Real-Time Analysis of Single-Cell RNA Sequencing Data Using GPU and ML

EasyChair Preprint 13942

Abstract

Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of cellular heterogeneity and gene expression dynamics at unprecedented resolution. However, the computational demands of analyzing scRNA-seq data pose significant challenges, particularly in achieving real-time insights crucial for dynamic biological processes. This paper explores the integration of Graphics Processing Units (GPUs) and Machine Learning (ML) techniques to accelerate the real-time analysis of scRNA-seq data. By harnessing the parallel computing power of GPUs, coupled with advanced ML algorithms tailored for dimensionality reduction, clustering, and trajectory inference, this approach aims to expedite the identification of cellular states and transitions. We discuss methodologies for optimizing data preprocessing, model training, and inference pipelines to enhance scalability and efficiency. Case studies demonstrate the utility of GPU-accelerated ML models in deciphering complex cellular landscapes and predicting cell-cell interactions. Ultimately, this framework not only facilitates rapid data interpretation but also paves the way for comprehensive exploration of cellular dynamics in health and disease contexts.

Keyphrases: Accelerated sequence analysis, Bioinformatic algorithms, Computational Proteomics, Computational genomics, Deep learning in bioinformatics, GPU-accelerated machine learning, GPU-based bioinformatics, Genomic data processing, High Performance Computing, Machine learning in computational biology

@booklet{EasyChair:13942,
  author    = {Abi Cit},
  title     = {Real-Time Analysis of Single-Cell RNA Sequencing Data Using GPU and ML},
  howpublished = {EasyChair Preprint 13942},
  year      = {EasyChair, 2024}}