CRC_abd: Species level feature abundance data of five public CRC...

Description Usage Format Source References Examples

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

1

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

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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

MMUPHin documentation built on April 9, 2021, 6:01 p.m.