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Driven by a commitment to propel scientific progress, we empower researchers with easy-to-use tools for navigating complex, high-dimensional biological data. Our solutions simplify the data processing journey, enabling scientists to focus on their groundbreaking ideas and experiments, accelerating the pace of discovery.

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Define novel cell subtype using single-cell datasets

The advent of single-cell technologies has revolutionized our understanding of biological complexity, providing a lens through which we can discern the fine details of cellular heterogeneity. This transformative approach enables researchers to characterize cell types and discover new subtypes that have remained elusive with previous methodologies. However, defining new cell subtypes often relies on the independent efforts of individual researchers. In this blog, we will show you how to leverage publicly available single-cell datasets to hypothesize and verify novel cell subtypes, providing stronger evidence before committing to laboratory experiments, ultimately saving time and resources.

Dive with Visium HD: Uncover Spatial Cell Communication at Single-Cell Clarity

In the ever-changing world of biological research, understanding how cells communicate within their spatial context is crucial. The 10X Visium HD technology has become a game-changer, allowing scientists to study cell interactions with amazing detail and clarity. In this post, we explore the capabilities of the Visium HD dataset to dissect cell-cell communication in a spatial context at single-cell precision.

Uncovering Alzheimer's Chromatin Accessibility: scATAC-seq Data Re-analysis with OmnibusX

Unlocking the molecular mechanisms of Alzheimer's disease involves exploring both genetic and epigenetic factors that contribute to cellular dysfunction. Single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) is a powerful technique that provides insights into chromatin accessibility at the single-cell level, shedding light on gene regulation and epigenetic diversity. In this blog post, we leverage the OmnibusX platform to re-analyze publicly available scATAC-seq data and reveal key chromatin accessibility patterns associated with Alzheimer's disease. Through this re-analysis, we aim to uncover novel insights into the regulatory landscape of this neurodegenerative disorder.

Cell Type Prediction: A Review of Current Approaches and Emerging Challenges

Recent advances in single-cell RNA sequencing (scRNAseq) technologies have revolutionized the study of cellular heterogeneity, characteristics, and activity in various environments. Yet accurate, reproducible, harmonized, consistent, and automated cell type annotation remains a significant challenge. In this blog post, we will explore some of the major approaches of automated cell type annotation and their challenges.

Unveiling clonotype diversity and repertoire overlap in diseased PBMCs through OmnibusX analysis

Understanding the dynamics of T-cell receptor (TCR) and B-cell receptor (BCR) clonotypes is essential for advancing our knowledge of immune system functionality and its role in health and disease. Single-cell RNA sequencing (scRNAseq) combined with TCR/BCR sequencing provides a robust framework for analyzing clonotype diversity and repertoire structure at a granular level. Clonotype diversity, repertoire overlap, and spectratype analyses are powerful tools for investigating immune responses.

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Integrative analysis of single-cell gene expression: A comprehensive database approach

The exponential growth of single-cell datasets provides unprecedented opportunities to advance our understanding of complex biological systems. However, effectively locating and integrating related studies for meaningful insights remains challenging. Traditional databases primarily index basic metadata, which necessitates time-consuming downloading and re-filtering based on gene expression and cell type or tissue composition, followed by computationally intensive aggregation. This process often results in excessively large datasets that are difficult to analyze effectively, further complicated by batch effects. To address these issues, we have developed a computational approach to efficiently extract and index both expression data and annotations. Our comprehensive database incorporates detailed author annotations and gene expression profiles, enabling refined searches and integrated analyses to uncover common biological patterns while accounting for the repeatability of patterns across multiple studies and mitigating batch effects. This approach significantly reduces computational demands and enhances the accessibility and utility of single-cell transcriptomics data for the broader research community. In the first version, we release a human database comprising 244 datasets from 236 cell types, 35 tissues, and 31 conditions.

doi.org/10.1101/2024.07.23.604709

Automated cell annotation in scRNA-seq data using unique marker gene sets

Single-cell RNA sequencing has revolutionized the study of cellular heterogeneity, yet accurate cell type annotation remains a significant challenge. Inconsistent labels, technological variability, and limitations in transferring annotations from reference datasets hinder precise annotation. This study presents a novel approach for accurate cell type annotation in scRNA-seq data using unique marker gene sets. By manually curating cell type names and markers from 280 publications, we verified marker expression profiles across these datasets and unified the nomenclature to consistently identify 166 cell types and subtypes. Our customized algorithm, which builds on the AUCell method, achieves accurate cell labeling at single-cell resolution and surpasses the performance of reference-based tools like Azimuth, especially in distinguishing closely related subtypes. To enhance accessibility and practical utility for researchers, we have also developed a user-friendly application that automates the cell typing process, enabling efficient verification and supporting comprehensive downstream analyses.

doi.org/10.1101/2024.05.24.595477