The analysis_summary.html file serves three purposes:
- Provides a convenient overview of data results
- Highlights summary metrics and plots to quickly QC data
- Contains information that 10x Genomics support can use for troubleshooting if needed
This file can be viewed on the instrument, on any web browser, and in 10x Explorer.
At the top, the banner states the Run name, Slide name, and Region name, which are user-defined inputs on the instrument during the setup step. The Run start time is time-stamped by the instrument and shown in UTC.
There are several clickable tabs:
- The Summary tab contains summary metrics, images, and experiment information for a quick overview of the data (default view).
- The Decoding tab contains transcript decoding metrics and plots.
- The Cell Segmentation tab shows metrics for cell segmentation and partitioning transcripts into single cells.
- The Analysis tab captures the results from the pipeline's secondary analysis run on single cell data.
- The Cell Annotation tab contains annotated cell type plots and metrics for human WTA datasets.
- The Image QC tab contains several image galleries, including downsampled RNA images for each cycle and channel, background autofluorescence images, and cell segmentation images.
When certain metrics fall below key thresholds, warning () or error () alerts will be shown below these tabs.
The Key Metrics are a good starting point for data QC. These metrics are helpful indicators if there is a problem with the data and for spotting outliers between multiple runs at a glance.
There are no universal thresholds for these metrics as interpretation requires some understanding of the sample and gene panel used. Researchers should have some understanding of how many cells are expected given the tissue size and type. Here are some important considerations for interpreting key metrics:
- Median transcripts per cell and decoded transcript density will depend on tissue type and gene panel (e.g., WTA panel vs. a 1K gene panel).
- The number of cells detected will depend on tissue type and the size of the region of interest (ROI) selected on the instrument in the analysis.
| Metric | Description |
|---|---|
| Number of cells detected | The expectation for this metric depends on the tissue sample. |
| Median transcripts per cell | The median number of transcripts per cell. Cells with zero transcripts are excluded from the calculation. This metric is expected to be lower if the selected gene panel does not match the tissue type. |
| Median genes per cell | The median number of genes per cell. |
| Total high quality decoded transcripts | The total number of gene transcripts that were decoded with a Q-Score ≥ 20. |
The Sample Region Summary (left) lists information provided during instrument run set up about the sample, selected tissue region, and slide.
The Overview scan (right) shows the nuclei-stained (DAPI) image and the selected region of interest (hover over the image to see number of fields of view (FOVs)). This image is helpful for quickly identifying sample orientation on the slide, as well as the specific region on the slide that was used to generate the data in the Atera output bundle. The image orients the slide with the slide label at the bottom, which is upside-down relative to the slide's orientation when loaded on the instrument.
The Region Details view shows a DAPI image of the analyzed region along with a heatmap overlay that provides a spatial distribution of several metrics per bin. Checking each heatmap separately may help troubleshoot issues that aggregated metrics could miss. This view can be used to QC a variety of common sample preparation issues:
- Does the DAPI morphology image look as expected?
- Were there any tears in the tissue or detachment of tissue from the slide?
- Depending on sample type, does the general transcript density match the tissue morphology? For example, if the tissue is a mouse brain, one would expect to see particularly high density in the hippocampus.
Each heatmap can be adjusted to two bin sizes (20 x 20 µm or 80 x 80 µm length square bins) and various opacities as needed. The 20 x 20 µm bins generally maintain high resolution while smoothing out noise, while the 80 x 80 µm bins help when viewing sparse data such as negative control metrics. Scroll to zoom in/out of the DAPI image and hover your mouse over the image to display information per bin.
- Transcript density spatial map shows binned high quality decoded transcripts.
- Mean Q-score spatial map shows binned average decoded transcript Q-Scores across the sample area. This plot can be used to identify any technical artifacts associated with one or more FOVs. It is normal for mean Q-Score to be low in bins that do not overlap with the tissue and thus have few or no transcripts.
- Negative controls spatial map shows binned counts of negative controls. "Show All" displays all negative control categories together. Select the radio buttons to view just the counts per bin for negative control probes, negative control codewords, or genomic control probes separately. This plot should ideally show a roughly equal spatial distribution of low counts across the sample area. The plot may indicate potential issues if counts are localized, outside the tissue area, or if counts are high.
The Gene Panel section displays information about the panel configuration that was used for the experiment. If the QC metrics look drastically different from what is expected, double-check that the correct panel was used. The source of the metadata is the panel_config.json file.
| Metric | Description |
|---|---|
| Panel type | Specifies whether the panel is pre-designed or custom. |
| Panel name | The human-readable panel name. |
| Species and tissue | Indicates the species and tissue type. |
| Design ID | A unique string ID for the panel. |
| Created by | Indicates who designed the panel. |
| Date created | Indicates when the panel design was created |
| Gene count | Total number of RNA targets on the panel. |
The Run Information section is meant to help researchers quickly determine key metadata regarding the instrument run.
| Metric | Description |
|---|---|
| Run name | A unique identifier that was input in the instrument during the run setup step. |
| Number of slides | Number of slides used in instrument run (up to 2 or 4 total) |
| Cell segmentation | The stain method used for cell segmentation. |
| Run start time | The time recorded by the Atera Instrument in UTC. |
| Region area (mm2) | The total summed area of imaged FOVs with tissue. |
| Total cell area (mm2) | The summed area of detected cells. It is used for calculating transcript density (Decoded transcripts per 100 µm2). |
The Software section shows key metadata for troubleshooting purposes. 10x Genomics continually updates the instrument and analysis software on the Atera Instrument to introduce new features, fix bugs, and provide the best user experience.
| Metric | Description |
|---|---|
| Instrument software version | The version of firmware software used on the instrument (tracks instrument user interface changes as well). See release notes for updates. This instrument software version may not exactly match the Atera Onboard Analysis version. For example, the instrument software may be updated, but if no changes are added to the analysis software, the analysis version will not change. |
| Analysis version | The version of the Atera Onboard Analysis pipeline that performs image processing, decoding, cell segmentation, and secondary analysis, and generates output files. |
| Instrument serial number | A unique ID that may be helpful for troubleshooting with 10x Support. |
| Calibration UUID | Instrument calibration metadata. |
| Cell annotation model | Automated cell type annotation model and version. |
This tab provides plots and metrics to assess transcript decoding quality.
For each gene, the Gene-Specific Transcript Quality plot shows the total number of decoded transcripts of any quality (x-axis) against the mean quality of those decoded transcripts (y-axis). The x-axis (total transcripts per gene) is plotted on a log10 scale, while the y-axis is on a linear scale. Click on a gene or negative control category in the legend to hide it from view.
When interpreting this plot, we typically expect to see all or most genes have mean quality scores ≥ 20, and the controls should be < 20. In other words, genes should be at the top-right quadrant and controls should be in the bottom-left quadrant. Examine genes, especially custom genes, for any performance problems (e.g., low density or low mean Q-Score may indicate problems in the performance of a specific gene).
Negative controls for both codewords and probes should have low transcript counts and low quality:
- A high rate of negative control codewords may indicate possible imaging issues, such as autofluorescence in one or more cycle-channels that are part of a negative control codeword. Check the RNA images in the
qc_images.htmlfile. - High quality and count of all negative control probes may indicate assay workflow issues, such as nonspecific probe hybridization or ligation conditions. It is ok if only a few negative control probes show high quality and/or count.
Data with quality scores < 20 are filtered from the cell-feature matrix, and downstream analyses that use the matrix file, but these filtered transcripts can still be found in the transcripts output. For more information, see Understanding Atera Outputs.
The Counts per Gene plot shows the total number of transcripts passing quality thresholds for every gene in the panel. This plot shows absolute counts of decoded transcripts (x-axis) by gene (y-axis). Genes are ranked by transcript count. Search for specific genes in the search window to the right of the plot. Sort by gene name or transcript count. The "Transcript Rank" column indicates a gene's place in the overall rank of transcript counts.
This plot can be used to quickly compare multiple samples analyzed with the same panel, or pre-designed vs. custom genes.
The Decoding Yield section provides QC metrics at a glance. Biological and experimental context may be needed for interpretation.
| Metric | Description |
|---|---|
| Percent of all gene transcripts that are high quality | The percent of transcripts from all genes that decode with high quality (≥ Q20). For experiments with custom panels, separate metrics are shown for pre-designed and custom genes since QC of custom genes is likely to be particularly important. |
| Total high quality decoded transcripts | The total number of decoded gene transcripts that were decoded with high quality (≥ Q20). |
| Median genes per cell | Median number of genes detected per non-empty cell (reported for each panel codebook). |
| Median transcripts per cell | Median number of Q20 transcripts detected per non-empty cell (reported for each panel codebook). |
| Nuclear transcripts per 100 µm2 | The high-quality, decoded-to-gene, nuclear transcript count divided by the total segmented nuclear area. |
| Thickness of high quality decoded transcripts (in µm) | The width in Z of high-quality transcripts measured in microns. The width is calculated as the difference of the 95th and 5th percentiles averaged over all the acquired fields of view (FOV). We exclude FOVs with fewer than 1000 high-quality transcripts before computing the average. This number typically ranges between 3-10 µm in tissues that are sliced at 5-10 µm. This number is proportional to the physical thickness of the tissue that is input into the assay for the same tissue type. The median high-quality transcripts per cell can be lowered when the measured thickness is lower than expected. Decoding quality scores may be affected if the tissue thickness is higher than expected. |
| Cellular transcripts per 100 µm2 | The high-quality, decoded-to-gene, cellular transcript count (including nuclear transcripts) divided by the total segmented cellular area. |
Below are descriptions for the Negative Controls metrics.
- The adjusted negative control probe rate measures both erroneous decoding and off-target binding, so it will always be greater than or equal to the adjusted negative control codeword rate. Both depend on the total number of high-quality transcripts detected, so a high value may result from lower detection of gene transcripts rather than nonspecific binding.
- Genomic control metrics are output when the gene panel includes genomic control probes and provide a more comprehensive measure of FDR as they measure erroneous decoding, off-target binding, and binding to genomic DNA (gDNA). While the assay is designed to avoid denaturing gDNA, there are other sources of gDNA that could explain high levels in some Atera data. These include sample condition (i.e., necrotic regions), sample preparation method (i.e., under fixation for FFPE tissues, tissue arrays), and/or the biology of the sample (i.e., cancerous tissues).
| Metric | Description |
|---|---|
| Adjusted negative control codeword rate | The estimated rate of false positives caused by erroneous decoding among high-quality (≥ Q20) transcripts. Estimated using negative control codewords such that any decoding to these codewords is definitively erroneous, and adjusted to estimate the rate of errors amongst all codewords. The rate is calculated as the fraction of high-quality transcripts that were assigned to negative control codewords, divided by the fraction of codewords in the panel that are negative control codewords. |
| Adjusted negative control probe rate | The estimated rate of false positive transcript signal caused by erroneous decoding and off-target binding among high-quality (≥ Q20) transcripts. Estimated using negative control probes that should not bind to any transcript sequence present in the tissue, and adjusted to estimate the rate of errors amongst all probes. The rate is calculated as the fraction of high-quality transcripts that were assigned to negative control probes, divided by the fraction of probe-associated codewords in the panel that belong to negative control probes. |
| Adjusted genomic control probe rate | The estimated rate of false positive transcript signal from probes binding to genomic DNA among high quality (Phred quality score ≥ Q20) transcripts. Estimated using intergenic genomic control probes that would bind to intergenic genomic DNA but should not bind to any transcript sequence present in the tissue, and adjusted to estimate the rate of errors amongst all probes. The rate is calculated as the fraction of high quality transcripts that were assigned to genomic control probes, divided by the fraction of probe-associated codewords in the panel that belong to genomic control probes. |
| Negative control probe counts per control per cell | This is the mean number of high-quality transcripts (≥ Q20) assigned to cells that were decoded as negative control probes, which are probes that should not bind to any transcript sequence present in the tissue. The mean is taken across all cells and all negative control probes. More on metric calculations here. |
| Genomic control probe counts per control per cell | The mean number of high-quality transcripts that were decoded as genomic control probes, which are probes that should only bind to intergenic genomic DNA. The mean is taken across all cells and all genomic control probes. More on metric calculations here. |
| Estimated number of false positive transcripts per cell | This is the estimated mean number of high-quality transcripts per cell that do not represent true expression ("false positives"). It is estimated using the "Negative control probe counts per control per cell" metric and adjusted to estimate the number of false positives amongst all gene probes. It should be considered in the context of the total number of transcripts expressed per cell. |
| Estimated number of false positive transcripts per cell including genomic counts | The estimated mean number of high-quality transcripts per cell that do not represent true expression ("false positives") due to decoding, non-specific probe binding, and genomic DNA binding errors. It is estimated using the "genomic control probe counts per control per cell" metric, and adjusted to estimate the number of false positives amongst all gene probes. |
The purpose of cell segmentation is to approximate cell boundaries so that transcripts can be assigned to cells. Downstream, these results will be used to produce a cell-feature matrix, similar to those output by existing single cell and spatial technologies.
Here are descriptions for the Segmentation Metrics:
| Metric | Description |
|---|---|
| Number of cells detected | The total number of cells detected. |
| Percent of transcripts within cells | Percent of high-quality transcripts that are found within cells. Low values can be caused by underdetection of cells or sample preparation issues leading to mis-localized transcripts. Unassigned transcripts are excluded from the cell-feature matrix. |
| Cells per 100 µm2 | The density of cells per 100 microns squared. This metric may vary by tissue type and cell size. |
| Percent of empty cells | Percent of all cells with no decoded high-quality transcripts. This should typically be a low value but an acceptable range for each region is dependent on the gene panel and tissue sample. |
Segmentation method metrics are provided to indicate the number of percent of cells segmented by each multimodal cell segmentation method (contingent on cell segmentation stains used in the assay).
| Metric | Description |
|---|---|
| Cells segmented by boundary stain | Number and percent of cells where the cell segmentation boundary is derived from the boundary stain. |
| Cells segmented by interior stain | Number and percent of cells where the boundary is from expansion of the nucleus using interior stain information. |
| Cells segmented by nucleus expansion | Number and percent of cells where the boundary is an isotropic expansion from the nucleus boundary. Distance is 5.0 µm by default. |
| Total number of cells detected | The total number and percent of cells detected. The sum of the cell categories above. |
For all cells with transcripts, the Cell Size Distribution view shows a histogram of cell area in µm2. The area is computed from the cell segmentation mask.
The Genes per cell view shows a histogram of the total number of unique, panel (non-control) genes found in each cell for all cells with transcripts.
The Transcripts per cell view shows a histogram of the total number of transcripts found in each cell over all panel (non-control) genes for all cells with transcripts.
The Transcripts Per Cell view shows the spatial distribution (left) and UMAP projection (right) of cells colored by the total number of transcripts detected in each cell. For performance reasons, a subset of cells may be plotted for very large samples.
The Clustering view shows the spatial distribution (left) and UMAP projection (right) of cells colored by cluster assignment using the Atera Onboard Analysis pipeline's automated clustering algorithm. The clusters should reflect groups of cells that have similar expression profiles. In the left plot, cells are colored according to their cluster assignment and plotted in their spatial location. Only cells with a nucleus detected by the DAPI stain are used in the clustering algorithm.
In the right plot, the axes correspond to the 2-dimensional embedding produced by the UMAP algorithm. In this space, pairs of cells that are close to each other have more similar gene expression profiles than cells that are distant from each other. For performance reasons, a subset of cells may be plotted for very large samples.
The Top Features by Cluster table displays results from the Atera Onboard Analysis pipeline's automated differential expression analysis. For each cluster, the table shows features that are more highly expressed in that cluster relative to the rest of the sample.
A differential expression test was performed between each cluster and the rest of the sample for each feature.
- The Log2 fold-change (L2FC) is an estimate of the log2 ratio of expression in a cluster to that in all other cells. A value of 1.0 indicates a 2-fold greater expression in the cluster of interest.
- The p-value is a measure of the statistical significance of the expression difference and is based on a negative binomial test. The p-value reported here has been adjusted for multiple testing via the Benjamini-Hochberg procedure.
In this table, you can click on a column to sort by L2FC or p-value for each cluster. Features were filtered (mean object counts > 1.0) and the top N features by L2FC were retained for each cluster. Features with L2FC < 0 or adjusted p-value ≥ 0.10 are grayed out. The number of top features shown per cluster, N, is set to limit the number of table entries shown to 10,000 (N=10,000/K2 where K is the number of clusters). N can range from 1 to 50.
Cell types are annotated with the Pan-human Azimuth model (see algorithm page for more information).
Cell Type Composition
This barchart provides a high-level summary of the coarse cell types present in your sample. By clicking on each bar, you can explore more detailed annotations, revealing the contribution of specific sub-types to the broader cell types. This interactive visualization helps to quickly assess whether the expected cell types are present.
The bars cover only the cells the model annotated, so they do not account for every cell in the region. The cells with fewer than five transcripts are labeled "Unannotated", while the cells that the Pan-human Azimuth model could not assign (e.g., due to low transcripts or ambient RNA) are labeled "Unassigned".
Cell Types
The spatial distribution (left) and UMAP projection (right) of cells is color-coded by the annotated high-level (coarse) cell type. Distinct cell type populations with relevant cell type labels can be used as a starting point for further annotation in downstream analysis. If you notice high-level cell types appearing in low numbers or scattered across the UMAP (e.g., unexpected cell types) these should be carefully reviewed and potentially re-annotated.
Top Features By Cell Type
This view provides another method for quality control of the annotations. For correctly annotated cells, you should expect to see common marker genes. Mis-annotated cells may show features that are not commonly expressed in that cell type.
Transcripts per cell distribution by cell type
A box plot showing the distribution of transcripts per cell across annotated coarse cell types.
This tab provides a gallery of images acquired across cycles of the instrument run. The x- and y-axes are measured in micrometers. The intensity values in the RNA decoding images are in photoelectrons (pe). The intensity values in autofluorescence and cell segmentation images are measured in arbitrary units (a.u.), as the image deconvolution processing steps transform the original image units (pe) units. Interpret data by examining images for brightness patterns.
This gallery shows a stitched and reconstructed fluorescence image of the analyzed region for RNA decoding cycles (row) and channels (column). They are downsampled to 200 x 200 pixels from the high-resolution images.
The first and last cycle images for each decoding block of cycles is shown (i.e., for Human WTA, there will be 10 rows of images; for Panel A or Panel C configurations, there will be two rows of images). See the qc_images.html file to view thumbnail images for all cycles and channels in the run.
This gallery can be useful for quick data QC, such as checking for stitching errors, total cycle dropouts or oversaturation, debris, or low transcript density areas. Patterned changes in fluorescence intensities across cycle-channel plots are a signal of good image quality. For example, in the images below, certain tissue areas are dim in some cycles and bright in others. Fluorescence signals that are always high in the same location in all cycles indicate tissue autofluorescence and may affect transcript data at that location.
This tab includes two rows of morphology images to help with troubleshooting.
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Autofluorescence Images: Thumbnail plots of the autofluorescence images (maximum intensity projection (MIP) images; not deconvolved). These images are subtracted from the raw stain images to produce the autofocused images in the Cell Segmentation images gallery. Use these images to review any debris or autofluorescence artifacts that were automatically subtracted during morphology image processing.
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Cell Segmentation Images: Thumbnail plots of the autofocused, downsampled, and processed morphology images. The interior and boundary stain images in this gallery have undergone background subtraction and stitching.
Very dim images may indicate problems during the morphology staining workflow. Full resolution images can be found in the
morphology_2d/directory, which contains multi-file OME-TIFF autofocus files.