Background::
The utilization of genetic data to investigate biological problems has recently
become a vital approach. However, it is undeniable that the heterogeneity of original samples at the
biological level is usually ignored when utilizing genetic data. Different cell-constitutions of a sample
could differentiate the expression profile, and set considerable biases for downstream research. Matrix
factorization (MF) which originated as a set of mathematical methods, has contributed massively to
deconvoluting genetic profiles in silico, especially at the expression level.
Objective:
With the development of artificial intelligence algorithms and machine learning, the
number of computational methods for solving heterogeneous problems is also rapidly abundant.
However, a structural view from the angle of using MF to deconvolute genetic data is quite limited.
This study was conducted to review the usages of MF methods on heterogeneous problems of genetic
data on expression level.
Methods:
MF methods involved in deconvolution were reviewed according to their individual
strengths. The demonstration is presented separately into three sections: application scenarios, method
categories and summarization for tools. Specifically, application scenarios defined deconvoluting
problem with applying scenarios. Method categories summarized MF algorithms contributed to
different scenarios. Summarization for tools listed functions and developed web-servers over the latest
decade. Additionally, challenges and opportunities of relative fields are discussed.
Results and Conclusion:
Based on the investigation, this study aims to present a relatively global
picture to assist researchers to achieve a quicker access of deconvoluting genetic data in silico, further
to help researchers in selecting suitable MF methods based on the different scenarios.
Sequential estimation of quantiles from delayed observations
