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.
The sample used for this tutorial can be found in the 10x Genomics Datasets. We will use the Control, replicate 1 mouse brain sample.
Choose the 10x Genomics Visium fastq import format
Click Next
If you have not transferred files to the server already, click here for more details 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.
The unaligned reads must be preprocessed before proceeding with the analysis steps covered here: Spatial data analysis.
From the unaligned reads node, select Space Ranger from the 10x Genomics drop-down in the toolbox.
For more information about Space Ranger click here.
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
Select the Image and Probe set files that have already been transferred to the server for all samples
Choose visium-2-large as the Slide parameter because this Visium CystAssist sample used a 11 x 11 slide capture area
Click Finish
The Space Ranger task output results in a Single cell counts node.
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
Click on the Browse button to open the file browser and point to the file _spatial.zip, created by the Space Ranger task
Click Finish
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. Click here to learn about viewing the multiple tissue images in the Data Viewer.
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Here we are .
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).
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.
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
Exclude features where value <= 0.0 in at least 99.0% of the cells
Click Finish
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.
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
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
Click Finish
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.
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
Click Finish
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 the project to make this attribute accessible for downstream applications.
Click the Classify result node
Select Publish cell attributes to project under Annotation/Metadata
Name the cell attribute
Click Finish
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).
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
Rename the attribute Cell Type
Click Save
Click Back to metadata tab
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.
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. .
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).
Classify the cells using to determine cell types.
Select cell_type from the drop-down and click the green Add button
If you need additional assistance, please visit to submit a help ticket or find phone numbers for regional support.
In this tutorial, we demonstrate how to:
The tutorial data is based on 10x Genomics Datasets.
If you need additional assistance, please visit our support page to submit a help ticket or find phone numbers for regional support.
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*.
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 Transfer files 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 in compressed format (.zip). 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 here.
For this tutorial, we do not need to edit or change any sample attributes.
If you need additional assistance, please visit our support page to submit a help ticket or find phone numbers for regional support.
With the pre-processed samples imported, we can begin analysis.
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 perform analyses, additional nodes representing tasks and new data will be created, 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.
If starting with unprocessed fastq files, 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
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
Change the Point size to 3
Click Save in the left panel and give the session an appropriate name
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
Toggle off Show title and Show axis for both the X & Y axis
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
Select the Normalized counts node as the source
Choose to Color by Numeric triad
Use the Green drop-down to select IL32, Red drop-down to select DES, and Blue drop-down to select PTGDS genes (type in name of gene in drop-down)
Increase the Point size to 11
To color by the Cell attribute "Cell Type" 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.
If you need additional assistance, please visit our support page to submit a help ticket or find phone numbers for regional support.
Click the blue circle node to the right of the Color by drop-down
