DNAnexus Apollo builds on the technological foundation of the core DNAnexus Platform to offer scientists and bioinformaticians an environment to store and query large sets of genomic, phenotypic, multi-omic, and other structured data. Researchers can bring their data to the Platform and leverage DNAnexus apps to ingest the data into queryable databases.
These databases can then be explored using the Cohort Browser. Scientists can filter the dataset by any data field and save these filtered samples as cohorts. These cohorts can be shared with other scientists and also can be used as inputs to analysis apps to perform such tasks as calculating allele frequencies or performing a GWAS analysis.
The Cohort Browser dashboard can show up to three tabs based on the configuration of the dataset: Overview, Data Preview, and Genomics. Tabs are loaded as the user clicks on them, so if there is no change in filtering, the tabs will stay cached and will not need to reload.
Bioinformaticians who wish to perform ad hoc statistical analysis are able to create JupyterLab environments backed by Spark clusters to directly query their data and create dataframes within a Python or R environment for further analysis.
The legacy Cohort Browser will be deprecated on September 15, 2021. Your system will be automatically upgraded to use the new Cohort Browser. If you would like to opt into using the new Cohort Browser early, follow the instructions in this video.
From the project where a dataset is located, go to Manage tab and select your dataset of interest. Click on the Explore Data action to open this dataset in Cohort Browser.
You can also access datasets via the Datasets page, which is located under the Projects menu. The Datasets page displays all datasets you have access to, and enables you to browse and find a specific dataset without navigating through projects.
You can use the optional information panel to view further information about a selected dataset, including creator, sponsorship, etc.
The Overview tab in the Cohort Browser provides insights on the data through the visualization of various data fields.
To add a field as a chart, click on Add Tile button. The Add Tile dialog shows a hierarchical view of all the data fields available in the dataset.
Browse the list or search an item by its title to narrow down the list.
Select a data field from the list. In the Data Field Details panel, you can see metadata on the selected data field, visualization preview, as well as options to customize chart types.
Confirm selection via the Add as Tile action. Now this tile will appear on your dashboard.
Once you have selected a primary data field in the Add Tile dialogue, you can add a secondary data field by clicking on the "+" icon that appears next to a data field item.
This video provides a detailed overview of exploring new datasets using the Cohort Browser:
When you start exploration on a dataset, an empty cohort is created automatically in the cohort browser. You can further narrow down your cohort by adding cohort filters. Cohorts created can be saved and exported for later use.
From the cohort which you wish to edit, click on Add Filter button.
Select a data field you want to filter by, confirm by clicking on Add as Filter.
Select operators and enter values to filter by. Click on Apply Filter to confirm.
Filters added are displayed in corresponding cohort panels. You can edit a specific filter any time by clicking on it, which would bring up the Edit Filter dialogue.
The default logical operator is 'AND'. To switch the operator to 'OR', click on the operator. For a filter group (a set of filters tied to 1 specific entity), all operators will be the same: all 'OR' or all 'AND'.
Once filters are added or edited, an updated cohort size will appear under name of the affected cohort. The dashboard will also auto-refresh to fetch updated results basing on latest cohort selection.
From the cohort you wish to edit, click on Add Filter button.
Toggle to Geno tab.
Edit filter in Edit Genomic Filter dialogue by one of the following criteria:
Filter by genes and variant effects: Filter your dataset by variants of certain types and consequences within specified genes and/or genomic ranges. A maximum of 5 genes/ranges can be entered.
Filter by a list of variant IDs. A maximum of 100 variants can be entered.
Confirm edit by clicking on Apply Geno Filter button.
When working with datasets that have multiple data entities, you can create a Join Filter by selecting data fields from a secondary entity and adding them as filters. An entity is a grouping of data around a unique item, event, or a concept: e.g. patient, visit, medication, laboratory tests.
Join Filters are displayed as subrows deriving from the main entity. Depending on the entity to which your selected data field belongs, a join filter that reflects the relationship between those entities will be automatically created. To create a new cohort criteria using the join filters, click + Add filter or the Filter > Add filter on a tile. To add additional criteria to an existing criteria in a join click the Add additional criteria inline on the row of the chosen filter.
You can choose between the 'AND' or 'OR' logical operators when creating a cohort and comparing join filters. To switch between them, click on the logical operator. For a specific level of join filtering, joins are either all 'AND' or all 'OR'. Note that even when using 'OR' for two join filters, the implication that "this criteria exists" precedes the join level, i.e. “where exists, join 1 or join 2”.
Once a join filter is created, you can further define the secondary entity by adding additional criteria to the branch, or adding more layers of join filters deriving from the current branch. As you add more layers, the field selector automatically hides fields that are ineligible to be added based on the join.
For an example of interpreting join filters, consider the following:
The First Example cohort identifies all patients with a "high" or "medium" risk level who have a first (visit instance = 1) hospital visit and who also have had a lab test that was a "nasal swab". This lab test does not necessarily have to be conducted at the time of the patient’s first hospital visit. In the Second Example, the cohort includes all patients with a "high" or "medium" risk level who had the "nasal swab" test performed on the first visit.
This video provides an overview of setting up your dashboard as part of defining and refining a cohort:
In the Data Preview tab, the Cohort Table shows records that are within your current cohort selection. You can add or remove data fields as columns via the column customization menu, which is located in the top-right corner of the table.
Click on table column headers to access more functionalities including sorting and searching in a specific column.
You can export table information either as a list of record IDs or a CSV file. Export options are available on the top-right corner of the table once you have selected a number of table rows.
In the Genomics tab, the Variant Browser shows variants that are present in current cohort selection. This section includes a lollipop chart displaying allele frequencies for variants in a specified genomic region.
You can modify the genomic region via the search bar on the top-right corner of the variant section. This genomic region will update information in both lollipop chart and table.
The table below the lollipop chart lists the same variants in tabular format, along with further annotation information including:
Type: whether the variant is a SNP, deletion, insertion, or mixed.
Consequences: The impact of variant according to SNPEff. For variants with multiple gene annotations, this column displays the most severe consequence per gene.
Population Allele Frequency: Allele frequency calculated across entire dataset from which the cohort is created.
Cohort Allele Frequency: Allele frequency calculated across current cohort selection.
GnomAD Allele Frequency: Allele frequency of the specified allele from the public dataset GnomAD.
To view further annotation information, you can go to the detail page of a given variant by clicking on the link in the Location column .
You can export selected variants in the table as a list of variant IDs or a CSV file. Export options will appear at the top-right corner of the table once you have items selected.
You can save your cohort selection to a project as a Class: Record object by clicking the Save icon in the top-right corner of the cohort panel.
Cohorts will be saved with the filters applied, along with the latest set of visualizations and dashboard layout information. Similar to Dataset objects, Cohort objects can be found under the Manage tab in your selected projects, and can be re-opened via the Explore Data option.
You can export a list of main entity IDs in your current cohort selection as a CSV file. This action can be found next to the Save Cohort button, on the top-right corner of cohort panel.
Dashboard views contain layout and configuration information that can be re-used during cohort browsing. You can save or load a dashboard view via the Views option menu located at the top-right corner of the header area.
Dashboard views are saved as "Type: DashboardView" objects, which once saved also show up in selected project folders.
You can compare two cohorts by adding both cohorts into the Cohort Browser. In cohort compare mode, all visualizations are converted to show data from both cohorts.
The Compare Cohort action can be found in the header area next to the cohort title. You can create a new cohort, duplicate the current cohort, or load a previously saved cohort.
In compare mode, you can continue to edit both cohorts and visualize the results dynamically.