Introduction

RNA sequencing methods have traditionally lacked positional context, but with the recent development of spatial transcriptomics this valuable information can now be recorded and analysed. There are many ways to generate spatial information, but the basic principle underpinning spatial transcriptomics is to associate a transcript with a unique positional identifier. The most popular identifiers come in the form of gene specific barcoded-fluorescent probes, or alternatively micro-arrays with probes unique to each array well. These initial data generation methods will in turn determine the available resolution of the information provided (Multi-cellular, cellular, sub-cellular). 

Subsequent analysis and plotting can prove a highly valuable tool in a wide variety of circumstances. Some of the more common use cases for spatial analysis include annotating tissue images to clearly see the locations of various cell type clusters, the elucidation of cell-cell interactions, as well as the visual description of sub-cellular structure and transcriptome organization. 

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

The spatial expression visualization algorithm does not have any available parameters at the moment. You can proceed to run the analysis by clicking the Run button.

2. Spatial expression visualization interactive plot page

In the spatial expression visualization interactive plot page you can interactively explore the expression or metadata values overlayed on the histology slide.

2.1 Select input

Once all input analyses have been selected, algorithm parameters can be specified. Firstly you can choose which slide to visualize.

There are two options for the type of data to plot, the original features, engineered features or metadata provided by the user. The user can then select for which features the dots of the spatial plot will be color-coded for with the Features to plot menu.

2.2 Plot parameters

After selecting the type of data and specific features to be plotted, the parameters of the plot can be modified. A toggle to crop the slides can be switched on or off. The level of opacity/opaqueness can be modified. The size of individual spatial spots can be scaled. The opacity/opaqueness of the initially provided tissue image can be modified using a sliding scale. And finally, a toggle to show the labels. 

2.3 Marker format and color

Here the color scale that is applied to the dots can be set.

1.3.4 Export settings

File export format, length and width settings as well as the output file's name can be specified within the export settings tab. Once set, clicking the blue download plot button will export the final plot.