predict.topics: Predict topic functional content

In EESI/themetagenomics: Exploring Thematic Structure and Predicted Functionality of 16s rRNA Amplicon Data

Description

Given an object of class topics, this function predicts the functional content using PICRUSt or tax4fun precalculated mapping tables that maps the taxonomic abundance for a given OTU to functional abundance content across a set of functional genes.

Usage

## S3 method for class 'topics'
predict(
  object,
  reference = c("gg_ko", "gg_cog", "silva_ko"),
  reference_path,
  scalar = 100,
  cn_normalize = FALSE,
  sample_normalize = FALSE,
  drop = TRUE,
  ...
)

Arguments

object	(required) Output of find_topics.
reference	A string for either gg_ko, gg_cog, or silva_ko. Defaults to gg_ko.
reference_path	Folder path of the reference file
scalar	Value for scaling the topics over taxa distribution to predicted counts. Defaults to 100.
cn_normalize	Logical flag for performing 16S rRNA copy number normalization. Defaults to FALSE.
sample_normalize	Logical flag to normalize functional predictions by the total functional abundance in a sample. Defaults to FALSE.
drop	Logical flag to drop empty gene columns. Defaults to TRUE.
...	Additional arguments for t4f method.

Value

An object of class functions containing

fxn_table

A matrix of gene counts across topics.

fxn_meta

A list of functional metadata corresponding to fxn_table.

method_meta

A matrix of method specific metadata.

References

ABhauer, K. P., Wemheuer, B. Daniel, R., and Meinicke, P. (2015). Bioinformatics, 1-3. 31(17).

Langille, M. G.I.*, Zaneveld, J.*, Caporaso, J. G., McDonald, D., Knights, D., a Reyes, J., Clemente, J. C., Burkepile, D. E., Vega Thurber, R. L., Knight, R., Beiko, R. G., and Huttenhower, C. (2013). Nature Biotechnology, 1-10. 8.

See Also

download_ref picrust t4f

Examples

formula <- ~DIAGNOSIS
refs <- 'Not IBD'

dat <- prepare_data(otu_table=GEVERS$OTU,rows_are_taxa=FALSE,tax_table=GEVERS$TAX,
                    metadata=GEVERS$META,formula=formula,refs=refs,
                    cn_normalize=TRUE,drop=TRUE)

## Not run: 
topics <- find_topics(dat,K=15)

download_ref(destination='/references',reference='gg_ko')
functions <- predict(topics,reference='gg_ko',
                     reference_path='/references')

## End(Not run)

EESI/themetagenomics documentation built on May 10, 2020, 1:40 a.m.