We will compare the classification (FASN expression) we previously made based on expression levels of the FASN gene. Here, we will compare FASN high and FASN low cells to identify genes and pathways.
Select the Normalized counts node and choose Compute biomarkers from the Statistics drop-down
Choose the "FASN expression" attribute
Do not select Split by sample
Click Finish
This results in a Biomarkers report.
Double-click the Biomarkers results node to open the report
The top features are reported for the comparison.
Please click here for more information on differential analysis methods.
We will create a list using List management with these 10 genes, so that we can use this list in the Gene set enrichment task.
Click your username in the top right corner
Select Settings from the drop-down
Choose Lists from the Components drop-down in the menu on the left
Use the + New list button to add these 10 genes
Choose Text as the list option
Give the list a Name and Description
Enter the 10 genes in column format as shown below
Click Add list
The list has been added and can now be used for further analysis. The Actions button can be used to modify this list if necessary, as shown below.
Here we are going to perform Gene Set Enrichment on our top 10 features for the FASN high group that we have added as a list called "Top 10 FASN high Features".
Go to the Analyses tab
Select the Normalized counts node
Choose Gene set enrichment from the Biological interpretation drop-down in the task menu
Use the KEGG database for pathway enrichment
Check Specify background gene list
Select "Top 10 FASN high Features" as the Background gene list
Click Finish
This results in a Pathway enrichment report, as shown below.
Double-click the report to view the pathways involved in this list of genes
Please click here for more information on Biological interpretation.
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Download this table with more than 10 features using the Download option
Follow along to add files to your Xenium project: Add files to the project.
Filter the data including control probes using the Filter features task
Choose Feature metadata filter
Include the Gene Expression features
Click Finish
This results in a Filter features task (rectangle) and node (circle) results.
Right click the circle to Rename data node to "Filtered to only gene expression"
Click the circular "Filtered to only gene expression" results node and select the Single cell QA/QC task from the context sensitive menu on the right
When the task completes it will be opaque and no longer transparent with a progress bar
Double click the opaque rectangle task to open and filter cells as described here. Apply the observation filter to the "Filtered to only gene expression" results node. This results in a "Filtered cells node".
Select the "Filtered cells node" and choose the Normalization task from the Normalization and scaling drop-down in the task menu
Click the Use recommended button to proceed with these settings
Click Finish
This results in a Normalized counts node as shown below in the pipeline.
If you need additional assistance, please visit our support page to submit a help ticket or find phone numbers for regional support.
The project includes Human Breast Cancer (In Situ Replicate 1) and Human Breast Cancer (In Situ Replicate 2) files in one project.
Obtain the Xenium Output Bundles (Figure 1) for each sample.
Navigate the options to select 10x Genomics Xenium Output Bundle as the file format for input. Choose to import 10x Genomics Xenium for your project (Figure 2).
Click Transfer files on the homepage, under settings, or during import.
You will need to decompress the Xenium Output Bundle zip file before they are uploaded to the server. After decompression, you can drag and drop the entire folder into the Transfer files dialog, all individual files in the folder will be listed in the Transfer files dialog after drag & drop, with no folder structure (Figure 4). The folder structure will be restored after upload is completed.
The Xenium output bundle should be included for each sample (Figure 5). Each sample requires the whole sample folder or a folder containing these 6 files: cell_feature_matrix.h5, cells.csv.gz, cell_boundaries.csv.gz, nucleus_boundaries.csv.gz, transcripts.csv.gz, morphology_focus.ome.tif. Once added, the Cells and Features values will update. You can choose an annotation file during import that matches what was used to generate the feature count.
Do not limit cells with a total read count since Xenium data is targeted to less features.
Once the download completes, the sample table will appear in the Metadata tab, with one row per sample (Figure 6).
The sample table is pre-populated with one 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. Cell attributes are found under Sample attributes and can be added by publishing cell attributes to a project.
For this tutorial, we do not need to edit or change sample attributes.
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 + Add sample button then use the green Add sample button to add each sample's Xenium output bundle folder. If you have not already transferred the folder to the server, this can be done using Transfer files to the server (Figure 3).
Once uploaded the folder to the server, navigate to the appropriate folder for each sample using Add sample (Figure 5).
Click the Normalized counts data node
Expand the Exploratory analysis section of the task menu
Click PCA
In this tutorial we will modify the PCA task parameters, to not split by sample, to keep the cells from both samples on the PCA output.
Uncheck (de-select) the Split by sample checkbox under Grouping
Click Finish
Double-click the circular PCA node to view the results
From this PCA node, further exploratory tasks can be performed (e.g. t-SNE, UMAP, and Graph-based clustering).
Choose Style under Configure
Color by and search for fasn by typing the name
Select FASN from the drop-down
The colors can be customized by selecting the color palette then using the color drop-downs as shown below.
Ensure the colors are distinguishable such as in the image above using a blue and green scale for Maximum and Minimum, respectively.
Click FASN in the legend to make it draggable (pale green background) and continue to drag and drop FASN to Add criteria within the Select & Filter Tool
Hover over the slider to see the distribution of FASN expression
Multiple gene thresholds can be used in this type of classification by performing this step with multiple markers.
Drag the slider to select the population of cells expressing high FASN (the cutoff here is 10 or the middle of the distribution).
Click Classify under Tools
Click Classify selection
Give the classification a name "FASN high"
Under the Select & Filter tool, choose Filter to exclude the selected cells
Exit all Tools and Configure options
Click the "X" in the right corner
Use the rectangle selection mode on the PCA to select all of the points on the image
This results in 147538 cells selected.
Open Classify
Click Classify selection and name this population of cells "FASN low"
Click Apply classifications and give the classification a name "FASN expression"
Now we will be able to use this classification in downstream applications (e.g. differential analysis).
If you need additional assistance, please visit our support page to submit a help ticket or find phone numbers for regional support.
