Aquila | Tutorial

Browse Data

Find the entry to view all datasets

Select a dataset that you interested

Brief introduction to data card?

View the data

What each section do?

Perform spatial analysis

Good to go!Last step

Analyze Your Data

Find the entry point for analysis

Prepare 3 files

Specify a Data ID

Submit the files

Check your resultLast step

What does each analysis do?

Cell components

Proportion of each cell type

CELL TYPE

Simple statistics on the proportion of different cell types

Cell density

Density of different of different cells

CELL TYPE

Simple statistics on the density of different cell types

Cell expression

Marker expression in different cell types

CELL TYPE

Co-expression

Marker co-expression

The expression correlation between two or more markers

Spatial Co-expression

Marker co-expression with spatial information

NEIGHBORS

The correlation between two or more markers expression at cells neighbors

Ripley Statistics

The distribution characteristics at different distance range

CELL TYPE

Few functions can be used profile the distribution of different cells at different distance range

Cell distribution

Distribution pattern for each cell type

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

Spatial community

Find the cell community based on neighbor graph

NEIGHBORS

Using graph community detection algorithms to cut the graph into different communities.

Cell centrality

How how a cell is connected to other cells

CELL TYPE

NEIGHBORS

Calculate different centrality metrics based on neighbor graph

Cell-Cell interaction

Check if two type of cell tend to stick together

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

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