Single Cell Basics

What this page is:

• A basic quickstart guide to get someone interested in single cell genomics data started with data analysis in Code Ocean capsules.
• Access to a complete runtime environment to run a handful of single cell data tutorials in a jupyter notebook (i.e you won't have to install any software).
• Access to test data to run complete workflows.

What this page is not:

• This page is not a course on single cell data analysis. It is recommended to go through the Single Cell Course for the appropriate background to better understand how this type of data is generated and to understand what types of experiments you can work on.

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Background

Single cell sequencing examines the sequence information from individual cells with optimized next-generation sequencing (NGS) technologies, providing a higher resolution of cellular differences and a better understanding of the function of an individual cell in the context of its microenvironment.

Having a strong background on the technology and computational methods will allow you to put your biological data into context and give you the best chance of answering a meaningful biological question.

For a more complete general orverview please read the Introduction to Single-Cell RNA-seq from the Single Cell Course website.

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Getting Started with Tutorials

• You should begin with the first tutorial listed below.
• #1 and #2 listed below is the same clustering workflow with the same data set. #1 uses Python and #2 uses R.

1. Preprocessing and clustering
   • pbmc3k.ipynb
     • Scanpy (Single Cell Analysis in Python) reimplementation of Seurat's clustering tutorial for 3,000 PBMCs from 10x Genomics.
   • seurat_pbmc3k.ipynb
     • Seurat clustering tutorial for 3,000 PBMCs from 10x Genomics.
2. Visualization
   • core.ipynb Core plotting functions
     • visually explore genes using scanpy
3. Trajectory inference
   • paga-paul15.ipynb Trajectory inference for hematopoiesis in mouse
     • Reconstructing myeloid and erythroid differentiation
4. Integrating datasets
   • integrating-data-using-ingest.ipynb Integrating data using ingest and BBKNN
     • simple PCA-based method for integrating data
5. Spatial data
   • basic-spatial-analysis.ipynb Analysis and visualization of spatial transcriptomics data
     • how to work with spatial transcriptomics data within Scanpy.
   • integration-scanorama.ipynb Integrating spatial data with scRNA-seq using scanorama
     • how to work with multiple Visium datasets and perform integration of scRNA-seq dataset
6. Multimodal
   • Using Seurat with Multimodal Data
     • Make simultaneous measurements of multiple data types from the same cell