Welcome to CellProfiler’s documentation!¶
Most laboratories studying biological processes and human disease use light/fluorescence microscopes to image cells and other biological samples. There is strong and growing demand for software to analyze these images, as automated microscopes collect images faster than can be examined by eye and the information sought from images is increasingly quantitative and complex.
CellProfiler is a versatile, open-source software tool for quantifying data from biological images, particularly in high-throughput experiments. CellProfiler is designed for modular, flexible, high-throughput analysis of images, measuring size, shape, intensity, and texture of every cell (or other object) in every image. Using the point-and-click graphical user interface (GUI), users construct an image analysis “pipeline”, a sequential series of modules that each perform an image processing function such as illumination correction, object identification (segmentation), and object measurement. Users mix and match modules and adjust their settings to measure the phenotype of interest. While originally designed for high-throughput images, it is equally appropriate for low-throughput assays as well (i.e., assays of < 100 images).
CellProfiler can extract valuable biological information from images quickly while increasing the objectivity and statistical power of assays. It helps researchers approach a variety of biological questions quantitatively, including standard assays (e.g., cell count, size, per-cell protein levels) as well as complex morphological assays (e.g., cell/organelle shape or subcellular patterns of DNA or protein staining).
The wide variety of measurements produced by CellProfiler serves as useful “raw material” for machine learning algorithms. CellProfiler’s companion software, CellProfiler Analyst, has an interactive machine learning tool called Classifier which can learn to recognize a phenotype of interest based on your guidance. Once you complete the training phase, CellProfiler Analyst will score every object in your images based on CellProfiler’s measurements. CellProfiler Analyst also contains tools for the interactive visualization of the data produced by CellProfiler.
In summary, CellProfiler contains:
Advanced algorithms for image analysis that are able to accurately identify crowded cells and non-mammalian cell types.
A modular, flexible design allowing analysis of new assays and phenotypes.
Open-source code so the underlying methodology is known and can be modified or improved by others.
A user-friendly interface.
The ability to use high-throughput computing (clusters, cloud).
A design that eliminates the tedium of the many steps typically involved in image analysis, many of which are not easily transferable from one project to another (for example, image formatting, combining several image analysis steps, or repeating the analysis with slightly different parameters).
For a full list of references, visit our citation page.
McQuin C, Goodman A, Chernyshev V, Kamentsky L, Cimini BA, Karhohs KW, Doan M, Ding L, Rafelski SM, Thirstrup D, Wiegraebe W. (2018) “CellProfiler 3.0: Next-generation image processing for biology.” PLoS biology 16(7), e2005970 (link)
Carpenter AE, Jones TR, Lamprecht MR, Clarke C, Kang IH, Friman O, Guertin DA, Chang JH, Lindquist RA, Moffat J, Golland P, Sabatini DM (2006) “CellProfiler: image analysis software for identifying and quantifying cell phenotypes” Genome Biology 7:R100 (link)
Kamentsky L, Jones TR, Fraser A, Bray MA, Logan D, Madden K, Ljosa V, Rueden C, Harris GB, Eliceiri K, Carpenter AE (2011) “Improved structure, function, and compatibility for CellProfiler: modular high-throughput image analysis software” Bioinformatics 27(8):1179-1180 (link)
Lamprecht MR, Sabatini DM, Carpenter AE (2007) “CellProfiler: free, versatile software for automated biological image analysis” Biotechniques 42(1):71-75. (link)
Jones TR, Carpenter AE, Lamprecht MR, Moffat J, Silver S, Grenier J, Root D, Golland P, Sabatini DM (2009) “Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning” PNAS 106(6):1826-1831 (link)
Jones TR, Kang IH, Wheeler DB, Lindquist RA, Papallo A, Sabatini DM, Golland P, Carpenter AE (2008) “CellProfiler Analyst: data exploration and analysis software for complex image-based screens” BMC Bioinformatics 9(1):482 (link)
Tips and instructions for using CellProfiler’s interactive module output displays.
Creating a Project¶
Learn how to configure a project and load images.
Learn how to create, run, and debug CellProfiler pipelines.
Using Your Output¶
Learn how to interact with and export data generated by CellProfiler.
Learn about the other features of CellProfiler.
- File Processing
- Image Processing
- Object Processing
- Worm Toolbox
These features are considered deprecated and will be removed in a future version of CellProfiler.