It is exciting to become a Computational Biologist working in transformative research, which means you may become an expert in multiple hot fields: genomics, genetics, coding, statistics, machine learning, systems immunology, translational medicine, etc. I hope you select > 3 from these fields and dive deeper into them.
Here, we list great papers and classical books that cover the key research topics in our lab. Many of them are "must read" 📖 for our lab. We are expanding this list and welcome any of our team members to contribute to this. We hope these resources can guide you to start this exciting field and find out your own interests to make an impact!
- RNA-sequencing introduction: RNA-Seq: a revolutionary tool for transcriptomics, Nature Reviews Genetics
- A nice review of RNA-seq analysis: From RNA-seq reads to differential expression results, Genome Biology
- A detailed introduction with nice illustrations for single-cell RNA-seq
- The triumphs and limitations of computational methods for scRNA-seq, Nature Methods
- Computational challenges and opportunities in Single-cell transcriptomics, Nature EMM
- Computational principles and challenges in single-cell data integration, Nature Biotechnology
- Eleven grand challenges in single-cell data science, Genome Biology
- Single-cell RNA sequencing to explore immune cell heterogeneity, Nature Reviews Immunology
- Current best practices in single-cell RNA-seq analysis: a tutorial, Molecular Systems Biology
- Principles and challenges of modeling temporal and spatial omics data, Nature Methods
- Benchmarking atlas-level data integration in single-cell genomics, Nature Methods
- A benchmark of batch-effect correction methods for single-cell RNA sequencing data, Genome Biology
- Advances in spatial transcriptomic data analysis, Genome Research
- Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution, Nature Methods
- A comparison of single-cell trajectory inference methods, Nature Biotechnology
- Systematic comparison of single-cell and single-nucleus RNA-sequencing methods, Nature Biotechnology
- A comparison of single-cell trajectory inference methods. Check dynverse.org for more information.
- Evaluation of single-cell classifiers for single-cell RNA sequencing data sets, Briefings in Bioinformatics. 9 tools have been systematically compared in this article.
- Deciphering cell–cell interactions and communication from gene expression, Nature Reviews Genetics
- Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data, Nature Methods
- Comparison of methods to detect differentially expressed genes between single-cell populations, Briefings in Bioinformatics
- Uncover spatially informed variations for single-cell spatial transcriptomics with STew, Bioinformatics Advances
- Stabilized mosaic single-cell data integration using unshared features, Nature Biotechnology
- Co-varying neighborhood analysis identifies cell populations associated with phenotypes of interest from single-cell transcriptomics, Nature Biotechnology
- Fast, sensitive and accurate integration of single-cell data with Harmony, Nature Methods
- Efficient and precise single-cell reference atlas mapping with Symphony, Nature Communications
- Deconstruction of rheumatoid arthritis synovium defines inflammatory subtypes, Nature
- Distinct mucosal endotypes as initiators and drivers of rheumatoid arthritis, Nature Review Rheumatology
- Immune mechanisms in fibrotic interstitial lung disease, Cell
- Insights into rheumatic diseases from next-generation sequencing, Nature Reviews Rheumatology
- Single-cell technologies — studying rheumatic diseases one cell at a time, Nature Reviews Rheumatology
- Not a losing battle: Single cell tools provide new insights into chronic autoimmune diseases, 10X Genomics Blog
- Single-cell eQTL mapping identifies cell type-specific genetic control of autoimmune disease, Science
- Defining inflammatory cell states in rheumatoid arthritis joint synovial tissues by integrating single-cell transcriptomics and mass cytometry, Nature Immunology
- IFN-γ and TNF-α drive a CXCL10+ CCL2+ macrophage phenotype expanded in severe COVID-19 lungs and inflammatory diseases with tissue inflammation, Genome Medicine
- scRepertoire: offers standard code for 10X single-cell TCR and BCR data.
- Immcantation: a start-to-finish ecosystem for adaptive immune receptor repertoire (AIRR) data like TCR and BCR.
- Applied Statistics for Experimental Biology: offers many useful examples with R code, and even a comprehensive introduction of useful R packages, R Markdown, and generalized linear models.
- R for Data Science: free PDF
- Data Science for Biological, Medical and Health Research: Notes and code. It includes multiple statistical modeling strategies, e.g., modeling a Count Outcome.
- An Introduction to Machine Learning with R: link
- R Style Guide: Google's R Style Guide
- Good coding style in R: The tidyverse coding style
- Package Development Guidelines: R Code Development: Re-use of functionality, classes, and generics, function development, robust and efficient R code
- The R Graph Gallery: code examples
- Applied Generalized Linear Models and Multilevel Models in R - Beyond Multiple Linear Regression: code
- tidymodels Labs includes code with comments for the 2nd version of "An introduction for statistical learning". For example, it includes Moving Beyond Linearity and Unsupervised Learning, etc.
- "Machine Learning: A Probabilistic Perspective": free PDF
- "Probabilistic Machine Learning: An Introduction": free PDF
- "Probabilistic Machine Learning: Advanced Topics": free PDF
- "Deep Generative Modeling": free PDF and corresponding example code.
- Awesome Machine Learning: A curated list of awesome machine learning frameworks, libraries, software, and free books!
Stay tuned 🔥