FIELD: microbiology.
SUBSTANCE: present set of inventions relates to microbiology. Disclosed is a method for correcting undesirable batch effects in microbiome data in which the data about the composition of the microbiota of a set of samples of biological material is obtained by sequencing them and the value of at least one covariance parameter for each sample; the relative representation of micro-organisms and/or their genes is determined; an artificial neural network autoencoder is taught, which receives at the input relative representation of micro-organisms and/or their genes, wherein at least one additional neuron is added to the encoding layer of the autoencoder, in which the value of the covariance parameter for each sample is given; the correction of the relative representation of micro-organisms and/or their genes in microbiota is carried out by introducing a standard value of a covariance parameter to the coding layer taught at the preceding step. A system and a machine-readable carrier are also provided for carrying out said method.
EFFECT: said set of inventions enables improving accuracy and quality of error correction by the analysis and interpretation of data about the composition of microbiota and therefore more accurate determination of a relative representation of micro-organisms in samples.
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Authors
Dates
2021-02-01—Published
2019-10-18—Filed