Analysis Methods

A description of the methods used for spatial analysis.

Filtering and normalization

PCA

UMAP

Clustering

Differential Expression

Differential analysis helps identify differential expression patterns in the dataset. For differential expression analysis of spatial data we have implemented Wilcoxon. A nonparametric statistical hypothesis test that compares two groups and does not assume a normal distribution. Our implementation closely matches Scanpy's scanpy.tl.rank_genes_groups when using method='wilcoxon'.

Running Wilcoxon

We recommend normalizing you data prior to running Wilcoxon, but it can be invoked on any counts data node.

• Click the counts data node
• Click the Differential analysis section in the toolbox
• Click Wilcoxon (Figure 1)
• Select the attribute to include in the statistical test (Figure 2), and click Next

Figure 1. Differential expression analysis method selection page.

Figure 2. Attribute selection page.

• Define the comparisons by dragging and dropping each group in the Denominator and Numerator boxes and click Add comparison (Figure 3)

Figure 3. Comparison setup page.

If the data has not been filtered upstream the Low value filter box will be checked by default. You can edit the filtering value and any of the Advanced options as needed (Figure 4). Click Finish to run the task.

Figure 4. Advanced options menu

Marker Gene Identification

Cell Typing