Systems and methods for using a convolutional neural network to detect contamination

A method of determining a contamination status of a test biological sample obtained from a test subject, comprising:

• (a) obtaining, in electronic format, one or more training subject datasets, each training subject dataset comprising a corresponding training variant allele frequency of each respective training single nucleotide variant in a plurality of training single nucleotide variants;
• (b) training a computational neural net based on the one or more training subject datasets, wherein the computational neural net comprises a pre-trained convolutional neural net and an untrained classifier;
• (c) obtaining, in electronic format, a test subject dataset comprising a corresponding test variant allele frequency of each respective test single nucleotide variant in a plurality of test single nucleotide variants;
• and
• (d) determining the contamination status for the test biological sample based on the trained computational neural net and the test subject dataset.

A system for determining a contamination status of a test biological sample obtained from a test subject:

• a storage device that stores instructions;
• and
• at least one processor that executes the instructions in order for:
• (a) obtaining, in electronic format, one or more training subject datasets, each training subject dataset comprising a corresponding training variant allele frequency of each respective training single nucleotide variant in a plurality of training single nucleotide variants;
• (b) training a computational neural net based on the one or more training subject datasets, wherein the computational neural net comprises a pre-trained convolutional neural net and an untrained classifier;
• (c) obtaining, in electronic format, a test subject dataset comprising a corresponding test variant allele frequency of each respective test single nucleotide variant in a plurality of test single nucleotide variants;
• and
• (d) determining the contamination status for the test biological sample based on the trained computational neural net and the test subject dataset.

A non-transitory computer-readable medium storing instructions for determining a contamination status of a test biological sample obtained from a test subject comprising:

• (a) obtaining, in electronic format, one or more training subject datasets, each training subject dataset comprising a corresponding training variant allele frequency of each respective training single nucleotide variant in a plurality of training single nucleotide variants;
• (b) training a computational neural net based on the one or more training subject datasets, wherein the computational neural net comprises a pre-trained convolutional neural net and an untrained classifier;
• (c) obtaining, in electronic format, a test subject dataset comprising a corresponding test variant allele frequency of each respective test single nucleotide variant in a plurality of test single nucleotide variants;
• and
• (d) determining the contamination status for the test biological sample based on the trained computational neural net and the test subject dataset.