TIM, September 9^th 2019

From science to software:

Developing the celltrackR package for analyzing immune cell migration data

Inge Wortel
inge.wortel@radboudumc.nl
Department of Tumor Immunology, Radboudumc

Why do we make software?

Why we build and release software:

Impact:
Make methods available to larger audience.
Reproducibility:
Make analyses completely reproducible by someone else.
Reproducibility:
Make analyses completely reproducible by someone else.
Transparancy (open source!):
Allow exact comparison between different studies/analyses.

Movement is crucial in the immune system

T cells in healthy LN

A. Peixoto, Harvard Medical School

Neutrophils in infected lung

Chtanova et al, Immunity 2008.

Subtle differences, large effects!

Two of these cells just move differently by chance; the other has a different movement pattern. Can you find it?

Subtle differences, large effects!

Two of these cells just move differently by chance; the other has a different movement pattern. Can you find it?

Cell directions:

Subtle differences, large effects!

How do we quantify movement? Extracting cell "tracks"

Lots of software already available:

€€€

ImarisTrack:

MetaMorph:

Volocity:

Celltracker:

Free

Icy:

Cellprofiler Tracer:

TrackMate (ImageJ):

How do we quantify movement? Statistics of migration

Most software quantifies tracks with some basic metrics, such as speed and displacement.

No single metric can tell the whole story:

How do we quantify movement? Statistics of migration

These cells move at the same speed, but have different directionality. We only see this when we look beyond speed:

The field of track analysis

Currently, no software supports all the required analyses. Many papers with novel methods keep coming out, but they generally use custom scripts.

Aim: build software for in-depth, exploratory analysis of cell tracks.

This software must:

Improve impact, reproducibility, and transparency of track analysis methods,
Be complete(-ish)
Be flexible

Step 1: Format and Audience

Graphical User Interface		Code It Yourself
+	No programming required	-	Need programming skills
-	Not very flexible: Only predefined methods	+	Very flexible: Can do anything

$\rightarrow$ Format: something in between that is easy and flexible.
$\rightarrow$ Audience: biologists with some (limited) coding skills.

Step 2: Language

R language:

Free and completely open-source
Combine code, output, and text
Popular among data scientists & bioinformaticians
Many tools for visualization & statistical analysis

Format: an R package

What is an R package?

An R package is a collection of functions that do things you might want to do.

Advantages:

Easy to install
Automatically installs any other required software
Combine with other R packages of interest
Works on Windows/mac/linux

Step 3: Speaking about functions... Which ones do we need?

Literature search yields a list of "metrics":

Step 3: Speaking about functions... Which ones do we need?

T cells and Neutrophils:

Cell-based versus step-based analyses

The same metric can be computed in different ways:

Cell-based versus step-based analyses

The same metric can be computed in different ways:

Angle analysis can help reveal chemotaxis

Fast forward: the end product

Step 4: Document

So now our software can do lots of stuff... Which is useless if nobody knows how!

Software documentation:

Document each function: help pages
Explain the analysis process: tutorials/"vignettes"

Step 4: Document

So now our software can do lots of stuff... Which is useless if nobody knows how!

Software documentation:

Document each function: help pages
Explain the analysis process: tutorials/"vignettes"
Overview of functionalities: cheat sheet

$\rightarrow$ Often forgotten in scientific software...

Step 5: Release & Publish

Releasing an R package:

Structure content so R recognizes functions & documentation
Check that everything works
Choose a license: who can use & adjust?
$\rightarrow$ GPL: open-source, free use & adaptation.
Upload to Github or CRAN