Spatial differential expression

Modified on Thu, 09 Feb 2023 at 10:38 AM

The spatial differential expression algortihm also you to perform differential expression analysis on spatial data, utilizing special algorithms that take into account the spatial component of the gene expression data.

1 Algorithm settings

1.1 Creating a plot 

As a first step in running the analysis, a plot must be created by clicking on the create plot icon in your analysis track. This will lead to a section where the analysis of interest can be selected.

In order to ensure efficient organization, a name and description must be assigned to the analysis under the appropriate fields. Subsequently under "Choose algorithm to run your analysis" Spatial analysis must be selected.

1.2 Selecting data

In the field "Choose track element", the input analysis can be selected. In the cell selection tab you can choose the observations to use as input. For more information see the section on Cell/sample selection.

1.3 Selecting parameters

There are two available algorithms. Mark variogram for which you can set the r value at which to report the “trans” value of the mark variogram and number of top highly varying features to subset by. The Moran's I algorithm has no additional parameters to set.

2. Spatial differential expression interactive plot page

Once you run the spatial differential expression algorithm you can explore the results interactively in the spatial differential expression interactive plot page.

As soon as the differential analysis is computed, a volcano plot will appear. A volcano plot shows the relationship between the p-values of a statistical test and the magnitude of the difference in expression/abundance values between the reference and experimental groups. Each dot in the plot is a feature. On the y-axis the -log10 p-values are plotted. On the x-axis the (log) fold change is plotted. By default, the blue color represents the significant features (p < 0.05), and the grey color represents the non significant features (p >= 0.05).


2.1 Plot parameters

You can also look at the differential analysis table by clicking on Show data table in the Visualization - Plot parameters tab. The table is interactive and sortable by clicking on column headers.
The columns in the table are:
Feature: the name of the feature.
Log fold change: represents the magnitude of the difference between the reference and experimental groups. If the value is positive, we say that the feature is upregulated in the experimental group (and downregulated in the reference group). If the value is negative, we say that the feature is downregulated in the experimental group (and upregulated in the reference group). It is important to note that this value actually represents the log fold change only if the data is in log-space. If the data was not log transformed, this value is just the difference between the experimental group mean and the reference group mean.
Average expression: the average expression/abundance across all observations used in the differential analysis.
P-value: the significance of the result. Usually the significance threshold is set at 0.05.
Adjust p-value: adjusted p-values calculated with the Benjamini-Hochberg procedure (false discovery rate, FDR).

2.2 Highlight features

In the Highlight features tab you can set the parameters to decide which features to highlight on the volcano plot. This can be done either by metric or by providing a custom list of features.

2.2.1 Highlight features - gene metrics

When highlighting features by metric you can select the following parameters:

Filter on adjusted p-value: toggles filtering on adjusted p-value.
p-value threshold: sets p-value threshold  for a feature to be highlighted.
Minimal absolute log fold change value: sets minimal absolute logFC value for a feature to be highlighted.
Highlight color: sets color of highlighted features. 
Background color: sets color of background features. 

2.2.2 Highlight features - custom gene list

When highlighting features by a custom gene list you can select the following parameters:
Background color: sets color of background features.
Number of custom groups: sets the number of custom groups of features. For each custom group you can provide the list of features and set the color. 
Custom gene set color: sets color for the custom gene set highlighted.
Features to highlight: here you can input a list of features to highlight, or search for a feature to select.

2.3 Marker format and color for numerical variables

In the Marker format and color tab you can change the appearance of the markers on the volcano plot. 

Marker symbol: change marker symbol.
Marker sizeadjust marker size.
Marker opacity: adjust marker opacity.

2.4 Details 

The Details tab contains additonal options for customizing your plot. 

2.4.1 Grid style

Show grid: toggles grid.
Grid widthadjusts grid width.
Grid color: changes grid color.
Border width: changes width of plot border. 

2.4.2 Title style

Title: sets plot title.
Title font size: adjusts plot title font size.
Legend position x-direction: changes plot title position on the x axis.
Legend position y-direction: changes plot title position on the y axis.

2.4.3 Plot margins

Margin bottom: sets bottom margin.
Margin leftsets left margin.
Margin right: sets right margin.
Margin top: sets top margin.
Padding: adjusts margin padding.

2.5 Axes style

Here you can edit the axis style for the x,y and z axes.

Axis labelsets axis label.
Axis padding: adjusts axis padding.
Invert axis: inverts axis.
Dimension to plot on axis: set dimesion to plot on axis. In PCA you can generate plot from different pricinple components using this option.

2.6 Export settings

Here you can prepare your plot for export.

Export formatsets plot file format.
Width of plot: adjusts plot width.
Height of plot: adjusts plot height.
File name: set file name for exported plot.

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