Skip to main content

Spatial phenotype analysis of crisp screens (SpaCr)

Project description

Documentation Status PyPI version Python version Licence: GPL v3 repo size

SpaCr

Spatial phenotype analysis of CRISPR-Cas9 screens (SpaCr). The spatial organization of organelles and proteins within cells constitutes a key level of functional regulation. In the context of infectious disease, the spatial relationships between host cell structures and intracellular pathogens are critical to understanding host clearance mechanisms and how pathogens evade them. SpaCr is a Python-based software package for generating single-cell image data for deep-learning sub-cellular/cellular phenotypic classification from pooled genetic CRISPR-Cas9 screens. SpaCr provides a flexible toolset to extract single-cell images and measurements from high-content cell painting experiments, train deep-learning models to classify cellular/subcellular phenotypes, simulate, and analyze pooled CRISPR-Cas9 imaging screens.

Features

  • Generate Masks: Generate cellpose masks of cell, nuclei, and pathogen objects.

  • Object Measurements: Measurements for each object including scikit-image-regionprops, intensity percentiles, shannon-entropy, pearsons and manders correlations, homogeneity, and radial distribution. Measurements are saved to a SQL database in object-level tables.

  • Crop Images: Save objects (cells, nuclei, pathogen, cytoplasm) as images. Object image paths are saved in a SQL database.

  • Train CNNs or Transformers: Train Torch models to classify single object images.

  • Manual Annotation: Supports manual annotation of single-cell images and segmentation to refine training datasets for training CNNs/Transformers or cellpose, respectively.

  • Finetune Cellpose Models: Adjust pre-existing Cellpose models to your specific dataset for improved performance.

  • Timelapse Data Support: Track objects in timelapse image data.

  • Simulations: Simulate spatial phenotype screens.

  • Sequencing: Map FASTQ reads to barcode and gRNA barcode metadata.

  • Misc: Analyze Ca oscillation, recruitment, infection rate, plaque size/count.

Installation

If using Windows, switch to Linux—it’s free, open-source, and better.

Before installing SpaCr on OSX ensure OpenMP is installed:

brew install libomp

SpaCr GUI requires Tkinter. On Linux, ensure Tkinter is installed. (Tkinter is included with the standard Python installation on macOS and Windows):

sudo apt-get install python3-tk

Install SpaCr with pip:

pip install spacr

Run SpaCr GUI:

spacr

Project details


Release history Release notifications | RSS feed

Download files

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

Source Distribution

spacr-0.3.0.tar.gz (82.5 MB view details)

Uploaded Source

Built Distribution

spacr-0.3.0-py3-none-any.whl (84.2 MB view details)

Uploaded Python 3

File details

Details for the file spacr-0.3.0.tar.gz.

File metadata

  • Download URL: spacr-0.3.0.tar.gz
  • Upload date:
  • Size: 82.5 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.10.12

File hashes

Hashes for spacr-0.3.0.tar.gz
Algorithm Hash digest
SHA256 2452d0435a308518cebbe229a2e0677fb6ed3f7fb7daf86dd9b091c9e1e630d2
MD5 fe87b48586456dcd348ce4869e50717c
BLAKE2b-256 0d2378e17a13ab6f3021b13f50fb4cc8c2b9ab11f458ffd14c4ce7ee75171bf9

See more details on using hashes here.

File details

Details for the file spacr-0.3.0-py3-none-any.whl.

File metadata

  • Download URL: spacr-0.3.0-py3-none-any.whl
  • Upload date:
  • Size: 84.2 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.10.12

File hashes

Hashes for spacr-0.3.0-py3-none-any.whl
Algorithm Hash digest
SHA256 47b2bc1215feacdb275e6f110df79c23b75cfb0300644778bc3a5a32f31eceb2
MD5 6d5aa04f7ee1c4bd6caa6cbd37899844
BLAKE2b-256 f85fb243607e7dee3242f94646cc9586440c5d75b3901ac7c4e258bb6b097b60

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