Description Usage Format Source References Examples
Metadata information of CRC and control patients in
the five public studies used in Thomas et al. (2019). These were accessed
through curatedMetagenomicData
.
1 |
A data.frame
of per-sample metadata information
curatedMetagenomicData
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | data(CRC_meta)
# has CRC and control samples across five studies
table(CRC_meta$studyID, CRC_meta$study_condition)
# 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_meta <- data.frame(sample_data(physeq))
# CRC_meta$studyID <- factor(CRC_meta$studyID)
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