CRC_abd {MMUPHin} | R Documentation |
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
.
data(CRC_abd)
A feature-by-sample matrix
of species-level profiles
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.
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