The 16S data from the Human Microbiome Project includes only the most diverse bodysites: Antecubital fossa (skin), Stool and Subgingival plaque (oral) and can be analysed using a multilevel approach to account for repeated measurements using our module mixMC. The data include 162 samples (54 unique healthy individuals) measured on 1,674 OTUs.
data(diverse.16S)A list containing two data sets, data.TSS and data.raw and some meta data information:
data.TSSdata frame with 162 rows (samples) and 1674 columns (OTUs). The prefiltered normalised data using Total Sum Scaling normalisation.
data.rawdata frame with 162 rows (samples) and 1674 columns (OTUs). The prefiltered raw count OTU data which include a 1 offset (i.e. no 0 values).
taxonomydata frame with 1674 rows (OTUs) and 6 columns indicating the taxonomy of each OTU.
indivdata frame with 162 rows indicating sample meta data.
bodysitefactor of length 162 indicating the bodysite with levels "Antecubital_fossa", "Stool" and "Subgingival_plaque".
samplevector of length 162 indicating the unique individual ID, useful for a multilevel approach to taken into account the repeated measured on each individual.
The data were downloaded from the Human Microbiome Project (HMP, http://hmpdacc.org/HMQCP/all/ for the V1-3 variable region). The original data contained 43,146 OTU counts for 2,911 samples measured from 18 different body sites. We focused on the first visit of each healthy individual and focused on the three most diverse habitats. The prefiltered dataset included 1,674 OTU counts. We strongly recommend to use log ratio transformations on the data.TSS normalised data, as implemented in the PLS and PCA methods, see details on www.mixOmics.org/mixMC.
The data.raw include a 1 offset in order to be log ratios transformed after TSS normalisation. Consequently, the data.TSS are TSS normalisation of data.raw.
The CSS normalisation was performed on the orignal data (including zero values)
L<U+00EA> Cao K.-A., Costello ME, Lakis VA, Bartolo, F,Chua XY, Brazeilles R, Rondeau P. MixMC: Multivariate insights into Microbial Communities. PLoS ONE, 11(8): e0160169 (2016).