CRC_abd {MMUPHin} | R Documentation

## Species level feature abundance data of five public CRC studies

### Description

Species level relative abundance profiles of CRC and control patients in the five public studies used in Thomas et al. (2019). These were accessed through `curatedMetagenomicData`.

### Usage

    data(CRC_abd)

### Format

A feature-by-sample `matrix` of species-level profiles

### Source

`curatedMetagenomicData`

### References

Thomas, Andrew Maltez, Paolo Manghi, Francesco Asnicar, Edoardo Pasolli, Federica Armanini, Moreno Zolfo, Francesco Beghini et al. "Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation." Nature medicine 25, no. 4 (2019): 667.

### Examples

    data(CRC_abd)
    # features included
    rownames(CRC_abd)
    # These are relative abundances
    apply(CRC_abd, 2, sum)
    # The following were used to generate the object
    # library(curatedMetagenomicData)
    # library(phyloseq)
    # library(genefilter)
    # datasets <- curatedMetagenomicData(
    #   c("FengQ_2015.metaphlan_bugs_list.stool"  ,
    #     "HanniganGD_2017.metaphlan_bugs_list.stool",
    #     "VogtmannE_2016.metaphlan_bugs_list.stool",
    #     "YuJ_2015.metaphlan_bugs_list.stool",
    #     "ZellerG_2014.metaphlan_bugs_list.stool"),
    #   dryrun = FALSE)
    # Construct phyloseq object from the five datasets
    # physeq <-
        # Aggregate the five studies into ExpressionSet
    #   mergeData(datasets) %>%
        # Convert to phyloseq object
    #   ExpressionSet2phyloseq() %>%
        # Subset samples to only CRC and controls
    #   subset_samples(study_condition %in% c("CRC", "control")) %>%
        # Subset features to species
    #   subset_taxa(!is.na(Species) & is.na(Strain)) %>%
        # Normalize abundances to relative abundance scale
    #   transform_sample_counts(function(x) x / sum(x)) %>%
        # Filter features to be of at least 1e-5 relative abundance in five
        # samples
    #   filter_taxa(kOverA(5, 1e-5), prune = TRUE)
    # CRC_abd <- otu_table(physeq)@.Data

[Package MMUPHin version 1.1.0 Index]