MOTIVATION: Single-cell RNA-seq analysis has emerged as a powerful tool for understanding inter-cellular heterogeneity. Due to the inherent noise of the data, computational techniques often rely on dimensionality reduction (DR) as both a pre-processing step and an analysis tool. Ideally, DR should preserve the biological information while discarding the noise. However, if the DR is to be used directly to gain biological insight it must also be interpretable-that is the individual dimensions of the reduction should correspond to specific biological variables such as cell-type identity or pathway activity. Maximizing biological interpretability necessitates making assumption about the data structures and the choice of the model is critical. RESULTS: We present a new probabilistic single-cell factor analysis model, Non-negative Independent Factor Analysis (NIFA), that incorporates different interpretability inducing assumptions into a single modeling framework. The key advantage of our NIFA model is that it simultaneously models uni- and multi-modal latent factors, and thus isolates discrete cell-type identity and continuous pathway activity into separate components. We apply our approach to a range of datasets where cell-type identity is known, and we show that NIFA-derived factors outperform results from ICA, PCA, NMF and scCoGAPS (an NMF method designed for single-cell data) in terms of disentangling biological sources of variation. Studying an immunotherapy dataset in detail, we show that NIFA is able to reproduce and refine previous findings in a single analysis framework and enables the discovery of new clinically relevant cell states. AVAILABILITY AND IMPLEMENTATION: NFIA is a R package which is freely available at GitHub (https://github.com/wgmao/NIFA). The test dataset is archived at https://zenodo.org/record/6286646. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

OBJECTIVE: Left ventricular assist devices (LVADs) fail in up to 10% of patients due to the development of pump thrombosis. Remote monitoring of patients with LVADs can enable early detection and, subsequently, treatment and prevention of pump thrombosis. We assessed whether acoustical signals measured on the chest of patients with LVADs, combined with machine learning algorithms, can be used for detecting pump thrombosis. METHODS: 13 centrifugal pump (HVAD) recipients were enrolled in the study. When hospitalized for suspected pump thrombosis, clinical data and acoustical recordings were obtained at admission, prior to and after administration of thrombolytic therapy, and every 24 hours until laboratory and pump parameters normalized. First, we selected the most important features among our feature set using LDH-based correlation analysis. Then using these features, we trained a logistic regression model and determined our decision threshold to differentiate between thrombosis and non-thrombosis episodes. RESULTS: Accuracy, sensitivity and precision were calculated to be 88.9%, 90.9% and 83.3%, respectively. When tested on the post-thrombolysis data, our algorithm suggested possible pump abnormalities that were not identified by the reference pump power or biomarker abnormalities. SIGNIFICANCE: We showed that the acoustical signatures of LVADs can be an index of mechanical deterioration and, when combined with machine learning algorithms, provide clinical decision support regarding the presence of pump thrombosis.

Saeideh Shahrokh Esfahani, Michael J. Cafarella, Maziyar Baran Pouyan, Gregory DeAngelo, Elena Eneva, Andy E. Fano. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019.

Motivation: Genome-wide transcriptome sequencing applied to single cells (scRNA-seq) is rapidly becoming an assay of choice across many fields of biological and biomedical research. Scientific objectives often revolve around discovery or characterization of types or sub-types of cells, and therefore, obtaining accurate cell-cell similarities from scRNA-seq data is a critical step in many studies. While rapid advances are being made in the development of tools for scRNA-seq data analysis, few approaches exist that explicitly address this task. Furthermore, abundance and type of noise present in scRNA-seq datasets suggest that application of generic methods, or of methods developed for bulk RNA-seq data, is likely suboptimal. Results: Here, we present RAFSIL, a random forest based approach to learn cell-cell similarities from scRNA-seq data. RAFSIL implements a two-step procedure, where feature construction geared towards scRNA-seq data is followed by similarity learning. It is designed to be adaptable and expandable, and RAFSIL similarities can be used for typical exploratory data analysis tasks like dimension reduction, visualization and clustering. We show that our approach compares favorably with current methods across a diverse collection of datasets, and that it can be used to detect and highlight unwanted technical variation in scRNA-seq datasets in situations where other methods fail. Overall, RAFSIL implements a flexible approach yielding a useful tool that improves the analysis of scRNA-seq data. Availability and implementation: The RAFSIL R package is available at www.kostkalab.net/software.html. Supplementary information: Supplementary data are available at Bioinformatics online.

Single-cell flow cytometry is a technology that measures the expression of several cellular markers simultaneously for a large number of cells. Identification of homogeneous cell populations, currently done by manual biaxial gating, is highly subjective and time consuming. To overcome the shortcomings of manual gating, automatic algorithms have been proposed. However, the performance of these methods highly depends on the shape of populations and the dimension of the data. In this paper, we have developed a time-efficient method that accurately identifies cellular populations. This is done based on a novel technique that estimates the initial number of clusters in high dimension and identifies the final clusters by merging clusters using their phenotypic signatures in low dimension. The proposed method is called SigClust. We have applied SigClust to four public datasets and compared it with five well known methods in the field. The results are promising and indicate higher performance and accuracy compared to similar approaches reported in literature.

Complex tissues such as brain and bone marrow are made up of multiple cell types. As the study of biological tissue structure progresses, the role of cell-type-specific research becomes increasingly important. Novel sequencing technology such as single-cell cytometry provides researchers access to valuable biological data. Applying machine-learning techniques to these high-throughput datasets provides deep insights into the cellular landscape of the tissue where those cells are a part of. In this paper, we propose the use of random-forest-based single-cell profiling, a new machine-learning-based technique, to profile different cell types of intricate tissues using single-cell cytometry data. Our technique utilizes random forests to capture cell marker dependences and model the cellular populations using the cell network concept. This cellular network helps us discover what cell types are in the tissue. Our experimental results on public-domain datasets indicate promising performance and accuracy of our technique in extracting cell populations of complex tissues.

Measurement of various markers of single cells using flow cytometry has several biological applications. These applications include improving our understanding of behavior of cellular systems, identifying rare cell populations and personalized medication. A common critical issue in the existing methods is approximation of the number of cellular populations which heavily affects the accuracy of results. In this work, we propose a novel technique to estimate the number of dominant subtypes and identify them in flow cytometry datasets. Our experimentation on 42 flow cytometry datasets indicates high performance and accurate clustering (F-measure > 91%) in identifying the main cellular populations.

Pressure ulcer is a major problem for bed-bound and wheelchair-bound individuals specially in regions like sacrum, buttocks, hip, heels, back and head. Once developed, it is extremely uncomfortable and costly. Identification and monitoring of high-risk regions and their pressure distributions help nurses to have information about risk in each specific area of body and reposition patient efficiently. In this paper, we propose an algorithm to detect regions that are under high stress. Because of low resolution nature of pressure image and changes in shape of human body parts in various images, we adopted image processing algorithms. The image of human body is segmented using Delaunay triangulation. The extracted tree is compared to defined template for each posture. Then, signal processing and graph matching algorithms are used to label the tree according to the template. Pressure values of each specific region are collected for other phases of ulcer management such as risk assessment and reposition schedule. The experimental results indicate that our method can detect 9 (6) regions in supine (side) postures with average accuracy of 85.7%.