Aquila | Tutorial

Browse Data

Find the entry for browse

Select a dataset that you interested

View the dataLast step

Analyze Your Data

Find the entry point for analysis

Prepare 3 files

Specifiy a Data ID

Submit the files

Check your resultLast step

Using Analyze Panel

Overview

Data Summary

Select ROI

ROI Viewer

Run spatial analysisLast step

What does each analysis do?

Cell components

Proportion of each cell type

SINGLE CELL

CELL TYPE

Simple statistics on the proportion of different cell types

Cell density

Density of different of different cells

SINGLE CELL

CELL TYPE

Simple statistics on the density of different cell types

Cell distribution

Distribution pattern for each cell type

SINGLE CELL

CELL TYPE

There are three patterns

1) Random

2) Cluster: Cells are aggregated together

3) Evenly distributed: This is very common to see

Find neighbors

Get to know the neighbors for each cells

You can know whether two cells are neighbors to each other, in the visualization, two cell will be linked if they are neighbors

Cell-Cell interaction

Check if two type of cell tend to stick together

SINGLE CELL

CELL TYPE

NEIGHBORS

This is to determine the spatial interaction between two cell type, either association or avoidance. Association means they are likely to appear at each others neighborhood mostly. Notice that we use a permutation method here, the results are NOT deterministic.

Spatial entropy

Evaluation of the tissue heterogeneity with spatial factor

SINGLE CELL

CELL TYPE

Useful to evaluate the heterogeneity within a tissue

Spatial auto-correlation

Correlation between expression and nearby spatial location

NEIGHBORS

Value close to -1 and is significant indicate negative spatial auto-correlation and vice versa.

Spatial variable gene

Whether the gene expression is dependent on spatial location

NEIGHBORS

If a gene is spatial variable, it suggest that spatial factor has certain influence on it's expression