Extraction of biological signals by factorization enables the reliable analysis of single-cell transcriptomics

Abstract

Accurately and reliably capturing actual biological signals from single-cell transcriptomics is vital for achieving legitimate scientific results, which is unfortunately hindered by the presence of various kinds of unwanted variations. Here we described a deep auto-regressive factor model known as scPhenoXMBD, demonstrated that each gene’s expression can be split into discrete components that represent biological signals and unwanted variations, which effectively mitigated the effects of unwanted variations in the data of single-cell sequencing. Using scPhenoXMBD, we evaluated various factors affecting IFNβ-stimulated immune cells and demonstrated that biological signal extraction facilitates the identification of IFNβ-responsive pathways and genes. Numerous experiments were conducted to show that scPhenoXMBDcould be utilized successfully in enhancing cell clustering stability, obtaining identical cell populations from diverse data sources, advancing the single-cell CRISPR screening of functional elements, and minimizing the influence of inter-subject discrepancies in the cell-disease relationships. scPhenoXMBDis anticipated to be a dependable and repeatable method for the precise analysis of single-cell data.

OmniClustifyXMBD: Uncover putative cell states within multiple single-cell omics datasets

“…We have presented OmniClustify XMBD , an innovative clustering methodology specifically designed for the identification of putative cell states within the intricate landscape of diverse single-cell omics datasets. This method represents an extension of our recent work in adaptive signal isolation [14, 15], now coupled with variational Gaussian mixture modeling. OmniClustify XMBD stands as an exceptional advancement in its capacity to robustly and accurately delineate distinct variations while generating dependable cell clusterings.…”

ChatGPT in Drug Discovery: A Case Study on Anticocaine Addiction Drug Development with Chatbots

“…Chatbots can be applied to assist molecular science research in a variety of ways. For example, ChatGPT has been leveraged to accurately annotate single-cell RNA sequencing data, connecting rare cell types to their functions and unveiling specific differentiation trajectories of cell subtypes that were previously overlooked . This assistance by ChatGPT could potentially lead to the discovery of key cells that disrupt differentiation pathways, offering fresh insights into cellular biology and related diseases.…”