BAYESIAN SEX CALLER
A method and system for analyzing sex-chromosome aneuploidies of an individual are provided. In one embodiment, a method comprises training a neural network model based on predetermined information related to at least one sex chromosome. The method also comprises determining a respective sex-chromosome status based on a normalized read depth for a gene in a genome of the individual using a machine learning algorithm. The machine learning algorithm is configured to receive, as inputs, the normalized read depth, and output the respective sex-chromosome status of the individual. In another embodiment, a system I is provided including a neural network model trained based on predetermined information related to at least one sex chromosome and is adapted to determine a respective sex-chromosome status based on a normalized read depth for a gene in a genome of the individual using a machine learning algorithm.
1 . A method for analyzing sex-chromosome aneuploidies of an individual comprising:
training a neural network model based on predetermined information related to at least one sex chromosome;
determining the respective sex-chromosome status based on a normalized read depth for a gene in a genome of the individual using a machine learning algorithm,
wherein the machine learning algorithm is configured to receive, as inputs, the normalized read depth, and output the respective sex-chromosome status of the individual.
2 . The method of claim 1 wherein the operation of determining the respective sex-chromosome status is based on the normalized read depth and at least one of fetal fraction data and fold change data.
3 . The method of claim 1 wherein the method comprises providing a twin sex calling.
4 . The method of claim 3 wherein the twin sex calling comprises calling sexes among the following three phenotypes: two XX twins, two XY twins, and one XX twin and one XY twin.
5 . The method of claim 1 wherein the method comprises determining a complex sex phenotype.
6 . The method of claim 5 wherein the complex sex phenotype comprises at least one of the group comprising: vanishing twins and mosaic monosomy.
7 . The method of claim 1 wherein the method provides a negative result where the respective sex-chromosome status is determined to be anomalous.
8 . The method of claim 1 wherein the method determines the respective sex-chromosome status via Bayesian statistics of the read depth and allosome data.
9 . The method of claim 1 wherein the method determines the respective sex-chromosome status via graphing of the read depth and allosome data.
10 . The method of claim 9 wherein the operation of graphing comprises graphing a sample as a point in a two-dimensional plane.
11 . The method of claim 1 wherein the method determines the respective sex-chromosome status via visualization of the read depth and allosome data.
12 . The method of claim 11 wherein the visualization comprises graphing a sample as a point in a two-dimensional plane.
13 . The method of claim 1 wherein the method comprises determining a probability of the sex-chromosome status for each sample of a plurality of samples according to the following:
P (SCA|FF chrX , FF chrY , FF inferred , depth)∝ P (SCA) P (FF chrX , FF chrY , FF inferred , depth|SCA j ) (1).
14 . The method of claim 1 wherein the determination of sex-chromosome status comprises heuristic data analysis and expert human review as a truth set.
15 . The method of claims 1 wherein the predetermined information comprises human adjudicated sex-chromosome status.
16 . The method of claim 15 wherein the human adjudicate sex-chromosome status calls are performed when the method provides a negative result.
17 . The method of claim 1 wherein the operation of training comprises optimizing the Bayesian network model.
18 . The method of claim 17 wherein the operation of optimizing comprises adapting learning rates based on a first and second gradient momentum.
19 . The method of claim 1 wherein the operation of training comprises automated retraining protocols.
20 . The method of claim 19 wherein the automated retraining protocol is adapted to synchronize the operation of training over time.
21 . The method of any of claims 19 and 20 wherein the automated retraining protocol is adapted to reduce drift and repetitively validate performance over time.
22 . The method of claim 1 wherein a confidence level is determined for the respective sex-chromosome status.
23 . A system adapted to analyze sex-chromosome aneuploidies of an individual comprising:
a neural network model trained based on predetermined information related to at least one sex chromosome; the neural network model adapted to determine a respective sex-chromosome status based on a normalized read depth for a gene in a genome of the individual using a machine learning algorithm,
wherein the machine learning algorithm is configured to receive, as inputs, the normalized read depth, and output the respective sex-chromosome status of the individual.
24 . The system of claim 23 wherein the neural network is adapted to determine the respective sex-chromosome status is based on the normalized read depth and at least one of fetal fraction data and fold change data.
25 . The system of claim 23 wherein the neural network is adapted to provide a twin sex call.
26 . The system of claim 25 wherein the twin sex call comprises a call of sexes among the following three phenotypes: two XX twins, two XY twins, and one XX twin and one XY twin.
27 . The system of claim 23 wherein the neural network is adapted to determine a complex sex phenotype.
28 . The system of claim 27 wherein the complex sex phenotype comprises at least one of the group comprising: vanishing twins and mosaic monosomy.
29 . The system of claim 23 wherein the neural network is adapted to provide a negative result where the respective sex-chromosome status is determined to be anomalous.
30 . The system of claim 23 wherein the neural network is adapted to determine the respective sex-chromosome status via Bayesian statistics of the read depth and allosome data.
31 . The system of claim 23 wherein the method determines the respective sex-chromosome status via graphing of the read depth and allosome data.
32 . The system of claim 31 wherein the operation of graphing comprises graphing a sample as a point in a two-dimensional plane.
33 . The system of claim 23 wherein the neural network is adapted to determine the respective sex-chromosome status via visualization of the read depth and allosome data.
34 . The system of claim 33 wherein the visualization comprises graphing a sample as a point in a two-dimensional plane.
35 . The system of claim 23 wherein the neural network is adapted to determine a probability of the sex-chromosome status for each sample of a plurality of samples according to the following:
P (SCA|FF chrX , FF chrY , FF inferred , depth)∝ P (SCA) P (FF chrX , FF chrY , FF inferred , depth|SCA j ) (1)
36 . The system of claim 23 wherein the determination of sex-chromosome status comprises heuristic data analysis and expert human review as a truth set.
37 . The system of claims 23 wherein the predetermined information comprises human adjudicated sex-chromosome status.
38 . The system of claim 37 wherein the human adjudicate sex-chromosome status calls are performed when the method provides a negative result.
39 . The system of claim 23 wherein the neural network is adapted to train based on an optimization of the Bayesian network model.
40 . The system of claim 39 wherein the neural network is adapted to optimize based on an adaptation of learning rates based on a first and second gradient momentum.
41 . The system of claim 23 wherein the neural network is adapted to train based on automated retraining protocols.
42 . The system of claim 41 wherein the automated retraining protocol is adapted to synchronize the operation of training over time.
43 . The system of any of claims 41 and 42 wherein the automated retraining protocol is adapted to reduce drift and repetitively validate performance over time.
44 . The system of claim 1 wherein a confidence level is determined for the respective sex-chromosome status.