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To ensure optimal assay performance, we advise you review the panel summary below and accept the relevant recommendations.

  • Your Current Panel shows the state of your current panel design based on your gene list and reference data
  • Optimized Panel shows the optimized panel design if you were to accept all recommendations shown below

How to interpret this?

This plot shows the predicted per-cell type TP10k utilization (a combination of all the genes' expression profiles for each cell type) based on the reference dataset you provided.

To ensure optimal results, check the utilization values for each cell type to be approximately below 400 TP10k. High utilization for a cell type can lead to optical crowding which results in reduced detection sensitivity (the fraction of transcripts detected per cell) and limits accurate quantification of lowly expressed genes in affected cells.

39 cell types have extremely high utilization and will likely experience optical crowding

CD4-positive helper T cell, CD4-positive, alpha-beta T cell and 37 others exceed the recommended limit of 600 TP10k.

See all problematic cell types
CD4-positive helper T cell, CD4-positive, alpha-beta T cell, CD8-positive, alpha-beta memory T cell, IgA plasma cell, IgG plasma cell, T cell, activated CD4-positive, alpha-beta T cell, activated CD8-positive, alpha-beta T cell, alternatively activated macrophage, basal cell, capillary endothelial cell, class switched memory B cell, classical monocyte, conventional dendritic cell, effector memory CD4-positive, alpha-beta T cell, effector memory CD8-positive, alpha-beta T cell, endothelial cell of artery, endothelial cell of lymphatic vessel, fibroblast, gamma-delta T cell, inflammatory macrophage, luminal epithelial cell of mammary gland, lymphocyte, macrophage, mammary gland epithelial cell, mast cell, mature NK T cell, myeloid cell, myeloid dendritic cell, naive B cell, natural killer cell, neutrophil, non-classical monocyte, pericyte, plasmacytoid dendritic cell, regulatory T cell, unswitched memory B cell, vascular associated smooth muscle cell, vein endothelial cell

Note

To avoid optical crowding in cell types with high utilization, we recommend excluding extremely highly expressed genes.

Please see Panel Utilization per Gene for our recommendations on what you can do with specific genes.

How to interpret this?

This plot shows the predicted per-gene TP10k utilization alongside the corresponding cell types associated with each gene.

To ensure optimal results, check that the utilization values for each gene to be below approximately 120 TP10k. High utilization for a gene can lead to optical crowding which results in reduced detection sensitivity and limits accurate quantification of lowly expressed genes in affected cells.

2 genes are too highly expressed and will likely lead to optical crowding

FTH1 and MALAT1 exceed the recommended limit of 120 TP10k.

15 genes are broadly and highly expressed

AKR1C1, B2M, CXCL2, DST, FOS, FTH1, KRT14, KRT7, KRT8, MALAT1 and 5 others are highly expressed in many cell types and may limit your ability to design a performant panel. You may wish to remove them and replace with more cell type-specific genes.

See all problematic genes
AKR1C1, B2M, CXCL2, DST, FOS, FTH1, KRT14, KRT7, KRT8, MALAT1, NEAT1, SPARCL1, TACSTD2, TOMM7, VIM

1 gene was excluded (too highly expressed)

We made changes to MALAT1 in order to avoid optical crowding issues.

If you choose to include genes that are this highly expressed, there is a high risk of decreased sensitivity in the affected cells. See an example here.

1 gene was excluded (contributed to optical crowding)

We made changes to FTH1 in order to avoid optical crowding issues.

Alternatively, you can replace the gene with lower-expressed genes by modifying your gene list. If you choose to include highly expressed genes, there is a potential risk of decreased sensitivity and a limited ability to accurately quantify lowly expressed genes in the affected cells.

1 gene was reduced to 4 probesets

We made changes to VIM in order to avoid optical crowding issues.

Alternatively, you can replace the gene with lower-expressed genes by modifying your gene list. If you choose to include highly expressed genes, there is a potential risk of decreased sensitivity and a limited ability to accurately quantify lowly expressed genes in the affected cells.

1 gene was reduced to 6 probesets

We made changes to B2M in order to avoid optical crowding issues.

Alternatively, you can replace the gene with lower-expressed genes by modifying your gene list. If you choose to include highly expressed genes, there is a potential risk of decreased sensitivity and a limited ability to accurately quantify lowly expressed genes in the affected cells.

Note

We left AKR1C1, B2M, CXCL2, DST, FOS, KRT14, KRT7, KRT8, NEAT1, SPARCL1 and 3 others on your panel. However, to maximize your panel's utility, we recommend you choose one of the following options, if possible:

  • Reduce the number of probesets to prevent them from contributing to optical crowding across many cell types
  • Remove these genes and replace them with more cell type specific genes

Learn more in our Xenium Panel Design Technical Note.

See all problematic genes
AKR1C1, B2M, CXCL2, DST, FOS, KRT14, KRT7, KRT8, NEAT1, SPARCL1, TACSTD2, TOMM7, VIM

How to interpret this?

This section shows if you have genes on your panel which we have identified as potentially undesirable on your Xenium panel. We currently test for:

  1. Extremely high expressors in TCGA data for tissues/conditions relevant to your selection. This is often a good indicator that a gene will cause optical crowding issues
  2. Genes which have caused degraded assay performance in R&D panels

3 genes may be undesirable

You may wish to reconsider adding HLA-DQA1, HLA-DQB1 and MTRNR2L11 to your panel. In general, we find that the following provide limited benefits on a Xenium panel:

  1. Mitochondrial genes can cause significantly degraded assay performance due to extremely high, broad expression
  2. Ribosomal genes can lead to the same phenotype
  3. HLA genes are highly polymorphic, and also generally highly expressed

Note

We left these genes on your panel, but recommend excluding them. Changing your reference data will not affect this alert.

How to interpret this?

This plot shows the number of probesets for each gene on your panel. The effectiveness of gene detection and subsequent sensitivity is correlated with the number of probesets used.

To ensure your genes have robust detection, make sure they have at least three probesets.

In some instances, the number of probesets will be less than the default of 8 because we either could not design 8 probes for a gene, or because one or more of the probes were removed as they were predicted to interact with other probes on your panel.

3 genes may have reduced sensitivity

GDF15 (1), SNCG (2) and SPI1 (2) have a small number of probesets on your panel. Sensitivity may be lower than expected for these genes.

Note

We cannot design the default of 8 probesets for GDF15 (1), SNCG (2) and SPI1 (2) due to sequence characteristics (see details) or utilization limits. Sensitivity may be lower than expected and you may wish to remove them.

How to interpret this?

This plot shows hierarchical clustering of the single cell data you selected, subset to genes on your panel. A higher z-score (calculated across cell types) indicates that the gene is expressed at a higher level in that cell type compared to other cell types in the dataset.

To ensure optimal results, you should make sure that:

  1. Each cell type forms a distinct pattern, indicating that you will likely be able to identify the relevant cell types in your Xenium data
  2. Each cell type has an appropriate number of marker genes (according to how deeply you intend to profile the cell type)
  3. A gene is not expressed uniformly across all cell types
  4. Your panel genes are present in the reference dataset

All reference data looks good!

Every gene on your panel is present in the reference data.