Examine expression of a marker gene across groups of single cells
Marker expression analysis is a powerful tool for examining the expression levels of specific target genes across different cell groups in single-cell RNA sequencing data. This analysis helps to visualize how the expression of a particular gene varies among distinct cell populations, enabling researchers to identify patterns and differences in gene expression. By comparing expression levels across multiple conditions or cell types, users can gain insights into the role of specific genes in defining cellular states, identify potential biomarkers, and understand the underlying biological processes driving cellular heterogeneity. Marker expression analysis provides valuable visual representations through cell scatter plots, violin plots, and ridge plots, each offering a unique perspective on gene expression distributions and their variations across groups.
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. Next, click "+ Analysis" and select Marker expression.
Create your analysis by searching for and selecting your target gene of interest, and then clicking "Run analysis".
Customize your plots
Use the Plot tab to customize the title, color palette, and other features of the plot that you created from the Marker expression analysis.
A key feature is the ability to define groups of cells based on cluster annotation sets, variables, or latent variables. This flexibility allows you to tailor cell groupings to fit your analytical needs and to focus on specific aspects of your data. You can mix and match these grouping options to best highlight the relationships and patterns in your Marker expression analysis.
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.
Remember to click "Save changes" to apply any of your customizations!
Cell scatter plot
The default plot for Marker expression analysis is the cell scatter plot. For the cell scatter plot, you can adjust the coordinate system (UMAP, t-SNE, PC) to visualize your data in different reduced-dimensional spaces. Customize the color scale to represent gene expression levels, and define how cells are grouped using cluster annotation sets, variables, or latent variables. This customization helps you explore the spatial distribution of gene expression across different cell groups.
If your dataset contains more than 100,000 cells, it will be downsampled to a maximum of 100,000 cells for cell scatter plots for visualization purposes. You can control the downsampling process by selecting a downsampling number and seed. Using the same downsampling seed across different marker expression analyses in your experiment will ensure that the same subset of cells is used, maintaining consistency in your cell scatter plots.
Ridge plot
Switch from the default plot to a ridge plot by clicking "Ridge Plot". Click "Save changes" to update the plot style. Once the plot has updated to a ridge plot, you can specify how cells are grouped into ridges, using cluster annotation sets, variables, or latent variables. Select colors for each ridge to represent different groups effectively. This customization helps you visualize the distribution of gene expression levels across multiple groups.
Violin plot
Switch from the default plot to a violin plot by clicking "Violin Plot". Click "Save changes" to update the plot style. Once the plot has updated to a violin plot, you can define how cells are grouped into violins using cluster annotation sets, variables, or latent variables. Customize the color palette for each violin to distinguish between different groups. This allows you to compare the distribution of gene expression across various groups and to visually asses variations between them.
If you have any questions, contact your Pluto representative by email or using our in-app chat.