DNAnexus Documentation
APIDownloadsIndex of dx CommandsLegal
  • Overview
  • Getting Started
    • DNAnexus Essentials
    • Key Concepts
      • Projects
      • Organizations
      • Apps and Workflows
    • User Interface Quickstart
    • Command Line Quickstart
    • Developer Quickstart
    • Developer Tutorials
      • Bash
        • Bash Helpers
        • Distributed by Chr (sh)
        • Distributed by Region (sh)
        • SAMtools count
        • TensorBoard Example Web App
        • Git Dependency
        • Mkfifo and dx cat
        • Parallel by Region (sh)
        • Parallel xargs by Chr
        • Precompiled Binary
        • R Shiny Example Web App
      • Python
        • Dash Example Web App
        • Distributed by Region (py)
        • Parallel by Chr (py)
        • Parallel by Region (py)
        • Pysam
      • Web App(let) Tutorials
        • Dash Example Web App
        • TensorBoard Example Web App
      • Concurrent Computing Tutorials
        • Distributed
          • Distributed by Region (sh)
          • Distributed by Chr (sh)
          • Distributed by Region (py)
        • Parallel
          • Parallel by Chr (py)
          • Parallel by Region (py)
          • Parallel by Region (sh)
          • Parallel xargs by Chr
  • User
    • Login and Logout
    • Projects
      • Project Navigation
      • Path Resolution
    • Running Apps and Workflows
      • Running Apps and Applets
      • Running Workflows
      • Running Nextflow Pipelines
      • Running Batch Jobs
      • Monitoring Executions
      • Job Notifications
      • Job Lifecycle
      • Executions and Time Limits
      • Executions and Cost and Spending Limits
      • Smart Reuse (Job Reuse)
      • Apps and Workflows Glossary
      • Tools List
    • Cohort Browser
      • Chart Types
        • Row Chart
        • Histogram
        • Box Plot
        • List View
        • Grouped Box Plot
        • Stacked Row Chart
        • Scatter Plot
        • Kaplan-Meier Survival Curve
      • Locus Details Page
    • Using DXJupyterLab
      • DXJupyterLab Quickstart
      • Running DXJupyterLab
        • FreeSurfer in DXJupyterLab
      • Spark Cluster-Enabled DXJupyterLab
        • Exploring and Querying Datasets
      • Stata in DXJupyterLab
      • Running Older Versions of DXJupyterLab
      • DXJupyterLab Reference
    • Using Spark
      • Apollo Apps
      • Connect to Thrift
      • Example Applications
        • CSV Loader
        • SQL Runner
        • VCF Loader
      • VCF Preprocessing
    • Environment Variables
    • Objects
      • Describing Data Objects
      • Searching Data Objects
      • Visualizing Data
      • Filtering Objects and Jobs
      • Archiving Files
      • Relational Database Clusters
      • Symlinks
      • Uploading and Downloading Files
        • Small File Sets
          • dx upload
          • dx download
        • Batch
          • Upload Agent
          • Download Agent
    • Platform IDs
    • Organization Member Guide
    • Index of dx commands
  • Developer
    • Developing Portable Pipelines
      • dxCompiler
    • Cloud Workstation
    • Apps
      • Introduction to Building Apps
      • App Build Process
      • Advanced Applet Tutorial
      • Bash Apps
      • Python Apps
      • Spark Apps
        • Table Exporter
        • DX Spark Submit Utility
      • HTTPS Apps
        • Isolated Browsing for HTTPS Apps
      • Transitioning from Applets to Apps
      • Third Party and Community Apps
        • Community App Guidelines
        • Third Party App Style Guide
        • Third Party App Publishing Checklist
      • App Metadata
      • App Permissions
      • App Execution Environment
        • Connecting to Jobs
      • Dependency Management
        • Asset Build Process
        • Docker Images
        • Python package installation in Ubuntu 24.04 AEE
      • Job Identity Tokens for Access to Clouds and Third-Party Services
      • Enabling Web Application Users to Log In with DNAnexus Credentials
      • Types of Errors
    • Workflows
      • Importing Workflows
      • Introduction to Building Workflows
      • Building and Running Workflows
      • Workflow Build Process
      • Versioning and Publishing Global Workflows
      • Workflow Metadata
    • Ingesting Data
      • Molecular Expression Assay Loader
        • Common Errors
        • Example Usage
        • Example Input
      • Data Model Loader
        • Data Ingestion Key Steps
        • Ingestion Data Types
        • Data Files Used by the Data Model Loader
        • Troubleshooting
      • Dataset Extender
        • Using Dataset Extender
    • Dataset Management
      • Rebase Cohorts and Dashboards
      • Assay Dataset Merger
      • Clinical Dataset Merger
    • Apollo Datasets
      • Dataset Versions
      • Cohorts
    • Creating Custom Viewers
    • Client Libraries
      • Support for Python 3
    • Walkthroughs
      • Creating a Mixed Phenotypic Assay Dataset
      • Guide for Ingesting a Simple Four Table Dataset
    • DNAnexus API
      • Entity IDs
      • Protocols
      • Authentication
      • Regions
      • Nonces
      • Users
      • Organizations
      • OIDC Clients
      • Data Containers
        • Folders and Deletion
        • Cloning
        • Project API Methods
        • Project Permissions and Sharing
      • Data Object Lifecycle
        • Types
        • Object Details
        • Visibility
      • Data Object Metadata
        • Name
        • Properties
        • Tags
      • Data Object Classes
        • Records
        • Files
        • Databases
        • Drives
        • DBClusters
      • Running Analyses
        • I/O and Run Specifications
        • Instance Types
        • Job Input and Output
        • Applets and Entry Points
        • Apps
        • Workflows and Analyses
        • Global Workflows
        • Containers for Execution
      • Search
      • System Methods
      • Directory of API Methods
      • DNAnexus Service Limits
  • Administrator
    • Billing
    • Org Management
    • Single Sign-On
    • Audit Trail
    • Integrating with External Services
    • Portal Setup
    • GxP
      • Controlled Tool Access (allowed executables)
  • Science Corner
    • Scientific Guides
      • Somatic Small Variant and CNV Discovery Workflow Walkthrough
      • SAIGE GWAS Walkthrough
      • LocusZoom DNAnexus App
      • Human Reference Genomes
    • Using Hail to Analyze Genomic Data
    • Open-Source Tools by DNAnexus Scientists
    • Using IGV Locally with DNAnexus
  • Downloads
  • FAQs
    • EOL Documentation
      • Python 3 Support and Python 2 End of Life (EOL)
    • Automating Analysis Workflow
    • Backups of Customer Data
    • Developing Apps and Applets
    • Importing Data
    • Platform Uptime
    • Legal and Compliance
    • Sharing and Collaboration
    • Product Version Numbering
  • Release Notes
  • Technical Support
  • Legal
Powered by GitBook

Copyright 2025 DNAnexus

On this page
  • When to Use Grouped Box Plots
  • Using Grouped Box Plots in the Cohort Browser
  • Non-Numeric Data in Grouped Box Plots
  • Outliers
  • Grouped Box Plots in Cohort Compare
  • Preparing Data for Visualization in Grouped Box Plots
  • Primary Field
  • Secondary Field

Was this helpful?

Export as PDF
  1. User
  2. Cohort Browser
  3. Chart Types

Grouped Box Plot

Learn to build and use grouped box plots in the Cohort Browser.

Last updated 1 year ago

Was this helpful?

The Cohort Browser is accessible to all users of the UK Biobank Research Analysis Platform and the Our Future Health Trusted Research Environment.

For DNAnexus Platform users, an Apollo license is required to access the Cohort Browser. for more information.

When to Use Grouped Box Plots

Grouped box plots can be used to compare the distribution of values in a field containing numerical data, across different groups in a cohort. In a grouped box plot, each such group is defined by its members sharing the same value in another field that contains categorical data.

When creating a grouped box plot, note that:

  • The primary field must contain categorical or categorical multiple data

  • The primary field must contain no more than 15 distinct category values

  • The secondary field must contain numerical data

Supported Data Types

Primary Field

Secondary Field

Using Grouped Box Plots in the Cohort Browser

The grouped box plot below shows a cohort that has been broken down into groups, according to the value in a field Doctor. For each group, a box plot provides detail on the reported Visit Feeling, for cohort members who share a doctor:

Non-Numeric Data in Grouped Box Plots

In some cases, a field containing numeric data may also contain some non-numeric values. These values cannot be represented in a grouped box plot. See the chart just above for an example of the informational message that will show below the chart, in this scenario.

Clicking the "non-numeric values" link will display detail on those values, and the number of record in which each appears:

Outliers

Cohort Browser grouped box plots represent all non-null numeric values. When a field contains an outlier value or values - that is, values that are unusually high or low - this can result in a grouped box plot that looks like this:

This grouped box plot displays data on the number of cups of coffee consumed per day, by members of different groups in a particular cohort, with groups defined by shared value in a field Coffee type. Note that in several groups, one member was recorded as consuming far more cups of coffee per day than others in the group.

Grouped Box Plots in Cohort Compare

In Cohort Compare mode, a grouped box plot can be used to compare the distribution of values in a field that's common to both cohorts, across groups defined using values in a categorical field that is also common to both cohorts.

In this scenario, a separate, color-coded box plot is displayed for each group in each cohort.

Hovering over one of these box plots opens an informational window showing detail on the distribution of values for the group in question.

Clicking the "ˇ" icon, in the lower right corner of the tile containing the chart, opens a tooltip showing the cohort names and the colors used to represent data in each.

Preparing Data for Visualization in Grouped Box Plots

Primary Field

  • String Categorical

  • String Categorical Multi-Select

  • String Categorical Sparse

  • Integer Categorical

  • Integer Categorical Multi-Select

Secondary Field

  • Integer

  • Integer Sparse

  • Float

  • Float Sparse

Categorical or Categorical Multiple (<=15 categories)

Numerical (Integer) or Numerical (Float)

When , note that the following data types can be visualized in grouped box plots:

ingesting data using Data Model Loader
Contact DNAnexus Sales
Grouped Box Plot
Grouped Box Plot: Detail on Non-Numeric Values
Outlier Value in a Grouped Box Plot
Grouped Box Plot in cohort compare mode