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Discover how to visualize cell clusters on UMAP scatter plots, search for features, explore gene expression with various plot types, and conduct advanced analyses like differential gene expression and pseudotime analysis directly from the explorer page.
After running an analysis, you will be able to visual the clusters of cells on a UMAP scatter plot, search for features, visualize the expression of genes in various types of plots and conduct further analyses, such as differential gene expression and pseudotime analysis on your dataset.
Here we have an overview of the explorer page and the list of tools and features. You can find links to separate pages with details for some of the more extensive tools here as well.
At the very centre of the explorer page is the scatter plot of UMAP_1 and UMAP_2 by default after you have run an analysis. Here you can visual the clustering of cells in your dataset, select cells and annotate the clusters, and so on. You can also change the UMAP to t-sne or view imported coordinates here as well.
From the cell metadata, you can select to view the some of the metrics that can be useful for quality checks on your dataset:
• Transcripts per cell
• Genes per cell
• % of mitochondrial counts
• % of ribosomal counts
• Cell cycling phase scores
You can use the information gathered from the above and reanalyse the dataset again with new parameters. For example, if you find clusters of cells with abnormally high mitochondrial count that’s not relevant to your study, you can choose to reanalyse this dataset and adjust the cell filtering parameters to exclude some of these cells.
The Import and Export buttons are also in this dropdown menu. You can import cell annotations or coordinates here and export the current analysis in a csv file.
You can learn more about:
You can search for a specific gene or feature in this search bar and conveniently visualise the expression of this gene on the UMAP, the bar plot and violin plot (on the right) as well.
Highly variable genes (HVGs) and other modalities (e.g. ADT) will be labelled here as well when you search for the gene or feature. You can also find and copy gene sets and lists of ADTs or HTOs from CITE-seq data.
On the left of the UMAP, we have the tools and options for clusters, imported cell annotations and cluster markers.
The Group by dropdown contains all cluster information from the analysis run and groups for imported cell annotations can be found here as well.
These are the marker genes of a cluster, i.e. genes expressed in the cells of this cluster that distinguishes this cluster from the rest. These gene are scored and can be sorted by their marker score, fold change, etc.
If you have specified the species of your dataset during analysis as human or mouse, you will find the MSigDB gene sets for these markers as well.
An overview of the plot options you may find on the explorer, you can find the detailed information for different plots on the Visualization tools page [link to Visualization tools article page]
The bar plot shows the percentage of cells that express a gene or feature across the clusters. You can change the cluster or group values on the x-axis by choosing a different category from Group by.
You can swap between violin and box plot and quickly visualize how the normalized expression values of a gene or feature is distributed in different clusters/groups.
Visualise cluster composition in a stacked bar plot
A radar plot to visualise and compare the expression of multiple sets of genes in clusters or groups.
A dot plot to visualise the mean expression of genes and the percentage of expression across different clusters.
Create a feature scatter plot using two features of interest.
Details on the parameters used in the current analysis. This also includes the list of HVGs and blocked genes, and a methodology section which you can copy.
Transfer labels from another dataset to matching cells in the current dataset.
Run a pseudotime analysis
Collection of saved selections and tools
Import clonotypes and VDJ data
Create pseudobulking data from different clusters
Add a quick note or comment to the dataset
Our AI-driven insights for the platform which provides reports on disease, quality analysis and auto-annotations for the clusters.