Negahban, S. | bioRxiv Science

bioRxiv2023

Rescuing missing data in connectome-based predictive modeling

Recent evidence suggests brain-behavior predictions may require very large sample sizes. However, as the sample size increases, the amount of missing data also increases. Conventional methods, like complete-case analysis, discard useful information and shrink the sample size. To address the missing data problem, we investigated rescuing these missing data through imputation. Imputation is the substitution of estimated values for missing data to be used in downstream analyses. We integrated imputation methods into the Connectome-based Predictive Modeling (CPM) framework. Utilizing four open-source datasets--the Human Connectome Project, the Philadelphia Neurodevelopmental Cohort, the UCLA Consortium for Neuropsychiatric Phenomics, and the Healthy Brain Network (HBN)--we validated and compared our framework with different imputation methods against complete-case analysis for both missing connectomes and missing phenotypic measures scenarios. Imputing connectomes exhibited superior prediction performance on real and simulated missing data as compared to complete-case analysis. In addition, we found that imputation accuracy was a good indicator for choosing an imputation method for missing phenotypic measures but not informative for missing connectomes. In a real-world example predicting cognition using the HBN, we rescued 628 individuals through imputation, doubling the complete case sample size and increasing explained variance by 45%. Together, our results suggest that rescuing data with imputation, as opposed to discarding subjects with missing information, improves prediction performance.

neuroscience↗

bioRxiv2021

A Network Propagation Approach to Prioritize Long Tail Genes in Cancer

IntroductionThe diversity of genomic alterations in cancer pose challenges to fully understanding the etiologies of the disease. Recent interest in infrequent mutations, in genes that reside in the "long tail" of the mutational distribution, uncovered new genes with significant implication in cancer development. The study of these genes often requires integrative approaches with multiple types of biological data. Network propagation methods have demonstrated high efficacy in uncovering genomic patterns underlying cancer using biological interaction networks. Yet, the majority of these analyses have focused their assessment on detecting known cancer genes or identifying altered subnetworks. In this paper, we introduce a network propagation approach that entirely focuses on long tail genes with potential functional impact on cancer development. ResultsWe identify sets of often overlooked, rarely to moderately mutated genes whose biological interactions significantly propel their mutation-frequency-based rank upwards during propagation in 17 cancer types. We call these sets "upward mobility genes" (UMGs, 28-83 genes per cancer type) and hypothesize that their significant rank improvement indicates functional importance. We report new cancer-pathway associations based on UMGs that were not previously identified using driver genes alone, validate UMGs role in cancer cell survival in vitro--alone and compared to other network methods--using extensive genome-wide RNAi and CRISPR data repositories, and further conduct in vitro functional screenings resulting the validation of 8 previously unreported genes. ConclusionOur analysis extends the spectrum of cancer relevant genes and identifies novel potential therapeutic targets.

bioinformatics↗