiti)"
taxacom.zi.ha.sex.adjustbfage$pop<-"Haiti"
taxacom6.zi.usbmk.sex.adjustbfage$study<-"Pannaraj et al 2017 (USA(CA_FL))"
taxacom6.zi.usbmk.sex.adjustbfage$pop<-"USA(CA_FL)"
taxacom6.zi.unc.sex.adjustedbfage$study<-"Thompson et al 2015 (USA(NC))"
taxacom6.zi.unc.sex.adjustedbfage$pop<-"USA(NC)"
tabsex4<-plyr::rbind.fill(taxacom6.zi.rm.sex.adjustbfage,taxacom.zi.ha.sex.adjustbfage,taxacom6.zi.usbmk.sex.adjustbfage,taxacom6.zi.unc.sex.adjustedbfage)
#Meta-analysis (take time to run)
metab.sex<-meta.taxa(taxcomdat=tabsex4,summary.measure="RR",pool.var="id",studylab="study",backtransform=FALSE,percent.meta=0.5,p.adjust.method="fdr")
metatab.show(metatab=metab.sex$random,com.pooled.tab=tabsex4,tax.lev="l2",showvar="genderMale",p.cutoff.type="p", p.cutoff=0.2,display="table")
metatab.show(metatab=metab.sex$random,com.pooled.tab=tabsex4,tax.lev="l2",showvar="genderMale",p.cutoff.type="p", p.cutoff=1,display="plot",plot="heatmap")

Predict microbiome age.

Description

This function predicts microbiome age using Random Forest model based on relative abundances of bacterial genera shared with the Bangladesh study (Subramanian et al 2014). This function gets the shared genera list between the Bangladesh study and all other included studies, get the training and test sets from Bangladesh data based on the shared genera list, fit the train Random Forest model and predict microbiome age in the test set of Bangladesh data and data from all included studies, check for performance of the model based on the shared genera list on Bangladesh healthy cohort data, reproduce the findings of the Bangladesh malnutrition study.

Usage

microbiomeage(l6.relabundtab)
microbiomeage(l6.relabundtab)

Arguments

l6.relabundtab

list of taxa summary table from phylum up to genus level merged to mapping file outputed from QIIME of all included studies.

Value

list of training and test sets of Bangladesh data, shared genera list, relative abundance data of shared genera, randomforest fit, RF model performance plot,predicted microbiome age of Bangladesh data and data of other included studies.

Examples

#Load data from each study and put in a list
#load Bangladesh taxa relative abundance summary up to genus level merged with mapping file (output from QIIME)
bal6<-read.delim(system.file("extdata/QIIME_outputs/Bangladesh/tax_mapping7", "Subramanian_et_al_mapping_file_L6.txt", package = "metamicrobiomeR", mustWork = TRUE))
colnames(bal6)<-tolower(colnames(bal6))
#View(bal6)
#format for data of other studies should be similar to Bangladesh data, must have 'age.sample' variable as age of infant at stool sample collection
# Load data of 3 other studies
data(gtab.3stud)
names(gtab.3stud)
#predict microbiome age on Bangladesh data and data of other three studies based on shared genera across 4 studies
#(take time to run)
miage<-microbiomeage(l6.relabundtab=gtab.3stud)
#list of shared genera that are available in the Bangladesh study and other included studies
miage$sharedgenera.importance
#check performance
gridExtra::grid.arrange(miage$performanceplot$ptrain, miage$performanceplot$ptest,nrow=1)
#replicate the findings of Subramanian et al paper
ggplot2::ggplot() +geom_point(data=miage$microbiomeage.bangladesh$all,aes(x=age.sample, y=age.predicted, colour=health_analysis_groups))
#Load data from each study and put in a list
#load Bangladesh taxa relative abundance summary up to genus level merged with mapping file (output from QIIME)
bal6<-read.delim(system.file("extdata/QIIME_outputs/Bangladesh/tax_mapping7", "Subramanian_et_al_mapping_file_L6.txt", package = "metamicrobiomeR", mustWork = TRUE))
colnames(bal6)<-tolower(colnames(bal6))
#View(bal6)
#format for data of other studies should be similar to Bangladesh data, must have 'age.sample' variable as age of infant at stool sample collection
# Load data of 3 other studies
data(gtab.3stud)
names(gtab.3stud)
#predict microbiome age on Bangladesh data and data of other three studies based on shared genera across 4 studies
#(take time to run)
miage<-microbiomeage(l6.relabundtab=gtab.3stud)
#list of shared genera that are available in the Bangladesh study and other included studies
miage$sharedgenera.importance
#check performance
gridExtra::grid.arrange(miage$performanceplot$ptrain, miage$performanceplot$ptest,nrow=1)
#replicate the findings of Subramanian et al paper
ggplot2::ggplot() +geom_point(data=miage$microbiomeage.bangladesh$all,aes(x=age.sample, y=age.predicted, colour=health_analysis_groups))

Compare (kegg) pathway abundance

Description

This is a slightly modified version of the taxa.compare function. It compares pathway abundance generated from PICRUSt analysis between groups using different methods (apply to pathway summary tables already merged to mapping file and put in a list (e.g.level 1, 2 and 3)). Specifically, it compares relative abundances of bacterial functional pathways at all levels using GAMLSS or LM/LMEM and compares of log(absolute abundances) of bacterial functional pathways at all levels using LM/LMEM.

Usage

pathway.compare(pathtab, mapfile, sampleid = "sampleid",
  pathsum = "rel", stat.med = "gamlss", transform = "none", comvar,
  adjustvar, personid = "personid", longitudinal = "yes",
  p.adjust.method = "fdr", percent.filter = 0.05,
  relabund.filter = 5e-05, pooldata = FALSE, age.limit = 1e+06,
  age.lowerlimit = 0, ...)
pathway.compare(pathtab, mapfile, sampleid = "sampleid",
  pathsum = "rel", stat.med = "gamlss", transform = "none", comvar,
  adjustvar, personid = "personid", longitudinal = "yes",
  p.adjust.method = "fdr", percent.filter = 0.05,
  relabund.filter = 5e-05, pooldata = FALSE, age.limit = 1e+06,
  age.lowerlimit = 0, ...)

Arguments

`pathtab`	list of pathway abundance table of all levels.
`mapfile`	mapping file or file containing covariates.
`sampleid`	variable containing sample id to be matched between pathway abundance table and mapping file.
`pathsum`	type of abundance to be compared. Options are "rel" for relative abundance or "log" for log of absolute abundance. Default is "rel".
`stat.med`	statistical method for comparison. stat.med can be "lm" for LM/LMEM (usable for both pathsum="rel" or "log") or "gamlss" for GAMLSS with BEZI family (gamlss only make sense if pathsum="rel" ).
`comvar`	main variable for comparison.
`adjustvar`	variables to be adjusted.
`personid`	name of variable for person id in mapping file (applicable for longitudinal data)
`longitudinal`	whether the data is longitudinal. Default is "yes".
`p.adjust.method`	method for multiple testing adjustment. Available options are those of the p.adjust function. Default is "fdr".
`percent.filter`	prevalence threshold (the percentage of number of samples the taxa/pathway available). Default is 0.05.
`relabund.filter`	relative abundance threshold (the minimum of the average relative abundance for a taxa/pathway to be retained). Default is 0.00005.
`pooldata`	whether the data is pooled from multiple studies. Default is FALSE.
`age.limit`	upper age limit for data to be included. Default is 1000000 months (~no upper age limit).
`age.lowerlimit`	lower age limit for data to be included. Default is 0 month.

Value

matrice of coefficients, standard errors, p-values and multiple testing adjusted p-values of all variables in the models.

Examples

#Load Bangladesh extra metadata
data(sam.rm)
#Read in PICRUSt output for KEGG pathways level 1-3
patht<-system.file("extdata/QIIME_outputs/Bangladesh/picrust", package = "metamicrobiomeR", mustWork = TRUE)
kegg<-read.multi(patht=patht,patternt=".txt",assignt="no")
kegg.rm<-list()
for (i in 1:length(kegg)){
  rownames(kegg[[i]])<-kegg[[i]][,"kegg_pathways"]
  kegg[[i]]<-kegg[[i]][,colnames(kegg[[i]])[!colnames(kegg[[i]]) %in% c("otu.id","kegg_pathways")]]
  kegg.rm[[i]]<-as.data.frame(t(kegg[[i]]))
}
covar.rm<-merge(samde, he50[,c("child.id","gender","zygosity","day.firstsample","day.lastsample","n.sample","sampling.interval.msd","month.exbf","month.food",
                               "n.diarrhea.yr","percent.time.diarrhea","fraction.antibiotic","subject.allocation")], by="child.id")
covar.rm<-dplyr::rename(covar.rm,sampleid=fecal.sample.id, personid=child.id ,age.sample=age.months)
covar.rm$bf<-factor(covar.rm$bf, levels=c('ExclusiveBF','Non_exclusiveBF','No_BF'))
covar.rm$personid<-as.factor(covar.rm$personid)
#Comparison of pathway relative abundances (take time to run)
pathcom.rm6.rel.gamlss.sexg<-pathway.compare(pathtab=kegg.rm,mapfile=covar.rm,sampleid="sampleid",pathsum="rel",stat.med="gamlss",comvar="gender",adjustvar=c("age.sample","bf"),longitudinal="yes",p.adjust.method="fdr",percent.filter=0.05,relabund.filter=0.00005,age.limit=6)
taxcomtab.show(taxcomtab=pathcom.rm6.rel.gamlss.sexg$l3, sumvar="path",tax.lev="l3",tax.select="none",showvar="genderMale", p.adjust.method="fdr",p.cutoff=0.05,digit=4)
#Load Bangladesh extra metadata
data(sam.rm)
#Read in PICRUSt output for KEGG pathways level 1-3
patht<-system.file("extdata/QIIME_outputs/Bangladesh/picrust", package = "metamicrobiomeR", mustWork = TRUE)
kegg<-read.multi(patht=patht,patternt=".txt",assignt="no")
kegg.rm<-list()
for (i in 1:length(kegg)){
  rownames(kegg[[i]])<-kegg[[i]][,"kegg_pathways"]
  kegg[[i]]<-kegg[[i]][,colnames(kegg[[i]])[!colnames(kegg[[i]]) %in% c("otu.id","kegg_pathways")]]
  kegg.rm[[i]]<-as.data.frame(t(kegg[[i]]))
}
covar.rm<-merge(samde, he50[,c("child.id","gender","zygosity","day.firstsample","day.lastsample","n.sample","sampling.interval.msd","month.exbf","month.food",
                               "n.diarrhea.yr","percent.time.diarrhea","fraction.antibiotic","subject.allocation")], by="child.id")
covar.rm<-dplyr::rename(covar.rm,sampleid=fecal.sample.id, personid=child.id ,age.sample=age.months)
covar.rm$bf<-factor(covar.rm$bf, levels=c('ExclusiveBF','Non_exclusiveBF','No_BF'))
covar.rm$personid<-as.factor(covar.rm$personid)
#Comparison of pathway relative abundances (take time to run)
pathcom.rm6.rel.gamlss.sexg<-pathway.compare(pathtab=kegg.rm,mapfile=covar.rm,sampleid="sampleid",pathsum="rel",stat.med="gamlss",comvar="gender",adjustvar=c("age.sample","bf"),longitudinal="yes",p.adjust.method="fdr",percent.filter=0.05,relabund.filter=0.00005,age.limit=6)
taxcomtab.show(taxcomtab=pathcom.rm6.rel.gamlss.sexg$l3, sumvar="path",tax.lev="l3",tax.select="none",showvar="genderMale", p.adjust.method="fdr",p.cutoff=0.05,digit=4)

Read multiple files

Description

This function reads multiple files of the same pattern in a directory into R.

Usage

read.multi(patht, patternt = ".txt", assignt = "no", study = NULL,
  tolower.colnames = TRUE)
read.multi(patht, patternt = ".txt", assignt = "no", study = NULL,
  tolower.colnames = TRUE)

Arguments

`patht`	path to files.
`patternt`	pattern of files. Options are ".txt" or ".csv" or ".sav" or ".sas7bdat".
`assignt`	assign files. Default is "no".
`study`	name of the study. Default is NULL.
`tolower.colnames`	turn all column names to lower case. Default is TRUE.

Value

list of all data files in the path

Examples

patht<-system.file("extdata/QIIME_outputs/Bangladesh/alpha_div_collated", package = "metamicrobiomeR", mustWork = TRUE)
alpha.ba<-read.multi(patht=patht,patternt=".txt",assignt="no",study="Bangladesh")
patht<-system.file("extdata/QIIME_outputs/Bangladesh/alpha_div_collated", package = "metamicrobiomeR", mustWork = TRUE)
alpha.ba<-read.multi(patht=patht,patternt=".txt",assignt="no",study="Bangladesh")

Compare taxa relative abundance

Description

This function compares taxa relative abundance summary tables at all levels between groups using GAMLSS with BEZI or Linear/Linear Mixed Effect models (LM/LMEM) after filtering (using prevalence and relative abundance thresholds).

Usage

taxa.compare(taxtab, propmed.rel = "gamlss", transform = "none",
  zeroreplace.method = "none", comvar, adjustvar,
  personid = "personid", longitudinal = "yes", percent.filter = 0.05,
  relabund.filter = 5e-05, p.adjust.method = "fdr", ...)
taxa.compare(taxtab, propmed.rel = "gamlss", transform = "none",
  zeroreplace.method = "none", comvar, adjustvar,
  personid = "personid", longitudinal = "yes", percent.filter = 0.05,
  relabund.filter = 5e-05, p.adjust.method = "fdr", ...)

Arguments

`taxtab`	taxa relative abundance table (already merged to mapping file) from phylum to species or any preferred highest taxa level.
`propmed.rel`	statistical method for comparing relative abundance. Options are "lm" for LM/LMEM or "gamlss" for GAMLSS with BEZI family.
`transform`	transformation of relative abundance data. Options are "none" for no transformation, "gmpr" for Geometric Mean of Pairwise Ratios (GMPR) normalization, "asin.sqrt" for arcsine transformation, "logit" for logit transformation, "clr" for centered log ratio transformation. Default is "none".
`zeroreplace.method`	Method for zero replacement implemented in R package zCompositions. Options are "none" for no replacement, "multKM" for Multiplicative Kaplan-Meier smoothing spline (KMSS) replacement, "multLN" for Multiplicative lognormal replacement, "multRepl" for Multiplicative simple replacement, "lrEM" for Log-ratio EM algorithm, "lrDA" for Log-ratio DA algorithm. Default is "none".
`comvar`	main variable for comparison
`adjustvar`	variables to be adjusted.
`personid`	name of variable for person id (applicable for longitudinal data)
`longitudinal`	whether data is longitudinal? Options are "yes" or "no". Default is "yes".
`percent.filter`	prevalence threshold (the percentage of number of samples the taxa/pathway available). Default is 0.05.
`relabund.filter`	relative abundance threshold (the minimum of the average relative abundance for a taxa/pathway to be retained). Default is 0.00005.
`p.adjust.method`	method for multiple testing adjustment. Options are those of the p.adjust.methods of stats:: p.adjust function. Default for this function is "fdr".
`pooldata`	whether data is pooled from multiple studies. Default is FALSE.

Value

matrice of coefficients, standard errors, p-values and multiple testing adjusted p-values of all variables in the models.

Examples

#Load summary tables of bacterial taxa relative abundance from Bangladesh data
data(taxtab.rm7)
#Comparison of bacterial taxa relative abundance using LMEM or GAMLSS (take time to run)
taxacom6.rmg<-taxa.compare(taxtab=taxtab6.rm[[5]],propmed.rel="lm",comvar="bf",adjustvar="age.sample",longitudinal="yes",p.adjust.method="fdr")
taxacom6.zi.rmg<-taxa.compare(taxtab=taxtab6.rm[[5]],propmed.rel="gamlss",comvar="bf",adjustvar="age.sample",longitudinal="yes",p.adjust.method="fdr")
taxcomtab.show(taxcomtab=taxacom6.zi.rmg,tax.select="none", showvar="bfNon_exclusiveBF", tax.lev="l2",readjust.p=TRUE,p.adjust.method="fdr")
#Load summary tables of bacterial taxa relative abundance from Bangladesh data
data(taxtab.rm7)
#Comparison of bacterial taxa relative abundance using LMEM or GAMLSS (take time to run)
taxacom6.rmg<-taxa.compare(taxtab=taxtab6.rm[[5]],propmed.rel="lm",comvar="bf",adjustvar="age.sample",longitudinal="yes",p.adjust.method="fdr")
taxacom6.zi.rmg<-taxa.compare(taxtab=taxtab6.rm[[5]],propmed.rel="gamlss",comvar="bf",adjustvar="age.sample",longitudinal="yes",p.adjust.method="fdr")
taxcomtab.show(taxcomtab=taxacom6.zi.rmg,tax.select="none", showvar="bfNon_exclusiveBF", tax.lev="l2",readjust.p=TRUE,p.adjust.method="fdr")

Filter relative abundance data

Description

This function filters bacterial taxa or pathway relative abundance tables based on the percentage of samples with their availability (prevalence) and relative abundance thresholds. It will remove taxa/pathway with relative abundance <relabund.filter and available in <percent.filter of number of samples.

Usage

taxa.filter(taxtab, percent.filter = 0.05, relabund.filter = 5e-05)
taxa.filter(taxtab, percent.filter = 0.05, relabund.filter = 5e-05)

Arguments

`taxtab`	taxa/pathway relative abundance table.
`percent.filter`	prevalence threshold (the percentage of number of samples the taxa/pathway available). Default is 0.05.
`relabund.filter`	relative abundance threshold (the minimum of the average relative abundance for a taxa/pathway to be retained). Default is 0.00005.

Value

list of all taxa/pathways retained after filtering.

Examples

#Load summary tables of bacterial taxa relative abundance from Bangladesh data
data(taxtab.rm7)
taxlist.rm<-taxa.filter(taxtab=taxtab.rm[[5]],percent.filter = 0.05, relabund.filter = 0.00005)
#Load summary tables of bacterial taxa relative abundance from Bangladesh data
data(taxtab.rm7)
taxlist.rm<-taxa.filter(taxtab=taxtab.rm[[5]],percent.filter = 0.05, relabund.filter = 0.00005)

Plot mean taxa abundance

Description

This function visualize mean relative abundance by group as stacked plots.

Usage

taxa.mean.plot(tabmean, sumvar = "taxa", tax.select = "none",
  tax.lev = "l2", comvar, groupvar, mean.filter = 0.005,
  pallete.by.phylum = FALSE, show.taxname = "full",
  legend.position = "right", xlab = "Chronological age (month)",
  ylab = "Relative abundance")
taxa.mean.plot(tabmean, sumvar = "taxa", tax.select = "none",
  tax.lev = "l2", comvar, groupvar, mean.filter = 0.005,
  pallete.by.phylum = FALSE, show.taxname = "full",
  legend.position = "right", xlab = "Chronological age (month)",
  ylab = "Relative abundance")

Arguments

`tabmean`	table of mean abundance generated from taxa.meansdn.
`sumvar`	variable to be plotted. Options are c("taxa","path"). Default is "taxa"
`tax.select`	list of selected taxa/pathways to be plotted. Default is "none" or plot all taxa/pathways.
`tax.lev`	taxa level to be visualized. Options are from "l2" (phylum) to "l7" (species). Default is "l2". If sumvar="path", all pathways will be visualized.
`comvar`	main variable for comparison.
`groupvar`	variable for stratifying.
`mean.filter`	mean abundance filtering threshold (only plot those with mean abundance>threshold and plot all those with mean abundance <threshold as "other").
`pallete.by.phylum`	whether each pallete of color for each phylum. Default is FALSE (plot distinc colors).
`show.taxname`	whether show "full" taxa name or "short" name. Default is "full".
`legend.position`	position of legend. Options are c("right", "left","bottom","top","none") as in ggplot2. Default is "right".

Value

a list of ggplot2 object and list of taxa/pathways plotted (those with mean abundance >mean.filter).

Examples

#Load summary tables of bacterial taxa relative abundance from Bangladesh data
data(taxtab.rm7)
library(magrittr)
taxa.meansdn.rm<-taxa.meansdn(taxtab=taxtab.rm[[5]],sumvar="bf",groupvar="age.sample")
p.bf.l2<-taxa.mean.plot(tabmean=taxa.meansdn.rm,tax.lev="l2", comvar="bf", groupvar="age.sample",mean.filter=0.005,show.taxname="short")
p.bf.l2$p
#Load summary tables of bacterial taxa relative abundance from Bangladesh data
data(taxtab.rm7)
library(magrittr)
taxa.meansdn.rm<-taxa.meansdn(taxtab=taxtab.rm[[5]],sumvar="bf",groupvar="age.sample")
p.bf.l2<-taxa.mean.plot(tabmean=taxa.meansdn.rm,tax.lev="l2", comvar="bf", groupvar="age.sample",mean.filter=0.005,show.taxname="short")
p.bf.l2$p

Summarize abundance by group

Description

This function summarizes taxa/pathway abundance tables to provide mean, sd, count by groups.

Usage

taxa.meansdn(taxtab, sumvar, groupvar, percent.filter = 0.05,
  relabund.filter = 5e-05, othervar = "none")
taxa.meansdn(taxtab, sumvar, groupvar, percent.filter = 0.05,
  relabund.filter = 5e-05, othervar = "none")

Arguments

`taxtab`	taxa/pathway abundance table from phylum to species or any preferred highest taxa level.
`sumvar`	main variable for summary
`groupvar`	variable to be stratified.
`percent.filter`	prevalence threshold (the percentage of number of samples the taxa/pathway available). Default is 0.05.
`relabund.filter`	relative abundance threshold (the minimum of the average relative abundance for a taxa/pathway to be retained). Default is 0.00005.
`othervar`	list of variables that are not abundance variables to be summarized. Default is "none".

Value

table of mean, sd, count by group.

Examples

#Load summary tables of bacterial taxa relative abundance from Bangladesh data
data(taxtab.rm7)
library(magrittr)
taxa.meansdn.rm<-taxa.meansdn(taxtab=taxtab.rm[[5]],sumvar="bf",groupvar="age.sample")
#Load summary tables of bacterial taxa relative abundance from Bangladesh data
data(taxtab.rm7)
library(magrittr)
taxa.meansdn.rm<-taxa.meansdn(taxtab=taxtab.rm[[5]],sumvar="bf",groupvar="age.sample")

Display abundance comparison results.

Description

This function displays taxa/pathway abundance comparison results as table.

Usage

taxcomtab.show(taxcomtab, sumvar = "taxa", tax.lev = "l2",
  tax.select = "none", showvar, readjust.p = FALSE,
  p.adjust.method = "fdr", p.cutoff = 0.05, digit = 2, p.digit = 4,
  ...)
taxcomtab.show(taxcomtab, sumvar = "taxa", tax.lev = "l2",
  tax.select = "none", showvar, readjust.p = FALSE,
  p.adjust.method = "fdr", p.cutoff = 0.05, digit = 2, p.digit = 4,
  ...)

Arguments

`taxcomtab`	table of taxa abundance comparison generated from taxa.compare.
`sumvar`	Options are "taxa" for bacterial taxa and "path" for pathway. Default is "taxa"
`tax.lev`	taxa level to be displayed. Options are from "l2" (phylum) to "l7" (species). Default is "l2".
`tax.select`	selected list of taxa to be displayed. Default is "none" or display all available taxa.
`showvar`	variable (pattern) in the model to be displayed.
`readjust.p`	multiple testing re-adjustment for only the level to be displayed (TRUE) or keep original multiple testing adjustment for all taxa of all levels (FALSE).
`p.adjust.method`	method for multiple testing adjustment. Available options are those of the p.adjust function. Default is "fdr".
`p.cutoff`	cutoff p-value to be displayed. Default is 0.05.
`digit`	digit for estimates and 95 CI. Default is 2.
`p.digit`	digit for p-values. Default is 4.

Value

a table of results.

Examples

#Load summary tables of bacterial taxa relative abundance from Bangladesh data
data(taxtab.rm7)
#Comparison of bacterial taxa relative abundance using LMEM or GAMLSS (take time to run)
taxacom6.rmg<-taxa.compare(taxtab=taxtab6.rm[[5]],taxsum="rel",propmed.rel="lm",comvar="bf",adjustvar="age.sample",longitudinal="yes",p.adjust.method="fdr")
taxcomtab.show(taxcomtab=taxacom6.rmg,tax.select="none", showvar="bfNon_exclusiveBF", tax.lev="l2",readjust.p=TRUE,p.adjust.method="fdr")
#Load summary tables of bacterial taxa relative abundance from Bangladesh data
data(taxtab.rm7)
#Comparison of bacterial taxa relative abundance using LMEM or GAMLSS (take time to run)
taxacom6.rmg<-taxa.compare(taxtab=taxtab6.rm[[5]],taxsum="rel",propmed.rel="lm",comvar="bf",adjustvar="age.sample",longitudinal="yes",p.adjust.method="fdr")
taxcomtab.show(taxcomtab=taxacom6.rmg,tax.select="none", showvar="bfNon_exclusiveBF", tax.lev="l2",readjust.p=TRUE,p.adjust.method="fdr")

Package 'metamicrobiomeR'

Help Index

Compare multiple alpha diversity indexes between groups

Description

Usage

Arguments

Value

Examples

Nice meta-analysis plots.

Description

Usage

Arguments

Value

Examples

Meta-analysis of taxa/pathway abundance comparison.

Description

Usage

Arguments

Value

Examples

Display meta-analysis results.

Description

Usage

Arguments

Value

Examples

Predict microbiome age.

Description

Usage

Arguments

Value

Examples

Compare (kegg) pathway abundance

Description

Usage

Arguments

Value

Examples

Read multiple files

Description

Usage

Arguments

Value

Examples

Compare taxa relative abundance

Description

Usage

Arguments

Value

Examples

Filter relative abundance data

Description

Usage

Arguments

Value

Examples

Plot mean taxa abundance

Description

Usage

Arguments

Value

Examples

Summarize abundance by group

Description

Usage

Arguments

Value

Examples

Display abundance comparison results.

Description

Usage

Arguments

Value

Examples