• Visualize the annotated image from the automatically generated Spatial report task

• Visualize the annotated image from the Annotate Visium image task

• Modify Style

Visualize the annotated image from the automatically generated Spatial report task

• Click Analyses to switch to the Analyses tab

For now, the Analyses tab has a starting node, a circular node called Single cell counts and also a rectangular task node called Spatial report which was automatically generated for this type of data. As you , forming a visual representation of your analysis pipeline.

• Click the Spatial report node

• Click Task report on the task menu

The spatial report will display the first sample (Replicate 1). We want to visualize all of the samples using the steps below.

• Duplicate the plot by clicking the Duplicate plot button in the upper right controls (arrow 1)

• Open the Axes configuration option (arrow 2)

• Change the Sample on the duplicated image under Misc (arrow 3)

Each data point is a tissue spot. Duplicate and change the sample to view multiple samples.

Visualize the annotated image from the Annotate Visium image task

If starting with unprocessed , the Annotate Visium image task will create a new result node, Annotated counts.

• Double click on the Annotated counts node to invoke the Data Viewer showing data points overlaid on top of the microscopy image

• Follow the steps outlined above by duplicating the image to visualize the multiple samples

Modify Style

To modify the points on the image to show more of the background image use the Style configuration option.

• Press and hold Ctrl or Shift to select both plots

• Click Style in the left panel

• Move the Opacity slider to the left

• Click Save in the left panel and give the session an appropriate name

Modify Axes

Modify the axes to remove the X and Y coordinates from the tissue image.

• Press and hold Ctrl or Shift to select both plots

• Click Axes in the left panel

• Toggle off Show lines for both the X & Y axis

Color by gene expression and attributes

Style the image and color by normalized gene expression using three genes of interest.

• Press and hold Ctrl or Shift to select both plots

• Click Style

• Click the blue circle node to the right of the Color by drop-down

To color by the which we previously determined in this tutorial, use the Color by drop-down and select Cell Type. Cell type is a blue categorical attribute while green attributes are numerical.

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In this tutorial, we demonstrate how to:

• Start with pre-processed Space Ranger output files

• Start with 10x Genomics Visium fastq files

• Spatial data analysis steps

Tutorial Data Set

The tutorial data is based on .

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Space Ranger output files are pre-processed 10x Genomics Visium data. The steps covered here will show you how to import and continue analyses with this pre-processed data from the Space Ranger pipeline. Partek Flow refers to this high cellular resolution data as Single cell counts; each point (spot) can be 1-10 cell resolution depending on the tissue type*.

Add files to the project

The project includes Human Colon Cancer (Replicate 1) and Human Colon Cancer (Replicate 2) output files in one project.

• Obtain the filtered Count matrix files (h5 or HDF5) files and Spatial outputs for each sample

The spatial imaging outputs should be in compressed format.

• Navigate the options to select 10x Genomics Visium Space Ranger output as the file format for input

• Click on the homepage, under settings, or during import

Proceed to transfer files as shown below using the 10x Genomics Visium Space Ranger outputs importer.

• Navigate to the appropriate files for each sample.

Please note that the 10x Genomics Space Ranger output can be count matrix data as 1 filtered .h5 file per sample or sparse matrix files for each sample as 3 files (two .csv with one .mtx or two .tsv with one .mtx for each sample).

The spatial output files should be compressed in one .gz or zip file when uploaded to Partek Flow server. This should include these related image files: tissue_hires_image.png, tissue_lowres_image.png, aligned_fiducials.jpg, detected_tissue_image.jpg, tissue_positions_list.csv, scalefactors_json.json.

The high resolution image can be uploaded and is optional.

Count matrix files and spatial outputs should be included for each sample. Once added, the Cells and Features values will update.

Once the download completes, the sample table will appear in the Metadata tab, with one row per sample.

The sample table is pre-populated with sample attributes, # Cells. Sample attributes can be added and edited manually by clicking Manage in the Sample attributes menu on the left. If a new attribute is added, click Assign values to assign samples to different groups. Alternatively, you can use the Assign values from a file option to assign sample attributes using a tab-delimited text file. For more information about sample attributes, see .

For this tutorial, we do not need to edit or change any sample attributes.

Resources

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• Add files to the project

• Pre-processing the unaligned fastq files with Space Ranger

• Annotate Visium image

The fastq files are not pre-processed. The steps covered here will show you how to import and pre-process of the Visium Spatial Gene Expression data with brightfield and fluorescence microscope images.

Add files to the project

The sample used for this tutorial can be found in the . We will use the .

• Choose the 10x Genomics Visium fastq import format

• Click Next

If you have not already, and choose to Transfer files to the server.

• Select the fastq files in the upload folder used for file transfer (select all sample files at one time; including R1 and R2 for each sample)

• Click Finish

The prefix used for R1 and R2 fastq files should match; one sample is shown in this example.

The fastq files will be imported into the project as an Unaligned reads node.

Pre-processing the unaligned fastq files with Space Ranger

The unaligned reads must be preprocessed before proceeding with the analysis steps covered here: .

• From the unaligned reads node, select Space Ranger from the 10x Genomics drop-down in the toolbox.

• Specify the type of 10x Visium assay; this tutorial uses the Visium CytAssist gene expression library as the assay type

• If you have not done so already, a Cell Ranger reference should be created

• Specify the Reference assembly

The Space Ranger task output results in a Single cell counts node.

Annotate Visium image

The tissue image must be annotated to associate the microscopy image with the expression data.

• Click the newly created Single cell counts data node

• Click the Annotation/Metadata section in the toolbox

• Click Annotate Visium image

Select the zipped image folder for each sample. The image zip file should contain 6 files including image files and tissue position text file with a scale factor json file. The setup page shows the sample table (one sample per row).

You can find the location of the _spatial.zip file using the following steps. Select the Space Ranger task node (i.e. the rectangle) and then click on the Task Details (toolbox). Click on the Output files link to open the page with the list of files created by the Space Ranger task. Mouse over any of the files to see the directory in which the file is located. The figure below shows the path to the .zip file which is required for Annotate Visium image.

Mousing over a file on the Output files page shows a balloon with the file location.

A new data node, Annotated counts, will be generated.

The Annotated counts node is Split by sample. This means that any tasks performed from this node will also be split by sample. Invoke tasks from the Single cell counts node to combine samples for analyses.

Annotate Visium image task creates a new node, Annotated counts. Double click on the Annotated counts node to invoke the Data Viewer showing data points overlaid on top of the microscopy image.

Data Viewer session as a result of opening an Annotated counts data node. Each data point is a tissue spot.

Proceed with analysis from the Single cell counts node.

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• Visium data analysis pipeline

• Performing tasks in the Analyses tab

• Filter Features

Here we are .

Visium data analysis pipeline

A basic example of a spatial data analysis, starting from the Single cell counts node, is shown below and is similar to with the addition of the Spatial report task (shown) or Annotate Visium image task (not shown).

Note that QA/QC has not been performed in this example, to visualize all spots (points) on the tissue image. Single cell QA/QC can be performed from the Single cell counts node with the filtered cells applied to the Single cell counts before the Filter features task. .

Performing tasks in the Analyses tab

A context-sensitive menu will appear on the right side of the pipeline. Use the drop-downs in the toolbox to open available tasks for the selected data node.

Low-quality cells can be filtered out during the spatial data analysis using QA/QC and will not be viewed on the tissue image. . We will not perform Single cell QA/QC in this tutorial; this task would be invoked from the Single cell counts node and the Filter features task discussed below would be invoked from this output node (Filtered counts).

Filter Features

Remove gene expression counts that are not relevant to the analysis.

• Click the Filtering drop-down in the toolbox

• Click the Filter Features task

• Choose Noise reduction

Remove gene expression values that are zero in the majority of the cells.

A task node, Filtered counts, is produced. Initially, the node will be semi-transparent to indicate that it has been queued, but not completed. A progress bar will appear on the Filter features task node to indicate that the task is running.

Normalization

Normalize (transform) the cells to account for variability between cells.

• Select the Filtered Counts result node

• Choose the Normalization task from the toolbox

• Click Use recommended

• Click Finish

Exploratory analysis

Explore the data by dimension reduction and clustering methods.

• Click the Normalized counts result node

• Select the PCA task under Exploratory analysis in the toolbox

• Unselect Split by Sample

The PCA result node generated by the PCA task can be visualized by double-clicking the circular node.

• Single click the PCA result node

• Select the Graph-based clustering task from the toolbox

• Click Finish

The results of graph-based clustering can be viewed by PCA, UMAP, or t-SNE. Follow the steps outlined below to generate a UMAP.

• Select the Graph-based clustering result node by single click

• Select the UMAP task from the toolbox

• Click Finish

• Double-click the UMAP result node

The UMAP is automatically colored by the graph-based clustering result in the previous node. To change the color, click Style.

Automatic classification

Classify the cells using to determine cell types.

• Click the Filtered counts node

• From the Classification drop-down in the toolbox, select Classify cell type

• Using the Managed classifiers, select the human Intestine Garnett classifier

The output of this task produces the Classify result node.

Double-click the Classify result node to view the cell count for each cell type and the top marker features for each cell type.

Publish cell attributes to project

Publish cell attributes to the project to make this attribute accessible for downstream applications.

• Click the Classify result node

• Select Publish cell attributes to project under Annotation/Metadata

• Select cell_type from the drop-down and click the green Add button

Publish cell attributes can be applied to result nodes with cell annotation (e.g. click the graph-based clustering result node and follow the same steps).

An example of this completed task is shown below.

Since this attribute has been published, we can choose to right-click the Publish cell attributes to project node and remove this from the pipeline. This attribute will be managed in the Metadata tab (discussed below).

Modify cell attribute

The name of the Cell attribute can be changed in the Metadata tab (right of the Analyses tab).

• Click Manage

• Click the Action dots

• Choose Modify attribute

Drag and drop the categories to rearrange the order of these categories, The order here will determine the plotted order and legend in visualizations.

We can use these Cell attributes in analyses tasks such as Statistics (e.g. differential analysis comparisons) as well as to Style the visualizations in the Data Viewer.

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