Compare genome-wide gene expression profiles across two groups of cells
Written by Caitlin Winkler, PhD
Differential expression analysis is a common method for analyzing genome-wide gene expression changes between two different groups of cells, and is useful to detect biomarkers for novel cell types or to discern gene signatures that define cellular heterogeneity. For each each gene, differential expression analysis outputs a fold change (measure of magnitude of change) and an adjusted p-value (measure of statistical significance of change).
Set up your analysis
Navigate to your experiment and open the Analysis page by clicking "Analysis" on the top navigation bar or by clicking the "+" button next to the "Analyses" summary on your experiment's Overview page.
Click "+ Analysis" and select Differential expression.
Build your comparison by selecting at least one cluster annotation set, variable, or latent variable to group cells by. You can use a combination of cluster annotation sets, variables, and latent variables for a single comparison to define your target group and reference group of interest.
Cluster annotation sets are created during the scRNA-seq preprocessing workflow, and are further refined on the Annotation page. Variables are derived from the metadata columns in your Sample Data. Latent variables are added during the scRNA-seq preprocessing workflow, and offer the flexibility to group your cells by cell cycle phase and/or multiplet class.
There are additional optional parameters under the Statistics section of the analysis menu.
Customize your plots
Use the Plot tab to customize the title, color palette, and other features of the plot that you created from the differential expression analysis. You can also select and label specific genes of interest on your plot. Press "Save changes" to apply your customization.
If you have any questions, contact your Pluto representative by email or using our in-app chat.