Multi-Omics Factor Analysis (MOFA)

记录一下今天下午EMBL组的Britta Velten下山来给我们讲多组学因子分析的工具(MOFA)。此人是EMBL Huber组的博后,他们实验室主要进行多组学分析和统计计算。今天她讲的这个工具是他们实验室开发的,综合多组学数据进行分析,致力于找到因子的最优组合,以表征疾病。

Integration of data from different omics is challenging


  1. Heterogeneous data sets have to be modeled under different statistical assumptions
  2. Appropriate regularization strategies are required to avoid over-fitting
  3. Approaches based on marginal correlations suffer from spurious associations and a high multiple testing burden
  4. Missing data is commonly encountered, both in terms of single values as well as individual samples that miss a certain assay.

Exploratory analysis: From single- to multi-omics

Principle component analysis (PCA) decomposes high-dimensional data Y into a low-dimensional representation by uncorrelated factors.

An extension to multi-omic data requires:

  1. Disentangling variation between different data sets:
    1. Which axes of variation are unique to a single data set?
    2. Which capture co-variation between different biological layers?
  2. Accounting for heterogeneity of different data sets
  3. Characterization and annotation of the major axes of variation

Multi-Omics Factor Analysis (MOFA)

MOFA is a computational method for discovering the principal sources of variation in a multi-omics dataset. MOFA infers a set of (hidden) factors that capture biological and technical sources of variability across a joint low-dimensional representation of multiple data sets, thereby enabling a variety of downstream analyses, including factor annotation, data imputation and the detection of outlier samples.

Important properties of MOFA for multi-omic applications:

  1. Interpretability by two-level sparsity
    1. factor- and view-wise sparsity by an ARD prior
    2. factor- and feature-wise sparsity by a spike and slab prior
  2. Scalable inference using a variational framework
  3. Handling of missing values
  4. Modeling non-Gaussian views



Multi-Omics factor analysis disentangles heterogeneity in blood cancer


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因为不想遗忘! 在这个信息大爆炸的年代,最重要的是对知识的消化-吸收-重铸。每天学了很多东西,但是理解的多少,以及能够运用多少是日后成功的关键。作为一个PhD,大脑中充斥了太多的东西,同时随着年龄的增长,难免会忘掉很多事情。所以只是为了在众多教程中写一个自己用到的,与自己...… Continue reading