Science
Learning Resources
A collection of self-guided learning resources created by others.
Single-Cell Analyses
Single-Cell RNA-seq
scRNA-seq Analysis Tutorial: Analysis of Single-Cell RNA-seq Data – A continuously updated and comprehensive single-cell RNA-seq analysis tutorial/course, taught primarily in R. This course begins with a discussion of single-cell methods, experimental design, and data processing and ends with single-cell dataset integration. It uses primarily Seurat, but also covers other tools for analysis and integration.
scRNA-seq Tutorial: Single-cell best practices – A very good tutorial on the best practices in single-cell RNA-seq analysis, taught primarily in Python. This course starts with pre-processing and QC, and ends with a brief overview on CITE-seq, immune repertoire, and integration. The paper that this course was originally based on.
Single-Cell Genomics Workshop: Single-Cell Genomics Day – A yearly workshop by the Satija lab (leaders in the world of single-cell analysis) on various single-cell genomics analysis topics/methods. In addition to a “recent and future advances” session, the workshop covers spatial and temporal analysis, epigenomic analysis, genotype-phenotype landscapes, and multimodal analyses.
scRNA-seq Best Practices Pipeline: nf-core/scrnaseq – A best-practices pipline for processing 10x genomics single-cell data using Nextflow, a workflow management tool that provides improved computational metics and reproducibility.
Curated database of single cell studies – A manually curated database of over 1800 single-cell studies, dating back to 2002. The doi, number of cells, organism, tissue, and experimental method are included, as well as other useful information.
Single-Cell ATAC-seq
scATAC-seq Lectues: Best practices for ATAC-seq – A set of lectures from Ming Tang that cover the scATAC-seq experimental method, pre-processing and QC, and anlysis and integration.
scATAC-seq Review Paper: Single-cell ATAC sequencing analysis: From data preprocessing to hypothesis generation – A helpful review of scATAC-seq technologies and analysis software.
Proteomics
[Mass-Spec Based Proteomics Aanalysis Tutorial]
Best practices for mass-spec-based biomarker discovery – A high-level paper overview of best practices for mass-spectrometry-based biomarker discovery.
Imaging Analysis
[Segmentation]
General Bioinformatics
Bioinformatics Course: Bioinformatics for Plant and Animal Sciences – A quality YouTube based course by Dr. Danny Arends (with videos, access to lectures, assignments, and answers) on bioinformatics. This course covers biological topics (genetics, molecular biology, metabolism, homology/phylogeny, etc.), as well as computational and analysis topics (transcriptomics, R, stats) and ends with learning how to create an R package.
Rosalind – a great resource to learn about bioinformatics (including algorithms) and programming by problem-solving.
Statistics
Introductory Stats Course: Introduction to Experimental Design and Hypothesis Testing – A beginner-friendly set of resources (slides, code) from an online workshop by the Gladstone Institutes to work though. The resources cover concepts underlying hypothesis testing.
Intermediate Course: Statistics of Enrichment Analyses Methods – A course designed for those familiar with basic statistics and experimental design concepts, an understanding of high-throughput analyses (RNA-seq, Mass Spec, etc.), and a working knowledge of R.