PT  - JOURNAL ARTICLE
AU  - Mancuso, Christopher A
AU  - Canfield, Jacob L
AU  - Singla, Deepak
AU  - Krishnan, Arjun
TI  - A Flexible, Interpretable, and Accurate Approach for Imputing the Expression of Unmeasured Genes
AID  - 10.1101/2020.03.30.016675
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.03.30.016675
4099  - http://biorxiv.org/content/early/2020/03/31/2020.03.30.016675.short
4100  - http://biorxiv.org/content/early/2020/03/31/2020.03.30.016675.full
AB  - While there are >2 million publicly-available human microarray gene-expression profiles, these profiles were measured using a variety of platforms that each cover a pre-defined, limited set of genes. Therefore, key to reanalyzing and integrating this massive data collection are methods that can computationally reconstitute the complete transcriptome in partially-measured microarray samples by imputing the expression of unmeasured genes. Current state-of-the-art imputation methods are tailored to samples from a specific platform and rely on gene-gene relationships regardless of the biological context of the target sample. We show that sparse regression models that capture sample-sample relationships (termed SampleLASSO), built on-the-fly for each new target sample to be imputed, outperform models based on fixed gene relationships. Extensive evaluation involving three machine learning algorithms (LASSO, k-nearest-neighbors, and deep-neural-networks), two gene subsets (GPL96-570 and LINCS), and three imputation tasks (within and across microarray/RNA-seq) establishes that SampleLASSO is the most accurate model. Additionally, we demonstrate the biological interpretability of this method by showing that, for imputing a target sample from a certain tissue, SampleLASSO automatically leverages training samples from the same tissue. Thus, SampleLASSO is a simple, yet powerful and flexible approach for harmonizing large-scale gene-expression data.