Single Cell RNAseq: Glossary

Single cell methods excel in defining cellular heterogeneity and profiling cells differentiating along a trajectory.

Single cell RNA-Seq data is sparse and cannot be analyzed using standard bulk RNA-Seq approaches.

Bulk transcriptomics provides a more in-depth portrait of tissue gene expression, but scRNA-Seq allows you to distinguish between changes in cell composition vs gene expression.

Different experimental questions can be investigated using different single-cell sequencing modalities.

Due to the high variance in single-cell data sets, a well-powered study with adequate biological replication is essential for rigor & reproducibility.

Increasing the number of cells also increases the multiplet rate.

Pooling cells using hashtagging is a useful way to reduce costs, but also stresses cells and may affect cell viability.

Pooling cells from genetically diverse individuals may allow cells to be demultiplexed using genetic variants that differ between samples.

Do not confound experimental batch with any technical aspect of the experiment, i.e. sample pooling or flow cell assignment.

CellRanger produces a gene expression count matrix that can be read in using Seurat.

The count matrix is stored as a sparse matrix with features in rows and cells in columns.

It is essential to filter based on criteria including mitochondrial gene expression and number of genes expressed in a cell.

Determining your filtering thresholds should be done separately for each experiment, and these values can vary dramatically in different settings.

Seurat has flexible functionality to normalize, process, and visualize your scRNA-Seq data.

Identifying cell types is a major objective in scRNA-Seq and can be present challenges that are unique to each dataset.

Statistically minded experimental design enables you to perform differential gene expression analyses that are likely to result in meaningful biological results.

There are excellent tools for helping you analyze your scRNA-Seq data.

Pay attention to points we have stressed in this course and points that are stressed in analysis vignettes that you may find elsewhere.

There is a vibrant single cell community at JAX and online (e.g. Twitter) where you can seek out help.

There exists a rich suite of concepts and methods for working with scRNA-Seq data that goes beyond cell type identification and into inference about how cells are changing and interacting with each other.

Multimodal datasets and spatially resolved technologies will be key to future inquiry in single cell and related fields.