Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----hiddensetup, echo=FALSE, message=FALSE-----------------------------------
library(knitr)
library(rmarkdown)
doctype <- opts_knit$get("rmarkdown.pandoc.to")
## ----setup, echo=TRUE, message = FALSE----------------------------------------
library(microbiomeExplorer)
## ----eval=FALSE,echo=TRUE-----------------------------------------------------
# runMicrobiomeExplorer()
## ----eval=TRUE, echo=TRUE-----------------------------------------------------
data("mouseData", package = "metagenomeSeq")
meData <- filterMEData(mouseData,minpresence = 1, minfeats = 2, minreads = 2)
## ----eval=TRUE, echo=TRUE, warning=FALSE--------------------------------------
makeQCPlot(meData, col_by = "diet",
log = "none",
filter_feat = 101,
filter_read = 511,
allowWebGL = FALSE)
plotlySampleBarplot(meData,
col_by = "diet")
## ----eval=TRUE, echo=TRUE-----------------------------------------------------
meData <- filterMEData(mouseData,minpresence = 1, minfeats = 100, minreads = 500)
## ----eval=TRUE, echo=TRUE-----------------------------------------------------
meData <- normalizeData(meData,norm_method = "Proportion")
## ----eval=TRUE, echo=TRUE-----------------------------------------------------
new_pheno <- interaction(pData(meData)[,c("mouseID","relativeTime")])
mutatedRows <- row.names(pData(meData))
mutatedData <- dplyr::mutate(pData(meData), "mouse_time" = new_pheno)
row.names(mutatedData) <- mutatedRows
meData <- addPhenoData(meData,mutatedData)
## ----eval=TRUE, echo=TRUE-----------------------------------------------------
bufcolnames <- names(fData(meData))
df <- as.data.frame(t(apply(fData(meData),1, rollDownFeatures)))
names(df) <- bufcolnames
meData <- addFeatData(meData,df)
## ----eval=TRUE, echo=TRUE-----------------------------------------------------
aggDat <- aggFeatures(meData, level = "genus")
## ----eval=TRUE, echo=TRUE, warning=FALSE--------------------------------------
plotAbundance(aggDat,
level = "genus",
x_var = "diet",
facet1 = NULL,
facet2 = NULL,
ind = 1:10,
plotTitle = "Top 10 feature percentage at genus level",
ylab = "Percentage")
## ----eval=TRUE, echo=TRUE-----------------------------------------------------
plotSingleFeature(aggDat,
x_var = "diet",
ind = 1:10,
plotTitle = "Percentage of Enterococcus",
facet1 = NULL,
facet2 = NULL,
feature = "Enterococcus",
ylab = "Percentage",
log = TRUE,
showPoints = TRUE)
## ----eval=TRUE, echo=TRUE-----------------------------------------------------
plotAlpha(aggDat,
level = "genus",
index = "shannon",
x_var = "diet",
facet1 = NULL,
facet2 = NULL,
col_by = "mouseID",
plotTitle = "Shannon diversity index at genus level")
## ----eval=TRUE, echo=TRUE, message = FALSE, warning = FALSE-------------------
distMat <- computeDistMat(aggDat, "bray")
pcaVals <- calculatePCAs(distMat,
c("PC1", "PC2"))
plotBeta(aggDat,
dist_method = "bray",
pcas = pcaVals,
dim = c("PC1", "PC2"),
col_by = "diet",
shape_by = NULL,
plotTitle = "Bray-Curtis diversity at genus level",
pt_size = "6",
plotText = "R2: 0.478; Pr(>F): 0.002",
confInterval = 0.95,
allowWebGL = FALSE)
## ----eval=TRUE, echo=TRUE, warning = FALSE, fig.width = 8, fig.height = 10----
plotHeatmap(aggDat,
features = NULL,
log = TRUE,
sort_by = "Variance",
nfeat = 50,
col_by = c("diet"),
row_by = "",
plotTitle = "Top 50 features sorted by Variance at genus level")
## ----eval=TRUE, echo=TRUE, warning = FALSE, message = FALSE-------------------
cf <- corrFeature(aggDat,
feat1 = "Bacteroides",
feat2 = "Prevotella",
log = TRUE,
facet1 = "diet",
facet2 = NULL,
method = "spearman",
plotTitle = "Spearman correlation of Bacteroides vs Prevotella split by diet",
col_by = "status",
allowWebGL = FALSE)
## ----eval=TRUE, echo=TRUE, warning = FALSE, message = FALSE-------------------
diffResults <- runDiffTest(aggDat,
level = "genus",
phenotype = "diet",
phenolevels = c("BK", "Western"),
method = "DESeq2")
kable(head(diffResults))
## ----eval=TRUE, echo=TRUE, warning = FALSE, fig.width = 8, fig.height = 10----
plotLongFeature(aggDat,
x_var = "date",
id_var = "mouseID",
plotTitle = "Abundance of Prevotella",
feature = "Prevotella",
ylab = "Reads",
log = TRUE,
x_levels = c("2007-12-11","2008-01-21","2008-02-11","2008-02-25"))
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