AD_data | R Documentation |
This study explored the microbial indicators that could improve the efficacy of anaerobic digestion (AD) bioprocess and prevent its failure. The samples were treated with two different ranges of phenol concentration (effect of interest) and processed at five different dates (batch effect). This study includes a clear and strong batch effect with an approx. balanced batch x treatment design.
data('AD_data')
A list containing three TreeSummarizedExperiment objects
FullData
, EgData
and CorrectData
:
A TreeSummarizedExperiment object containing the counts of 75 samples and 567 OTUs. The meta data information of each sample is stored in the rowData, while the taxonomy of each OTU is stored in the colData.
A TreeSummarizedExperiment object containing the values of 75
samples and 231 OTUs filtered and centered log ratio transformed from the
FullData
with raw counts.The rowData includes Y.trt
and
Y.bat
. Y.trt
is the effect of interest, which is a factor of
phenol concentrations for each sample in the AD study; Y.bat
is the
batch effect, which is a factor of sample processing dates for each sample.
The taxonomy of each OTU is stored in the colData. The rowTree is built based
on the Y.bat
.
A TreeSummarizedExperiment object containing seven datasets before or after batch effect correction using different methods. Each assay includes 75 samples and 231 OTUs.
None.
The raw data were provided by Dr. Olivier Chapleur and published at the referenced article. Filtering and normalisation described in our package vignette.
chapleur2016increasingPLSDAbatch
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