Biology subjects

Orzechowski, P.

Publications and source records attributed to Orzechowski, P..

5 recordsLinked to original sources

bioRxiv2024

Automated Cytometric Gating with Human-Level Performance Using Bivariate Segmentation

Recent advances in cytometry technology have enabled high-throughput data collection with multiple single-cell protein expression measurements. The significant biological and technical variance between samples in cytometry has long posed a formidable challenge during the gating process, especially for the initial gates which deal with unpredictable events, such as debris and technical artifacts. Even with the same experimental machine and protocol, the target population, as well as the cell population that needs to be excluded, may vary across different measurements. To address this challenge and mitigate the labor-intensive manual gating process, we propose a deep learning framework UNITO to rigorously identify the hierarchical cytometric subpopulations. The UNITO framework transformed a cell-level classification task into an image-based semantic segmentation problem. For reproducibility purposes, the framework was applied to three independent cohorts and successfully detected initial gates that were required to identify single cellular events as well as subsequent cell gates. We validated the UNITO framework by comparing its results with previous automated methods and the consensus of at least four experienced immunologists. UNITO outperformed existing automated methods and differed from human consensus by no more than each individual human. Most critically, UNITO framework functions as a fully automated pipeline after training and does not require human hints or prior knowledge. Unlike existing multi-channel classification or clustering pipelines, UNITO can reproduce a similar contour compared to manual gating for each intermediate gating to achieve better interpretability and provide post hoc visual inspection. Beyond acting as a pioneering framework that uses image segmentation to do auto-gating, UNITO gives a fast and interpretable way to assign the cell subtype membership, and the speed of UNITO will not be impacted by the number of cells from each sample. The pre-gating and gating inference takes approximately 2 minutes for each sample using our pre-defined 9 gates system, and it can also adapt to any sequential prediction with different configurations.

immunology↗

bioRxiv2024

Single-cell Masked Autoencoder: An Accurate and Interpretable Automated Immunophenotyper

High-throughput single-cell cytometry data are crucial for understanding involvement of immune system in diseases and responses to treatment. Traditional methods for annotating cytometry data, specifically manual gating and clustering, face challenges in scalability, robustness, and accuracy. In this study, we propose a cytometry masked autoencoder (cyMAE), which offers an automated solution for immunophenotyping tasks including cell type annotation. The cyMAE model is designed to uphold user-defined cell type definitions, thereby facilitating easier interpretation and cross-study comparisons. The cyMAE model operates on a pre-train and fine-tune approach. In the pre-training phase, cyMAE employs Masked Cytometry Modelling (MCM) to learn relationships between protein markers in immune cells solely based on protein expression, without relying on prior information such as cell identity and cell type-specific marker proteins. Subsequently, the pre-trained cyMAE is fine-tuned on multiple specialized tasks via task-specific supervised learning. The pre-trained cyMAE addresses the shortcomings of manual gating and clustering methods by providing accurate and interpretable predictions. Through validation across multiple cohorts, we demonstrate that cyMAE effectively identifies co-occurrence patterns of bound labeled antibodies, delivers accurate and interpretable cellular immunophenotyping, and improves the prediction of subject metadata status. Specifically, we evaluated cyMAE for cell type annotation and imputation at the cellular-level and SARS-CoV-2 infection prediction, secondary immune response prediction against COVID-19, and prediction of the infection stage in COVID-19 progression at the subject-level. The introduction of cyMAE marks a significant step forward in immunology research, particularly in large-scale and high-throughput human immune profiling. This approach offers new possibilities for predicting and interpreting cellular-level and subject-level phenotypes in both health and disease.

bioinformatics↗

bioRxiv2024

QOT: Efficient Computation of Sample Level Distance Matrix from Single-Cell Omics Data through Quantized Optimal Transport

AO_SCPLOWBSTRACTC_SCPLOWSingle-cell technologies have emerged as a transformative technology enabling high-dimensional characterization of cell populations at an unprecedented scale. The datas innate complexity and voluminous nature pose significant computational and analytical challenges, especially in comparative studies delineating cellular architectures across various biological conditions (i.e., generation of sample level distance matrices). Optimal Transport (OT) is a mathematical tool that captures the intrinsic structure of data geometrically and has been applied to many bioinformatics tasks. In this paper, we propose QOT (Quantized Optimal Transport), a new method enables efficient computation of sample level distance matrix from large-scale single-cell omics data through a quantization step. We apply our algorithm to real-world single-cell genomics and pathomics datasets, aiming to extrapolate cell-level insights to inform sample level categorizations. Our empirical study shows that QOT outperforms OT-based algorithms in terms of accuracy and robustness when obtaining a distance matrix at the sample level from high throughput single-cell measures. Moreover, the sample level distance matrix could be used in downstream analysis (i.e. uncover the trajectory of disease progression), highlighting its usage in biomedical informatics and data science.

bioinformatics↗

bioRxiv2017

runibic: a Bioconductor package for parallel row-based biclustering of gene expression data

MotivationBiclustering (called also co-clustering) is an unsupervised technique of simultaneous analysis of rows and columns of input matrix. From the first application to gene expression data, multiple algorithms have been proposed. Only a handful of them were able to provide accurate results and were fast enough to be suitable for large-scale genomic datasets.\n\nResultsIn this paper we introduce a Bioconductor package with parallel version of UniBic biclustering algorithm: one of the most accurate biclustering methods that have been developed so far. For the convenience of usage, we have wrapped the algorithm in an R package called runibic. The package includes: (1) a couple of times faster parallel version of the original sequential algorithm,(2) muchmore efficient memory management, (3) modularity which allows to build new methods on top of the provided one, (4) integration with the modern Bioconductor packages such as SummarizedExperiment, ExpressionSetand biclust.\n\nAvailabilityThe package is implemented in R (3.4) and will be available in the new release of Bioconductor (3.6). Currently it could be downloaded from the following URL: http://github.com/athril/runibic/\n\nContactpatryk.orzechowski@gmail.com, jhmoore@upenn.edu\n\nSupplementary informationSupplementary informations are available in vignette of the package.

bioinformatics↗

bioRxiv2017

Mapping Patient Trajectories using Longitudinal Extraction and Deep Learning in the MIMIC-III Critical Care Database

Electronic Health Records (EHRs) contain a wealth of patient data useful to biomedical researchers. At present, both the extraction of data and methods for analyses are frequently designed to work with a single snapshot of a patients record. Health care providers often perform and record actions in small batches over time. By extracting these care events, a sequence can be formed providing a trajectory for a patients interactions with the health care system. These care events also offer a basic heuristic for the level of attention a patient receives from health care providers. We show that is possible to learn meaningful embeddings from these care events using two deep learning techniques, unsupervised autoencoders and long short-term memory networks. We compare these methods to traditional machine learning methods which require a point in time snapshot to be extracted from an EHR.

bioinformatics↗