QUANTIFYING EFFECTS OF SEQUENCING VARIANCE ON CLASSIFICATION

1 . A computer-implemented quality control method for predicting whether a continuous value classifier will generate discordant classifications of a sample, comprising:

receiving, via one or more processors, transcriptomic data including a plurality of observed gene expression values each corresponding to one of a plurality of samples sequenced a plurality of times;

generating, via a machine learning model, a predicted standard deviation value for each of the plurality of observed gene expression values;

for each sample in the plurality of samples:

computing, for each simulation in a plurality of simulations, and for each of a plurality of genes, a respective simulated expression value, by drawing from a normal distribution based on an observed expression value for the gene and the predicted standard deviation value for the gene;

classifying, via one or more processors, the simulated expression values using the classifier, wherein classifying the simulated expression values includes counting a number of classifications;

computing, via one or more processors, a confidence score; and

when the confidence score is within a predetermined range, flagging the sample as a potentially discordant sample; and

storing, in an electronic database, at least one of the confidence scores in association with the transcriptomic data.

2 . The computer-implemented method of claim 1 , further comprising:

generating, via one or more processors, a static digital report including at least one classification result.

3 . The computer-implemented method of claim 2 , wherein generating the static digital report includes censoring a no-call classification result.

4 . The computer-implemented method of claim 1 , further comprising:

receiving, via an application programming interface, a request for RNA sequence data;

processing, via one or more processors, the request using a rules engine to determine an identity of a sender of the request;

determining, via one or more processors, a role of the identity of the sender of the request;

configuring, via one or more processors, a rules engine based on the role of the identity of the sender of the request;

generating, via one or more processors, modified sequence data by processing the sequence data using the configured rules engine; and

transmitting, via electronic network, the modified sequence data to a computing device of the sender of the request.

5 . The computer-implemented method of claim 4 , wherein generating the modified sequence data by processing the sequence data using the configured rules engine includes generating censored results when the role of the identity of the sender of the request is a patient.

6 . The computer-implemented method of claim 4 , wherein generating the modified sequence data by processing the sequence data using the configured rules engine includes generating annotated results when the role of the identity of the sender of the request is a clinician.

7 . The computer-implemented method of claim 4 ,

wherein the method is performed automatically as part of a bioinformatics pipeline;

wherein the role of the sender of the request is a programmatic script; and

wherein generating the modified sequence data by processing the sequence data includes programmatically generating a structured output corresponding to the modified sequence data,

wherein the structured output includes the modified sequence data and one or more confidence scores corresponding to the modified sequence data.

8 . The computer-implemented method of claim 1 , wherein the number of classifications is two or more.

9 . The computer-implemented method of claim 1 , further comprising:

determining, based on flagging the sample as the potentially discordant sample, an alternative therapy that may be appropriate for a patient corresponding to the sample.

10 . The computer-implemented method of claim 9 , wherein the alternative therapy includes at least one of: (i) a first-line therapy or (ii) a second-line therapy.

11 . The computer-implemented method of claim 9 , wherein the alternative therapy includes at least one of:

(i) a chemotherapeutic drug therapy,

(ii) a radiation therapy,

(iii) a surgery therapy,

(iv) a therapy in a different class,

(v) a molecular inhibitor therapy,

(vi) an antibody therapy,

(vii) a recombinant nucleic acid therapy,

(viii) an engineered immune cell therapy,

(ix) a checkpoint inhibitor therapy,

(x) a cytokine therapy,

(xi) a cancer treatment vaccine therapy,

(xii) a CAR-cell therapy; or

(xiii) an oncolytic virus therapy.

12 . The computer-implemented method of claim 1 , wherein the machine learning model is at least one of a Bayesian model, a supervised machine learning model or an unsupervised machine learning model.

13 . A computing system, comprising:

one or more processors; and

one or more memories, having stored thereon instructions that when executed, cause the computing system to:

receive, via the one or more processors, transcriptomic data including a plurality of observed gene expression values each corresponding to one of a plurality of samples sequenced a plurality of times;

generate, via a machine learning model, a predicted standard deviation value for each of the plurality of observed gene expression values;

for each sample in the plurality samples:

compute, for each simulation in a plurality of simulations, and for each of a plurality of genes, a respective simulated expression value, by drawing from a normal distribution based on an observed expression value for the gene and the predicted standard deviation value for the gene;

classify, via the one or more processors, the simulated expression values using the classifier, wherein classifying the simulated expression values includes counting a number of classifications;

compute, via the one or more processors, a confidence score; and

when the confidence score is within a predetermined range, flag the sample as a potentially discordant sample; and

store, in an electronic database, at least one of the confidence scores in association with the transcriptomic data.

14 . The computing system of claim 13 , the memory having stored thereon instructions that when executed, cause the computing system to:

generate, via one or more processors, a static digital report including at least one classification result.

15 . The computing system of claim 13 , the memory having stored thereon instructions that when executed, cause the computing system to:

receive, via an application programming interface, a request for RNA sequence data;

process, via one or more processors, the request using a rules engine to determine an identity of a sender of the request;

determine, via one or more processors, a role of the identity of the sender of the request;

configure, via one or more processors, a rules engine based on the role of the identity of the sender of the request;

generate, via one or more processors, modified sequence data by processing the sequence data using the configured rules engine; and

transmit, via electronic network, the modified sequence data to a computing device of the sender of the request.

16 . The computing system of claim 13 , the memory having stored thereon instructions that when executed, cause the computing system to:

generate censored results when a role of an identity of a sender of a request is a patient.

17 . The computing system of claim 13 , the memory having stored thereon instructions that when executed, cause the computing system to:

generate annotated results when a role of an identity of a sender of a request is a clinician.

18 . The computing system of claim 13 , wherein the number of classifications is two or more.

19 . The computing system of claim 13 , the memory having stored thereon instructions that when executed, cause the computing system to:

determine, based on flagging the sample as the potentially discordant sample, an alternative therapy that may be appropriate for a patient corresponding to the sample.

20 . A computer-readable media having stored thereon a set of non-transitory computer-readable instructions that, when executed, cause a computer to:

receive, via one or more processors, transcriptomic data including a plurality of observed gene expression values each corresponding to one of a plurality of samples sequenced a plurality of times;

generate, via a machine learning model, a predicted standard deviation value for each of the plurality of observed gene expression values;

for each sample in the plurality samples:

compute, for each simulation in a plurality of simulations, and for each of a plurality of genes, a respective simulated expression value, by drawing from a normal distribution based on an observed expression value for the gene and the predicted standard deviation value for the gene;

classify, via one or more processors, the simulated expression values using the classifier, wherein classifying the simulated expression values includes counting a number of classifications;

compute, via one or more processors, a confidence score; and

when the confidence score is within a predetermined range, flag the sample as a potentially discordant sample; and

store, in an electronic database, at least one of the confidence scores in association with the transcriptomic data.