Nothing
#### this demo shows a short workflow that could lead to an elaborate analysis.
#### first we create a file of metagenome IDs.
#### a user would have their own, in a real application.
writeLines (colnames (xx1), "my_ids.txt")
readLines ("my_ids.txt")
#### annotation data is easy to retrieve.
zz <- biomRequest (file="my_ids.txt", group_level="level2", evalue=1)
#### a log (or other) transformation is often a good idea.
zz0 <- transform (zz, t_Log)
#### metadata is easy to inspect, as follows.
columns (zz0, "host_common_name|samp_store_temp|material")
#### metadata of interest can then be incorporated into a plot.
princomp (zz0, map=c(col="host_common_name", pch="samp_store_temp"), labels="$$pubmed_id", cex=2)
#### next, a grouped distance calculation.
distx (zz0, groups="$$host_common_name")
#### annotations providing significant group differentiation can be identified.
pp <- (rowstats (zz0, groups="$$material") $ p.value < 0.05)
pp[is.na(pp)] <- FALSE
pp
#### that information can be used to make an informative heatmap.
image (zz0 [pp,], margins=c(5,10), cexRow=0.3)
#### for comparison, here is the same heatmap, but including all annotations.
image (zz0, margins=c(5,10), cexRow=0.3)
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