Transcriptic

Getting Started With Jupyter

To interactively work with protocols and data, we recommend using Jupyter Notebook, formerly known IPython. The Transcriptic Command Line Interface comes with a a Python library that makes working with the Transcriptic API very easy from the IPython environment.

from transcriptic import run
from transcriptic.config import Connection

Connection("me@example.com", "my_API_Key", "my-organization-id")

run_data = run("my-run-id")

If you've previously logged into the Transcriptic CLI using the transcriptic login command, you can create a Connection from the cached credentials:

from transcriptic import run
from transcriptic.config import Connection

Connection.from_file("~/.transcriptic")

run_data = run("my-run-id")

Easy Data Accessor Functions

There are a variety of helper functions to make getting data easy. They work by automatically picking up on a previously-created Connection object, so you must create a connection before using them. You don't, however, need to pass in the connection as a parameter - they pick up on it automatically.

run(id: string)
project(id: string)
dataset(id: string)
container(id: string)
aliquot(id: string)
resource(id: string)
analyze(protocol: dict, test_mode: bool)
submit(protocol: dict, project: string, title: string, test_mode: bool) 

The Connection object also contains many methods for interacting with your Transcriptic account; it can be thought of as an "organization" class.

org = Connection("me@example.com", "my_API_key", "my-organization-id")

project = org.create_project("Example Project")
run_list = project.runs() # will be an empty list, since you just created this project

package_list = org.packages()

Getting Started With Jupyter


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