Skip to main content

Quantitative mosaic analysis of Drosophila imaginal discs.

Project description

Fly-QMA

Fly-QMA Overview

Fly-QMA is part of the NU FlyEye platform for quantitative analysis of Drosophila imaginal discs. The package enables Quantitative Mosaic Analysis (QMA) - that is, it helps users quantify and analyze expression patterns in mosaic tissues.

Expression patterns are typically identified by comparing the intensities of fluorescent reporters between groups of cells. Fly-QMA uses computer vision to quantify these differences in reporter expression by inferring them from microscope images. The measurements may then used to detect and analyze spatial patterns that might otherwise go unnoticed.

Given microscopy data, Fly-QMA facilitates:

  • Automated detection of cell nuclei
  • Automated measurement of reporter expression levels
  • Automated bleedthrough control for enhanced measurement accuracy
  • Automated annotation of clonal patch patterns
  • Statistical analysis of expression levels and tissue morphology

Please visit the Fly-QMA homepage for tips on getting started with your own data.

Installation

Installing Fly-QMA is easy. Set up a working environment running Python 3.6+, then install via pip:

pip install flyqma

Getting Started

See the Fly-QMA tutorial.

Additioanl Examples

For examples of complete projects utilizing Fly-QMA and the entire NU FlyEye platform, check out:

Authors

Amaral Lab

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

flyqma-0.4.tar.gz (95.3 kB view details)

Uploaded Source

File details

Details for the file flyqma-0.4.tar.gz.

File metadata

  • Download URL: flyqma-0.4.tar.gz
  • Upload date:
  • Size: 95.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.23.0 setuptools/45.1.0 requests-toolbelt/0.9.1 tqdm/4.50.1 CPython/3.6.4

File hashes

Hashes for flyqma-0.4.tar.gz
Algorithm Hash digest
SHA256 37b5b437f64f0c95031834601a7fe060e895f8468b78dd5110c7f703cf0a7e70
MD5 7b568f27fdc78787aa9dda0d28871eda
BLAKE2b-256 e4b66520cf47b62762d9455040f8ca43eee2e05dd281223a5b6c8457b8308e5d

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page