Biology subjects

Levy, J. J.

Publications and source records attributed to Levy, J. J..

6 recordsLinked to original sources

bioRxiv2020

PathFlow-MixMatch for Whole Slide Image Registration: An Investigation of a Segment-Based Scalable Image Registration Method

Image registration involves finding the best alignment between different images of the same object. In these tasks, the object in question is viewed differently in each of the images (e.g. different rotation or light conditions, etc.). In digital pathology, image registration aligns correspondent regions of tissue from different stereotactic viewpoints (e.g. subsequent deeper sections of the same tissue). These comparisons are important for histological analysis and can facilitate previously unavailable manipulations, such as 3D tissue reconstruction and cell-level alignment of immunohistochemical (IHC) and special stains. Several benchmarks have been established for evaluating image registration techniques for histological tissue; however, little work has evaluated the impact of scaling registration techniques to Giga-Pixel Whole Slide Images (WSI), which are large enough for significant memory limitations, and contain recurrent patterns and deformations that hinder traditional alignment algorithms. Furthermore, as tissue sections often contain multiple, discrete, smaller tissue fragments, it is unnecessary to align an entire image when the bulk of the image is background whitespace and tissue fragments orientations are often agnostic of each other. We present a methodology for circumventing large-scale image registration issues in histopathology and accompanying software. By removing background pixels, parsing the slide into discrete tissue segments, and matching, orienting and registering smaller segment pairs, we recovered registrations with lower Target Registration Error (TRE) when compared to utilizing the unmanipulated WSI. We tested our technique by having a pathologist annotate landmarks from 13 pairs of differently stained liver biopsy slides, performing WSI and segment-based registration techniques, and comparing overall TRE. Preliminary results demonstrate superior performance of registering segment pairs versus registering WSI (difference of median TRE of 44 pixels, p<0.001). Segment matching within WSI is an effective solution for histology image registration but requires further testing and validation to ensure its viability for stain translation and 3D histology analysis.

pathology↗

bioRxiv2020

Preliminary Evaluation of the Utility of Deep Generative Histopathology Image Translation at a Mid-Sized NCI Cancer Center

Evaluation of a tissue biopsy is often required for the diagnosis and prognostic staging of a disease. Recent efforts have sought to accurately quantitate the distribution of tissue features and morphology in digitized images of histological tissue sections, Whole Slide Images (WSI). Generative modeling techniques present a unique opportunity to produce training data that can both augment these models and translate histologic data across different intra-and-inter-institutional processing procedures, provide cost-effective ways to perform computational chemical stains (synthetic stains) on tissue, and facilitate the creation of diagnostic aid algorithms. A critical evaluation and understanding of these technologies is vital for their incorporation into a clinical workflow. We illustrate several potential use cases of these techniques for the calculation of nuclear to cytoplasm ratio, synthetic SOX10 immunohistochemistry (IHC, sIHC) staining to delineate cell lineage, and the conversion of hematoxylin and eosin (H&E) stain to trichome stain for the staging of liver fibrosis.

pathology↗

bioRxiv2019

Don't Dismiss Logistic Regression: The Case for Sensible Extraction of Interactions in the Era of Machine Learning

BackgroundMachine learning approaches have become increasingly popular modeling techniques, relying on data-driven heuristics to arrive at its solutions. Recent comparisons between these algorithms and traditional statistical modeling techniques have largely ignored the superiority gained by the former approaches due to involvement of model-building search algorithms. This has led to alignment of statistical and machine learning approaches with different types of problems and the under-development of procedures that combine their attributes. In this context, we hoped to understand the domains of applicability for each approach and to identify areas where a marriage between the two approaches is warranted. We then sought to develop a hybrid statistical-machine learning procedure with the best attributes of each. MethodsWe present three simple examples to illustrate when to use each modeling approach and posit a general framework for combining them into an enhanced logistic regression model building procedure that aids interpretation. We study 556 benchmark machine learning datasets to uncover when machine learning techniques outperformed rudimentary logistic regression models and so are potentially well-equipped to enhance them. We illustrate a software package, InteractionTransformer, which embeds logistic regression with advanced model building capacity by using machine learning algorithms to extract candidate interaction features from a random forest model for inclusion in the model. Finally, we apply our enhanced logistic regression analysis to two real-word biomedical examples, one where predictors vary linearly with the outcome and another with extensive second-order interactions. ResultsPreliminary statistical analysis demonstrated that across 556 benchmark datasets, the random forest approach significantly outperformed the logistic regression approach. We found a statistically significant increase in predictive performance when using hybrid procedures and greater clarity in the association with the outcome of terms acquired compared to directly interpreting the random forest output. ConclusionsWhen a random forest model is closer to the true model, hybrid statistical-machine learning procedures can substantially enhance the performance of statistical procedures in an automated manner while preserving easy interpretation of the results. Such hybrid methods may help facilitate widespread adoption of machine learning techniques in the biomedical setting.

bioinformatics↗

bioRxiv2019

MethylNet: A Modular Deep Learning Approach to Methylation Prediction

BackgroundDNA methylation (DNAm) is an epigenetic regulator of gene expression programs that can be altered by environmental exposures, aging, and in pathogenesis. Traditional analyses that associate DNAm alterations with phenotypes suffer from multiple hypothesis testing and multi-collinearity due to the high-dimensional, continuous, interacting and non-linear nature of the data. Deep learning analyses have shown much promise to study disease heterogeneity. DNAm deep learning approaches have not yet been formalized into user-friendly frameworks for execution, training, and interpreting models. Here, we describe MethylNet, a DNAm deep learning method that can construct embeddings, make predictions, generate new data, and uncover unknown heterogeneity with minimal user supervision. ResultsThe results of our experiments indicate that MethylNet can study cellular differences, grasp higher order information of cancer sub-types, estimate age and capture factors associated with smoking in concordance with known differences. ConclusionThe ability of MethylNet to capture nonlinear interactions presents an opportunity for further study of unknown disease, cellular heterogeneity and aging processes.

bioinformatics↗

bioRxiv2019

PyMethylProcess - highly parallelized preprocessing for DNA methylation array data

SummaryThe ability to perform high-throughput preprocessing of methylation array data is essential in large scale methylation studies. While R is a convenient language for methylation analyses, performing highly parallelized preprocessing using Python can accelerate data preparation for downstream methylation analyses, including large scale production-ready machine learning pipelines. Here, we present a methylation data preprocessing pipeline called PyMethylProcess that is highly reproducible, scalable, and that can be quickly set-up and deployed through Docker and PIP.\n\nAvailability and ImplementationProject Name: PyMethylProcess\n\nProject Home Page: https://github.com/Christensen-Lab-Dartmouth/PyMethylProcess. Available on PyPI as pymethylprocess.\n\nAvailable on DockerHub via joshualevy44/pymethylprocess.\n\nHelp Documentation: https://christensen-lab-dartmouth.github.io/PyMethylProcess/\n\nOperating Systems: Linux, MacOS, Windows (Docker)\n\nProgramming Language: Python, R\n\nOther Requirements: Python 3.6, R 3.5.1, Docker (optional) License: MIT\n\nContactjoshua.j.levy.gr@dartmouth.edu

epidemiology↗

bioRxiv2018

PolyCRACKER, a robust method for the unsupervised partitioning of polyploid subgenomes by signatures of repetitive DNA evolution

Existing methods for assigning sequences to individual species from pooled DNA samples rely on differences in genome properties like GC content or sequences from related species. These approaches do not work for closely related species where gross features are indistinguishable and related genomes are lacking. We describe a method and associated software package that uses rapidly evolving repetitive DNA to circumvent these limitations. By using short, repetitive, DNA sequences as species-specific signals we separated closely related genomes without any prior knowledge. This approach is ideal for separating the subgenomes of polyploid species with unsequenced or unknown progenitor genomes.

genomics↗