File Name: web_summary_cell_types.html

Description: A standalone web summary that presents key statistics and visualizations related to the annotation of your sample. This interactive file provides high-level metrics and plots, allowing you to explore the distribution and characteristics of cell types in your dataset.

Key summary visualizations and tables generated from running the annotation pipeline on a publicly available 10x Genomics dataset are described below.

Cell Type Composition Barchart

This chart provides a high-level summary of the cell types present in your sample. By clicking on each bar, you can explore more detailed annotations, revealing the contribution of specific subtypes to the broader cell types. This interactive visualization helps you quickly assess whether the expected cell types are present and suggests potential subtypes within the sample.

UMAP projections

The UMAP projection of cells is color-coded by the annotated high-level cell type. Distinct cell type populations with relevant cell type labels can be used as a starting point for further annotation in Loupe. If you notice high-level cell types appearing in low numbers or scattered across the UMAP—particularly unexpected cell types—these should be carefully reviewed and potentially re-annotated during further analysis.

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.

File Name: cell_annotation_sample_cloupe.cloupe

Description: A new .cloupe file is generated, which includes coarse cell types in the "Custom Groups" section. By default, this group is labeled "Cell Types," but the name can be customized during the annotation analysis setup.

File Name: cell_annotation_results.json.gz

Description: This file is a compressed JSON containing a list of dictionaries. Each element in the list represents the annotation results from a single barcode, derived from the cell annotation model.

For each barcode, the corresponding dictionary includes the top 500 matches obtained using an approximate-Nearest Neighbor (ANN) lookup. These matches are summarized for the total number of occurrences for a given cell type. While more cells supporting a particular annotation can increase your confidence in the annotation, occasionally the most common nearest-neighbor cell type can have a low number of supporting cells because the nearest-neighbors are split amongst several highly similar cell types (e.g., 'Cd16-Negative, Cd56-Bright Natural Killer Cell, Human' and 'Cd16-Negative, Cd56-Dim Natural Killer Cell'). The dataset_id corresponds to the Chan Zuckerberg CELL by GENE (CZ CELLxGENE) study from which the annotation was derived. To view this study, insert the id into this URL: https://cellxgene.cziscience.com/e/{dataset_id}.cxg/.

An example output is shown below:

Copy
{
        "barcode": "AAACCAAAGAATGCAA-1",
        "matches": [
            {
                "cell_count_in_model": 32,
                "cell_type": "monocyte",
                "dataset_ids_with_counts": [
                    {
                        "count_per_dataset": 30,
                        "dataset_id": "87ce26ed-e5d1-44b4-81cc-cc5b709a169f"
                    },
                    {
                        "count_per_dataset": 2,
                        "dataset_id": "b0e547f0-462b-4f81-b31b-5b0a5d96f537"
                    }
                ]
            },

File Name: cell_types.csv

Description: This file contains the cell type annotation for each barcode and can be used to import the fine-scale cell type annotations directly into Loupe Browser.

The file contains four columns:

• barcode: The cell barcode being annotated
• coarse_cell_type: The broad classification or high-level annotation of the cell (e.g., T Cell, B Cell, Neutrophil, etc.)
• fine_cell_type: The original annotation derived from the model based on the most common cell type amongst the 500 nearest-neighbors. Note: This may be the same as coarse_cell_type if the original reference was only annotated to that level of detail.
• cell_count_in_model: The number of cells in the model that support the given fine_cell_type annotation, with a maximum of 500 cells.

An example is shown below:

Copy
1 barcode             coarse_cell_type  fine_cell_type          cell_count_in_model
2 AAACCAAAGAATGCAA-1  monocyte          CD14-positive monocyte  454
3 AAACCAAAGAGCCGAA-1  monocyte          monocyte                217
4 AAACCAAAGCACTCCC-1  T cell            "central memory CD4-positive alpha-beta T cell"  370

File Name: cell_annotation_differential_expression.csv

Description: This file contains the results of a differential expression analysis conducted between coarse cell types. These differentially expressed genes can be used to check that the cell type contains the expected marker genes. The pipeline uses the same algorithm employed in Cell Ranger and Loupe Browser to calculate fold changes and p-values, ensuring consistency within these platforms.