Chang, H. H. | bioRxiv Science

bioRxiv2020

Learning from local to global - an efficient distributed algorithm for modeling time-to-event data

ObjectivesWe developed and evaluated a privacy-preserving One-shot Distributed Algorithm to fit a multi-center Cox proportional hazard model (ODAC) without sharing patient-level information across sites. MethodsUsing patient-level data from a single site combined with only aggregated information from other sites, we constructed a surrogate likelihood function, approximating the Cox partial likelihood function obtained using patient-level data from all sites. By maximizing the surrogate likelihood function, each site obtained a local estimate of the model parameter, and the ODAC estimator was constructed as a weighted average of all the local estimates. We evaluated the performance of ODAC with (1) a simulation study, and (2) a real-world use case study using four datasets from the Observational Health Data Sciences and Informatics (OHDSI) network. ResultsOur simulation study showed that ODAC provided estimates nearly the same as the estimator obtained by analyzing, in a single dataset, the combined patient-level data from all sites (i.e., the pooled estimator). The relative bias was less than 0.1% across all scenarios. The accuracy of ODAC remained high across different sample sizes and event rates. On the other hand, the metaanalysis estimator, which was obtained by the inverse variance weighted average of the sitespecific estimates, had substantial bias when the event rate is less than 5%, with the relative bias reaching 20% when the event rate is 1%. In the OHDSI network application, the ODAC estimates have a relative bias less than 5% for 15 out of 16 log hazard ratios; while the meta-analysis estimates had substantially higher bias than ODAC. ConclusionsODAC is a privacy-preserving and non-iterative method for implementing time-to-event analyses across multiple sites. It provides estimates on par with the pooled estimator and substantially outperforms the meta-analysis estimator when the event is uncommon, making it extremely suitable for studying rare events and diseases in a distributed manner.

bioinformatics↗

bioRxiv2020

Cell segmentation using deep learning: comparing label and label-free approaches using hyper-labeled image stacks

Deep learning provides an opportunity to automatically segment and extract cellular features from high-throughput microscopy images. Many labeling strategies have been developed for this purpose, ranging from the use of fluorescent markers to label-free approaches. However, differences in the channels available to each respective training dataset make it difficult to directly compare the effectiveness of these strategies across studies. Here we explore training models using subimage stacks composed of channels sampled from larger, hyper-labeled, image stacks. This allows us to directly compare a variety of labeling strategies and training approaches on identical cells. This approach revealed that fluorescence-based strategies generally provide higher segmentation accuracies but were less accurate than label-free models when labeling was inconsistent. The relative strengths of label and label-free techniques could be combined through the use of merging fluorescence channels and out-of-focus brightfield images. Beyond comparing labeling strategies, using subimage stacks for training was also found to provide a method of simulating a wide range of labeling conditions, increasing the ability of the final model to accommodate a greater range of experimental setups.

bioengineering↗