Section Heading

Section headings should use level 2 heading, while the content of the section should use paragraph (which is the default). You can choose the style in the first dropdown in toolbar.

Next, we will perform some exploratory analysis on the merged mRNA and protein expression data and visualize the data in preparation to identify cell populations. 

PCA

Because the merged count matrix has thousands of features, it is a good idea to reduce the dimensionality of the data for more efficient downstream processing.

• Click the Merged counts data node
• Click Exploratory analysis in the toolbox
• Click PCA
• Click Finish to run the PCA with default settings (Figure ?)

A PCA task node will be added to the pipeline under the Analyses tab and a circular PCA output data node will be produced (Figure ?)

Once the task completes, we will inspect the results to decide the optimal number of principal components (PCs) to use in downstream analyses. To do this, we will use a Scree plot.

• Double click the PCA data node to open the task report

 The PCA plot will open in a new data viewer session. A 3D scatterplot will be displayed on the canvas (Figure ?)

• Click and drag the Scree plot from the Available plots card on the left onto the canvas
• Drop it over the Replace option (Figure ?)

• Select PCA as data for the new Scree plot

The Scree plot (Figure ?) shows the eigenvalues on the y-axis for each of the 100 PCs on the x-axis. The higher the eigenvalue, the more variance explained by each PC. Typically, after an initial set of highly informative PCs, the amount of variance explained by analyzing additional components is minimal. By identifying the point where the Scree plot levels off, you can choose an optimal number of PCs to use in downstream analysis steps like graph-based clustering and UMAP.

• Click and drag over the first set of PCs to zoom in (Figure ?)

• Mouse over the Scree plot to identify the point where additional PCs offer little additional information (Figure ?)

In this data set, a reasonable cut-off could be set anywhere between around 10 and 30 PCs. We will use 15 in downstream steps.

Graph-based clustering

We can use Graph-based clustering to group similar cells together in an unsupervised manner.

• Click the project name near the top to go back to the Analyses tab
• Click the circular PCA data node
• Click Exploratory analysis in the toolbox
• Click Graph-based clustering
• Set the number of principal components to 15 (Figure ?)
• Click Finish to run the task

A Graph-based clustering task node will be added to the pipeline under the Analyses tab and a circular Graph-based clusters output data node will be produced (Figure ?)

UMAP

Once the graph-based clustering task hs completed, we can visualize the results with a UMAP plot.

• Click the circular Graph-based clusters data node
• Click Exploratory analysis in the toolbox
• Click UMAP
• Set the number of principal components to 15 (Figure ?)
• Click Finish to run the task

A UMAP task node will be added to the pipeline under the Analyses tab and a circular UMAP output data node will be produced (Figure ?)

...