There are 1 repository under cellprofiler topic.
Open-source software for exploring and analyzing large, high-dimensional image-derived data.
A curated list of software, tools, pipelines, plugins etc. for image analysis related to biological questions.
A pixel classification based multiplexed image segmentation pipeline
Python package for processing image-based profiling data
A curated list of awesome cytodata resources
Run encapsulated docker containers with CellProfiler in the Amazon Web Services infrastructure.
Image-based Profiling Handbook
Transform CellProfiler and DeepProfiler data for processing image-based profiling readouts with Pycytominer and other Cytomining tools.
High-dimensional phenotyping to define the genetic basis of cellular morphology
slideToolkit: a free toolset for analyzing wholeslide high-resolution digital histological images.
Running cellprofiler on eddie3 / SGE clusters
Image-based profiling and machine learning to predict failing vs. non-failing cardiac fibroblasts
Single cell analysis of the JUMP Cell Painting consortium pilot data (cpg0000)
Tutorial for single cell analysis of nuclear translocation measured by timelapse imaging
Tools for Processing Results from CellPainting Assay
Install script for CellProfiler v3.1.9 on Ubuntu 18.04.3 LTS(+) - bash, installs python3.6, unet and classify
Singularity-based linux container with CellProfiler 4 pre-installed into it.
Documentation and scripts to install CellProfiler on eddie3
Collect distributed cellprofiler results into sqlite databases.
Scripts used for the "Glycophorin C in carotid atherosclerotic plaque reflects intraplaque hemorrhage and pre-procedural neurological symptoms" paper.
OrganelleProfiler and OrganelleContentProfiler pipelines for CellProfiler as described in Laan et al (PLoS ONE, 2023)
Automated image analysis of colony formation assay experiments, using Cell profiler and R markdown
ImageJ macros for preparation of single images from stacks with maximum intensity projections and partially automated selection of slices in focus. CellProfiler Pipeline for yH2AX and 53BP1 counting and mitotic cells segmentation in asynchronous cultures. R scripts for processing of CellProfiler output.
“Novel Marker Distribution Pipeline for Analysis of Cell Based Quantitative Spatial Information”. The aim of this research was to analyse quantitative spatial information and provide intuitive visualisation relating to the identification of cells relative to tissue structure and pathology such as cancer tumour.
Website of Turku Bioimaging - Image Data Team (TBI-IDAT) containing our current and past projects as well as software and tools we use.
Analyzing Cell Painting results with Dask and Pandas - not yet ready for use by others
A plate image viewer for visually analysing Cell Painting results
A collection of python modules and command line tools for processing image-based RNAi screens.
R scripts to clean/analyse CellProfiler output, forked from
slideNormalize: normalisation of histological high-resolution images.
Automatic quantification nuclear staining with CellProfiler