US 2021/0174898 A1 · SYSTEMS AND METHODS FOR AUTOMATING RNA EXPRESSION CALLS IN A CANCER PREDICTION PIPELINE

Abstract

Systems and methods are provided for performing quality control analysis. The method obtains, in electronic form, a batch dataset comprising, for each respective sample in a batch of samples, a corresponding plurality of sequence reads derived from the respective sample by targeted or whole transcriptome RNA sequencing and corresponding metadata for the respective sample. The method determines for the batch dataset a cohort-matched reference batch, where the cohort-matched reference batch is balanced for tissue site, tumor purity, cancer type, sequencer identity, or date sequenced. The method performs one or more global batch quality control tests on the batch dataset using at least the cohort-matched reference batch. The method removes respective samples from the batch dataset that fail any one of the one or more global batch quality control tests or flagging for manual inspection respective samples that fail any one of the one or more global batch quality control tests.

Claims61

Dependent claims

1-25. (canceled)

A method of performing quality control, the method comprising:

at a computer system having one or more processors, and memory storing one or more programs for execution by the one or more processors:
a) obtaining, in electronic form, a batch dataset comprising, for each respective test sample in a batch of test samples,
a corresponding expression profile comprising a corresponding gene expression value for each respective gene in a first set of genes, wherein generating the expression dataset comprises:
obtaining, in electronic form, a corresponding plurality of at least 10,000 sequence reads derived from the respective test sample by RNA sequencing;
and
determining, from the corresponding plurality of at least 10,000 sequence reads, the corresponding gene expression value for each respective gene in the first set of genes, and
a corresponding set of metadata comprising values for each respective characteristic in a first set of characteristics about the respective test sample;
b) determining for the batch dataset, a cohort-matched reference dataset by selecting, from a reference database comprising a plurality of reference database samples, a subset of the reference database samples thereby identifying a plurality of cohort-matched reference samples, wherein:
the cohort-matched reference dataset comprises, for each respective cohort-matched reference sample in the plurality of cohort-matched reference samples, a corresponding expression profile comprising a corresponding gene expression value for each respective gene in the first set of genes,
each respective cohort-matched reference sample in the plurality of cohort-matched reference samples is associated with a corresponding set of metadata comprising a corresponding value for each respective characteristic in a second set of characteristics about the respective reference sample, wherein the second set of characteristics comprises a third set of one or more characteristics that are also present in the first set of characteristics, and
for each respective characteristic in the third set of one or more characteristics, the aggregate values for the respective characteristic are balanced between the batch dataset and the cohort-matched reference dataset;
c) performing dimension reduction on a combined dataset comprising the corresponding expression profile for each respective test sample in the plurality of test samples and the corresponding expression profile for each respective cohort-matched reference sample in the plurality of cohort-matched reference samples, thereby obtaining for each respective test sample and each respective cohort-matched reference sample, a corresponding set of coordinates embedded in a lower dimensional-space than the dimensions of the corresponding expression profile;
and
d) determining a statistical measure of similarity between the sets of coordinates obtained for the test samples and the sets of coordinates obtained for the cohort-matched reference samples;
and
e) comparing the statistical measure of similarity to a threshold value, and
validating the batch dataset for reporting when the statistical measure of similarity satisfies the threshold value, or
not validating the batch dataset for reporting when the statistical measure of similarity does not satisfy the threshold value.

15 more elements

A method of validating a change in an RNA expression pipeline, the method comprising:

at a computer system having one or more processors, and memory storing one or more programs for execution by the one or more processors:
a) obtaining, in electronic form, a batch dataset comprising, for each respective test sample in a batch of test samples,
a corresponding expression profile prepared using a first methodology, the corresponding expression profile comprising a corresponding gene expression value for each respective gene in a first set of genes, wherein generating the expression profile comprises:
obtaining, in electronic form, a corresponding plurality of at least 10,000 sequence reads derived from the respective test sample by RNA sequencing;
and
determining, from the corresponding plurality of at least 10,000 sequence reads, a corresponding gene expression value for each respective gene in the first set of genes, and
a corresponding set of metadata comprising values for each respective characteristic in a first set of characteristics about the respective test sample;
b) determining for the batch dataset, a cohort-matched reference dataset by selecting, from the reference database, a subset of the reference database samples thereby identifying a plurality of cohort-matched reference samples, wherein:
the cohort-matched reference dataset comprises, for each respective cohort-matched reference sample in the plurality of cohort-matched reference samples, a corresponding expression profile comprising a corresponding gene expression value for each respective gene in the first set of genes prepared using a second methodology that is different than the first methodology,
each respective cohort-matched reference sample in the plurality of cohort-matched reference samples is associated with a corresponding set of metadata comprising a corresponding value for each respective characteristic in a second set of characteristics about the respective reference sample, wherein the second set of characteristics comprises a third set of one or more characteristics that are also present in the first set of characteristics, and
for each respective characteristic in the third set of one or more characteristics, the aggregate values for the respective characteristic are balanced between the batch dataset and the cohort-matched reference dataset;
c) performing dimension reduction on a combined dataset comprising the corresponding expression profile for each respective test sample in the plurality of test samples and the corresponding expression profile for each respective cohort-matched reference sample in the plurality of cohort-matched reference samples, thereby obtaining for each respective test sample and each respective cohort-matched reference sample, a corresponding set of coordinates embedded in a lower dimensional-space than the dimensions of the corresponding expression profile;
and
d) determining a statistical measure of similarity between the sets of coordinates obtained for the test samples and the sets of coordinates obtained for the cohort-matched reference samples;
and
e) comparing the statistical measure of similarity to a threshold value, and
validating the change in the RNA expression pipeline when the statistical measure of similarity satisfies the threshold value, or
not validating the change in the RNA expression pipeline when the statistical measure of similarity satisfies the threshold value.

15 more elements

A method of adding RNA expression data to a reference database comprising a plurality of reference database samples, the method comprising:

at a computer system having one or more processors, and memory storing one or more programs for execution by the one or more processors:
a) obtaining, in electronic form, an expression dataset comprising, for each respective test sample in a plurality of test samples,
a corresponding expression profile comprising a corresponding gene expression value for each respective gene in a first set of genes, wherein generating the expression profile comprises:
obtaining, in electronic form, a corresponding plurality of at least 10,000 sequence reads derived from the respective test sample by RNA sequencing;
and
determining, from the corresponding plurality of at least 10,000 sequence reads, a corresponding gene expression value for each respective gene in the first set of genes, and
a corresponding set of metadata comprising values for each respective characteristic in a first set of characteristics about the respective test sample;
b) determining for the expression dataset, a cohort-matched reference dataset by selecting, from the reference database, a subset of the reference database samples thereby identifying a plurality of cohort-matched reference samples, wherein:
the cohort-matched reference dataset comprises, for each respective cohort-matched reference sample in the plurality of cohort-matched reference samples, a corresponding expression profile comprising a corresponding gene expression value for each respective gene in the first set of genes,
each respective cohort-matched reference sample in the plurality of cohort-matched reference samples is associated with a corresponding set of metadata comprising a corresponding value for each respective characteristic in a second set of characteristics about the respective reference sample, wherein the second set of characteristics comprises a third set of one or more characteristics that are also present in the first set of characteristics, and
for each respective characteristic in the third set of one or more characteristics, the aggregate values for the respective characteristic are balanced between the batch dataset and the cohort-matched reference dataset;
c) performing dimension reduction on a combined dataset comprising the corresponding expression profile for each respective test sample in the plurality of test samples and the corresponding expression profile for each respective cohort-matched reference sample in the plurality of cohort-matched reference samples, thereby obtaining for each respective test sample and each respective cohort-matched reference sample, a corresponding set of coordinates embedded in a lower dimensional-space than the dimensions of the corresponding expression profile;
and
d) determining a statistical measure of similarity between the sets of coordinates obtained for the test samples and the sets of coordinates obtained for the cohort-matched reference samples;
and
e) comparing the statistical measure of similarity to a threshold value, and
adding the expression data set to the reference database when the statistical measure of similarity satisfies the threshold value, or
when the statistical measure of similarity does not satisfy the threshold value:
determining a set of conversion factors for standardizing the expression profiles in the new expression data set against expression profiles in the reference database,
standardizing the expression profiles in the new expression data set using the set of conversion factors, thereby obtaining a standardized new expression data set, and
adding the standardized new expression data set to the reference database.

18 more elements

Dependencies are inferred from the claim text.

Systems and methods for automating rna expression calls in a cancer prediction pipeline

Life of this patent

Systems and methods for automating rna expression calls in a cancer prediction pipeline

Life of this patent

Abstract

Claims61