Code Ocean User Guide
v3.6.0
v3.6.0
  • Welcome to Code Ocean
  • Onboarding
    • Quick Start Guides
      • Create a Capsule in 5 minutes
      • Create a Pipeline in 5 minutes
    • Video Library
  • Key Concepts
  • Capsule Guide
    • The Capsule Interface
      • File Navigation/App Builder Panel
      • Editor Panel
      • Reproducibility Panel
    • The Structure of a Capsule
      • Metadata
      • Environment
      • Code
      • Data
      • .codeocean
      • Scratch
      • Results
    • Reproducible Runs
    • Version Control
      • Importing a Capsule from a Git Provider
      • Clone via Git...
    • Managing Capsules
      • Sharing a Capsule with Secrets
      • Exporting Capsules to your Local Machine
    • Secret Management Guide
      • Setting a Secret in the Account Settings Page
      • Accessing a Secret in a Capsule
  • Setting up the Environment
    • Selecting a Starter Environment
    • Package Managers and Adding Packages
    • Installing R packages
    • Environment Variables
    • Build Log
    • Post-Install Script
    • Compute Resources
  • Working in a Cloud Workstation
    • Launching a Cloud Workstation
      • Running RShiny in Code Ocean
      • Running Streamlit in Code Ocean
      • Using Terminal in Code Ocean
      • Using Ubuntu Desktop in Code Ocean
      • Using MATLAB in Code Ocean
      • Using VS Code in Code Ocean
    • Exiting a Cloud Workstation Session
  • Data Assets Guide
    • Types of Data Assets
    • Creating a New Data Asset
    • Capturing a Result
      • Provenance of the Result Data Asset
    • Using Data Assets in a Capsule
    • Managing Data Assets
      • Finding Data Assets
        • Custom Metadata
  • Pipeline Guide
    • The Pipeline UI
      • File Tree System
      • Pipeline App Panel
      • Timeline
    • Components of a Pipeline
      • Nextflow File
      • Capsules
      • Data
      • Results Bucket
      • Map Paths
      • Capsule Settings
      • Pipeline Settings
      • .codeocean
    • Managing Pipelines
      • Exporting Pipelines to your Local Machine
    • Capsule vs Pipeline Differences
    • How to Run Code in Parallel
    • Nextflow Configurations
    • Pipeline Monitoring
      • Monitoring Dashboard
      • Task Details
    • Nextflow Artifacts
    • nf-core Pipelines
      • Import nf-core pipelines
      • nf-core RNASeq Tutorial
        • iGenomes
    • Pipeline Tutorial
  • Release Capsules and Pipelines
    • Creating and Using Release Capsules
    • Creating Release Pipelines
    • Internal Releases
  • Collections
  • Code Ocean Apps
    • Genomics
    • Cheminformatics
    • Visualization
    • Machine Learning
    • Data Connectors
    • Quality Control
    • Use Cases and Examples
      • CombFold Pipeline
      • Data Connector Example
      • Gene Set Enrichment Overview
      • RNASeq Quantification Pipeline
  • App Panel Guide
    • App Builder User Interface
      • Validation pattern for strings
    • Passing App Panel's Parameters to the Script
    • Releasing an App Panel Capsule
    • Executing an App Panel Capsule
  • MLflow Guide
    • Enable MLflow Tracking
    • MLflow UI
    • Creating Models
    • MLflow Permissions
  • Models Guide
    • Creating Models
    • Managing Models
  • Git Provider Integration Guide
    • Setting up the Integration
    • Using Git Provider Integration
  • Code Ocean API
    • Authentication
    • Errors
    • Capsule
    • Computation
    • Data Asset
    • Python SDK
  • More Support
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  1. Code Ocean Apps

Visualization

PreviousCheminformaticsNextMachine Learning

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Title
Description
Input Data

Wally visualizations

This Capsule creates a genomic alignment plot of reads in a specified region mapped to a reference. The visualization of genomic alignments was heavily inspired by IGV, and customized to visualize structural variation and CNVs.

  • Genome .fasta reference and .fai file

  • .bam files

Samplot Visualization (from coordinates)

This Capsule is a command line tool for rapid, multi-sample structural variant visualization.

  • compare sheet

  • .bam files

Dose response

This Capsule produces dose response curves that describe the magnitude of the response as a function of exposure (dose) to a condition after a certain exposure time.

  • .csv file containing gene expression and treatment dosage (see README for details)

DESeq2

This Capsule performs differential gene expression analysis with potential input types: 1. Transcript abundance; 2. Counts data; 3. HTseq-counts data; 4. Summarized experiment in RDS format.

  • Input varies depending on the source (i.e., Seurat vs RSEM) see README for details.

Mol* Viewer for PDB Files

This Capsule allows you to view PDB files interactively on a Streamlit cloud workstation using the Mol* 3D Viewer.

  • Protein structure files in .pdb format

TensorBoard GPU

This Capsules allows users to view model experiment metrics like loss and accuracy, visualizing the model graph, project embeddings to a lower dimensional space and more. This capsule shows an example from the Tensorboard website.

This Capsule runs on GPU instances and comes preloaded with Tensorflow.

  • None

RShiny Cell

ShinyCell is an R package that allows users to create interactive Shiny-based web applications to visualize single-cell data.

  • Single cell .rds inputs from Seurat (see README)

1-3. Single Cell Analysis Tutorial (Scanpy & Seurat)

Tutorials to describe working with Single Cell data for Scanpy and Seurat:

1. Preprocessing and clustering 3k PBMCs

2. Core Plotting Functions

3. How to preprocess UMI count data with analytic Pearson residuals

  • Tutorial datasets (see README for details)

5-6. Single Cell Analysis Tutorial (Scanpy)

Tutorials demonstrating how to regress cell cycle effect and how to simulate data using a literature-curated boolean gene regulatory network.

  • Tutorial datasets (see README for details)

7-10. Single Cell Analysis Tutorial (Scanpy) Advanced

Tutorials for advanced Single Cell processing.

  • Tutorial datasets (see README for details)

MLflow Orchestrator Capsule

This is a central MLflow "Orchestrator" capsule that can be used to interactively view the results from multiple machine learning capsules using the same MLflow database.

  • None