## ---- include = FALSE--------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ----DEPENDENCIES, echo=FALSE, eval=TRUE, results='asis', tidy=FALSE---------- # Dependency table pkg_dependencies <- data.frame("Package"=c("phyloseq","magrittr","cluster", "dplyr","ggplot2","gridExtra", "limma","lme4","lmerTest","pheatmap", "rmarkdown","ruv","sva","tibble", "tidyr","vegan","methods","stats", "utils"), "Version"=NA, "Date"=NA, "Repository"=NA) for( pkg in 1:length(pkg_dependencies$Package) ) { pkg_dependencies$Version[pkg] <- toString(utils::packageVersion(eval(pkg_dependencies$Package[pkg]))) tmp_description <- utils::packageDescription(eval(pkg_dependencies$Package[pkg])) pkg_dependencies$Date[pkg] <- toString(tmp_description["Date"]) pkg_dependencies$Repository[pkg] <- toString(tmp_description["Repository"]) } knitr::kable(pkg_dependencies, align = 'c', caption = "MBECS package dependencies", label = NULL) ## ----BIOCINSTALLATION, echo=TRUE, eval=FALSE, results='asis', tidy=FALSE------ # if (!"BiocManager" %in% rownames(installed.packages())) # install.packages("BiocManager") # BiocManager::install("MBECS") ## ----GITINSTALLATION, echo=TRUE, eval=FALSE, results='asis', tidy=FALSE------- # # Use the devtools package to install from a GitHub repository. # install.packages("devtools") # # # This will install the MBECS package from GitHub. # devtools::install_github("rmolbrich/MBECS") ## ----ACTIVATION, echo=TRUE, eval=TRUE, results='asis', tidy=FALSE------------- library(MBECS) ## ----Load_dummies, echo=TRUE, eval=TRUE, results='asis', tidy=FALSE----------- # List object data(dummy.list) # Phyloseq object data(dummy.ps) # MbecData object data(dummy.mbec) ## ----Structure_list, echo=TRUE, eval=TRUE, tidy=FALSE------------------------- # The dummy-list input object comprises two matrices: names(dummy.list) ## ----Usage_list, echo=TRUE, eval=TRUE, tidy=FALSE----------------------------- mbec.obj <- mbecProcessInput(dummy.list, required.col = c("group", "batch", "replicate")) ## ----Usage_phyloseq, echo=TRUE, eval=TRUE, tidy=FALSE------------------------- mbec.obj <- mbecProcessInput(dummy.ps, required.col = c("group", "batch", "replicate")) ## ----Usage_tss_transformation, echo=TRUE, eval=TRUE, tidy=FALSE--------------- mbec.obj <- mbecTransform(mbec.obj, method = "tss") ## ----Usage_clr_transformation, echo=TRUE, eval=TRUE, tidy=FALSE--------------- mbec.obj <- mbecTransform(mbec.obj, method = "clr", offset = 0.0001) ## ----Usage_prelimreport, echo=TRUE, eval=FALSE, results='asis', tidy=FALSE---- # mbecReportPrelim(input.obj=mbec.obj, model.vars=c("batch","group"), # type="clr") ## ----Usage_single_correction, echo=TRUE, eval=TRUE, results='asis', tidy=FALSE---- mbec.obj <- mbecCorrection(mbec.obj, model.vars=c("batch","group"), method = "bat", type = "clr") ## ----Usage_multiple_correction, echo=TRUE, eval=TRUE, results='asis', tidy=FALSE---- mbec.obj <- mbecRunCorrections(mbec.obj, model.vars=c("batch","group"), method=c("ruv3","rbe","bmc","pn","svd"), type = "clr") ## ----Usage_postreport, echo=TRUE, eval=FALSE, results='asis', tidy=FALSE------ # mbecReportPost(input.obj=mbec.obj, model.vars=c("batch","group"), # type="clr") ## ----Usage_returnPS_clr, echo=TRUE, eval=FALSE, results='asis', tidy=FALSE---- # ps.clr <- mbecGetPhyloseq(mbec.obj, type="clr") ## ----Usage_returnPS_bmc, echo=TRUE, eval=FALSE, results='asis', tidy=FALSE---- # ps.bmc <- mbecGetPhyloseq(mbec.obj, type="cor", label="bmc") ## ----Usage_rle, echo=TRUE, eval=TRUE, results='asis', tidy=FALSE-------------- rle.plot <- mbecRLE(input.obj=mbec.obj, model.vars = c("batch","group"), type="clr",return.data = FALSE) # Take a look. plot(rle.plot) ## ----Usage_pca_one, echo=TRUE, eval=TRUE, results='asis', tidy=FALSE---------- pca.plot <- mbecPCA(input.obj=mbec.obj, model.vars = "group", type="clr", pca.axes=c(1,2), return.data = FALSE) ## ----Usage_pca_two, echo=TRUE, eval=TRUE, results='asis', tidy=FALSE---------- pca.plot <- mbecPCA(input.obj=mbec.obj, model.vars = c("batch","group"), type="clr", pca.axes=c(1,2), return.data = FALSE) ## ----Usage_box_n, echo=TRUE, eval=TRUE, results='asis', tidy=FALSE------------ box.plot <- mbecBox(input.obj=mbec.obj, method = "TOP", n = 2, model.var = "batch", type="clr", return.data = FALSE) # Take a look. plot(box.plot$OTU109) ## ----Usage_box_selected, echo=TRUE, eval=TRUE, results='asis', tidy=FALSE----- box.plot <- mbecBox(input.obj=mbec.obj, method = c("OTU1","OTU2"), model.var = "batch", type="clr", return.data = FALSE) # Take a look. plot(box.plot$OTU2) ## ----Usage_heat, echo=TRUE, eval=TRUE, results='asis', tidy=FALSE------------- heat.plot <- mbecHeat(input.obj=mbec.obj, method = "TOP", n = 10, model.vars = c("batch","group"), center = TRUE, scale = TRUE, type="clr", return.data = FALSE) ## ----Usage_mosaic, echo=TRUE, eval=TRUE, results='asis', tidy=FALSE----------- mosaic.plot <- mbecMosaic(input.obj = mbec.obj, model.vars = c("batch","group"), return.data = FALSE) ## ----Usage_varLM, echo=TRUE, eval=TRUE, results='hide', fig.keep='all', message = FALSE, warning = FALSE, tidy=FALSE---- lm.variance <- mbecModelVariance(input.obj=mbec.obj, model.vars = c("batch", "group"), method="lm",type="cor", label="svd") # Produce plot from data. lm.plot <- mbecVarianceStatsPlot(lm.variance) # Take a look. plot(lm.plot) ## ----Usage_varLMM, echo=TRUE, eval=TRUE, results='hide', fig.keep='all', message = FALSE, warning = FALSE, tidy=FALSE---- lmm.variance <- mbecModelVariance(input.obj=mbec.obj, model.vars = c("batch","group"), method="lmm", type="cor", label="ruv3") # Produce plot from data. lmm.plot <- mbecVarianceStatsPlot(lmm.variance) # Take a look. plot(lmm.plot) ## ----Usage_varRDA, echo=TRUE, eval=TRUE, results='hide', fig.keep='all', message = FALSE, warning = FALSE, tidy=FALSE---- rda.variance <- mbecModelVariance(input.obj=mbec.obj, model.vars = c("batch", "group"), method="rda",type="cor", label="bat") # Produce plot from data. rda.plot <- mbecRDAStatsPlot(rda.variance) # Take a look. plot(rda.plot) ## ----Usage_varPVCA, echo=TRUE, eval=TRUE, results='hide', fig.keep='all', message = FALSE, warning = FALSE, tidy=FALSE---- pvca.variance <- mbecModelVariance(input.obj=mbec.obj, model.vars = c("batch", "group"), method="pvca",type="cor", label="rbe") # Produce plot from data. pvca.plot <- mbecPVCAStatsPlot(pvca.variance) # Take a look. plot(pvca.plot) ## ----Usage_varSCOEF, echo=TRUE, eval=TRUE, results='hide', fig.keep='all', message = FALSE, warning = FALSE, tidy=FALSE---- sil.coefficient <- mbecModelVariance(input.obj=mbec.obj, model.vars = c("batch", "group"), method="s.coef",type="cor", label="bmc") # Produce plot from data. scoef.plot <- mbecSCOEFStatsPlot(sil.coefficient) # Take a look. plot(scoef.plot) ## ----Session_Info, echo=FALSE, eval=TRUE-------------------------------------- print(sessionInfo(), locale = FALSE)