Computational Methods For The Analysis Of Single Cell Rna Seq Data
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Computational Methods for Single-Cell Data Analysis
This detailed book provides state-of-art computational approaches to further explore the exciting opportunities presented by single-cell technologies. Chapters each detail a computational toolbox aimed to overcome a specific challenge in single-cell analysis, such as data normalization, rare cell-type identification, and spatial transcriptomics analysis, all with a focus on hands-on implementation of computational methods for analyzing experimental data. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Computational Methods for Single-Cell Data Analysis aims to cover a wide range of tasks and serves as a vital handbook for single-cell data analysis.
Computational Methods for the Analysis of Single-Cell RNA-Seq Data
Single cell transcriptional profiling is critical for understanding cellular heterogeneity and identification of novel cell types and for studying growth and development of tissues and tumors. Leveraging recent advances in single cell RNA sequencing (scRNA-Seq) technology requires novel methods that are robust to high levels of technical and biological noise and scale to datasets of millions of cells. In this work, we address several challenges in the analysis work-flow of scRNA-Seq data: First, we propose novel computational approaches for unsupervised clustering of scRNA-Seq data based on Term Frequency - Inverse Document Frequency (TF-IDF) transformation that has been successfully used in text analysis. Here, we present empirical experimental results showing that TF-IDF methods consistently outperform commonly used scRNA-Seq clustering approaches. Second, we study the so called 'drop-out' effect that is considered one of the most notable challenges in scRNA-Seq analysis, where only a fraction of the transcriptome of each cell is captured. The random nature of drop-outs, however, makes it possible to consider imputation methods as means of correcting for drop-outs. In this part we study existing scRNA-Seq imputation methods and propose a novel iterative imputation approach based on efficiently computing highly similar cells. We then present results of a comprehensive assessment of existing and proposed methods on real scRNA-Seq datasets with varying per cell sequencing depth. Third, we present a computational method for assigning and/or ordering cells based on their cell-cycle stages from scRNA-Seq. And finally, we present a web-based interactive computational work-flow for analysis and visualization of scRNA-seq data.
Machine Learning in Single-Cell RNA-seq Data Analysis
This book provides a concise guide tailored for researchers, bioinformaticians, and enthusiasts eager to unravel the mysteries hidden within single-cell RNA sequencing (scRNA-seq) data using cutting-edge machine learning techniques. The advent of scRNA-seq technology has revolutionized our understanding of cellular diversity and function, offering unprecedented insights into the intricate tapestry of gene expression at the single-cell level. However, the deluge of data generated by these experiments presents a formidable challenge, demanding advanced analytical tools, methodologies, and skills for meaningful interpretation. This book bridges the gap between traditional bioinformatics and the evolving landscape of machine learning. Authored by seasoned experts at the intersection of genomics and artificial intelligence, this book serves as a roadmap for leveraging machine learning algorithms to extract meaningful patterns and uncover hidden biological insights within scRNA-seq datasets.