Murphy Lab

 Cytometry Development Workshop

 Flow Cytometry



 Carnegie Mellon University
 Computational Biology Department
 Center for Bioimage Informatics
 Biological Sciences Department
 Biomedical Engineering Department
 Machine Learning Department

Murphy Lab

Murphy Lab


New! v2.0!

Welcome to the Murphy lab at Carnegie Mellon University. The lab is a multidisciplinary environment with people working on projects in computational cell biology.


February 6, 2015Today was a busy day. Congratulations to Drs. Devin Sullivan and Aparna Kumar for successfully defending their Ph.D. theses. Devin is on his way to Stockholm, Sweden for a postdoctoral fellowship with Dr. Emma Lundberg at the Science for Life Labs, KTH Royal Institute of Technology, where he will be working on the Subcell Atlas of the Human Protein Project. Aparna has accepted a position as a Data Scientist at Dow Jones in New York City. Devin and Aparna are the 25th and 26th Ph.D. to graduate from Murphylab.
December 8, 2014Our group published a paper with implications for cancer research today in the U.S. Proceedings of the National Academy of Sciences. It describes a new method for identifying proteins that differ significantly in subcellular location between normal and cancerous tissue and applies it to images of four human tissues from the Human Protein Atlas. The proteins identified may help improve cancer detection and diagnosis, and may increase our understanding of the oncogenic process.
October 5, 2014The image analysis and modeling team at the NIH-supported National Center for Multiscale Modeling of Biological Systems is seeking new partners for collaborative or service projects with researchers at the Center. We are seeking investigators who wish to use our CellOrganizer system) for learning and using generative models of cell size, shape and subcellular organization (or to help with further development). We can provide extensive training to external personnel, consultation on appropriate methods and design of studies, help with local installation of any desired software, and access to computational resources at the Center for image analysis, modeling and simulation. CellOrganizer learns modular models of things such as cell shape, nuclear shape, vesicular organelle distribution and microtubule distribution directly from 2D or 3D images and can produce specific instances of cell geometries without the need to create them by hand or to segment microscope images (see Buck et al, 2012 for an overview). Through Center funding, pipelines have been created whereby these geometries can be combined with biochemical models to perform spatially realistic cell simulations with a minimum of effort (Center resources can be provided to run these using the cell simulation engine MCell. The biochemical models can be encoded in SBML (i.e., investigator created or downloaded from models databases) or can be generated by BioNetGen (a powerful rule-based modeling package). This combination of CellOrganizer and MCell allows investigators to explore the effect of different cell geometries on their models (e.g., to independently explore different modes of variation in the generative models, such as variation in organelle number vs. shape). Existing generative models of 3T3 cells, HeLa cells, and C2C12 cells can be used so that making extensive image collections can be avoided.

If interested, please contact or fill out the form at the MMBioS web site. We would be happy to further explain the capabilities of the current system and discuss development of new capabilities.

May 22, 2014Our paper on using active learning to identify drug-target interactions using PubChem data has been published in BMC Bioinformatics.
April 29, 2014CellOrganizer 2.1 released.
April 17, 2014A new service for content-based image retrieval, CellSearcher released. It allows users to upload cell images and find images in other databases that are similar in subcellular pattern (using the OMERO.searcher system).
February 19, 2014The MMBioS center, a collaboration between the University of Pittsburgh, Pittsburgh Supercomputing Center, Salk Institute and Carnegie Mellon is featured in a video created by the Biophysical Society for the 'Biophysical Society TV' shown at their annual meeting. The video is also available at YouTube. The Technology Research and Development project (TR&D3) that we lead is described starting at 4:18. The open source CellOrganizer system plays a central role in this project.
December 17, 2013Our paper characterizing new algorithms for active learning for drug discovery in the absence of compound or target features has been published in PLoS ONE. The algorithms seek to learn the effects of many compounds on many targets, and address the case in which the effect of a given compound on a given target is represented as one of a number of different categorical phenotypes (rather than just as a score measuring extent of an expected effect). We introduces measures of uniqueness and responsiveness to characterize the nature of a given experimental space, and show in simulated experiments that our active learner shows significant improvement over using random choice and does so for essentially all values of the uniqueness and responsiveness. We also introduce a stopping rule approach for estimating the lower limit of the true accuracy of an actively learned model, permitting decisions to be made about when to stop a campaign of active learning-driven experimentation. Lastly, we show using Connectivity Map data that accurate models of the effects of drugs on gene expression in various cell lines can be constructed without the need to perform experiments for all possible combinations of drugs and cell lines.
September 30, 2013CellOrganizer 2.0 released. New shape space modeling capabilities, SBML-spatial outputs, and reporter tools.
July 10, 2013 OMERO.searcher Local Client v1.3 released, along with contentDBs for three new databases (The Human Protein Atlas, The Cell Libary, and PSLID RandTag2).
July 8, 2013A new article in Bioinformatics describes a more demanding paradigm for subcellular location classification than has previously been used, which uses different sets of proteins for training and testing. New publicly available datasets were created to test this paradigm. Previously described classification methods did not perform well under this paradigm, but a combination of local and global features was shown to yield very good accuracies on a number of datasets.
May 17, 2013 CellOrganizer v1.9.0 released. Major addition is use of Bio-Formats to read input files.
April 2, 2013 CellOrganizer v1.8.1 has been released. The primary goal of this release was to add the resolution of the dataset to the model trainer graphical user interface.
March 11, 2013 CellOrganizer v1.8.0 has been released. The primary new feature is the ability to generate cell and nuclear shapes from diffeomorphic models.
January 24, 2013 Congratulations to Dr. Joshua Kangas for successfully defending his thesis entitled, "Active Learning for Drug Discovery." Dr. Kangas will be joining a new startup, Quantitative Medicine, LLC, as cofounder and Chief Science Officer.
January 15, 2013 A review article from our group on automated image analysis methods for high-content screening and analysis was awarded the 2013 JBC Authors' Choice Award at the annual meeting of the Society for Laboratory Automation and Screening.
January 9, 2013 A new version of OMERO.searcher Local Client has been released, along with a content database for the PSLID RandTag2 database also released today. This version permits searching of both OMERO and non-OMERO databases and supports user-defined feature sets.
January 9, 2013 A significantly expanded collection of images and sequences from the RandTag project has been released. Automated analysis of the images of CD-tagged NIH 3T3 clones in which the tagged gene has been identified permitted the assignment of subcellular location for a number of previously unannotated or minimally-annotated proteins.
November 30, 2012 Two articles in PLoS ONE describe results from our collaboration with the Human Protein Atlas. In the first, analysis of images of eleven cultured cell lines reveals that accounting for differences in cell shape and size reduces apparent variation in microtubule distribution. Accounting for this, three groups of cell lines remain distinguishable. In the second, computational analysis identified proteins whose annotations from visual analysis were incorrect.
November 28, 2012 Congratulations to Dr. Jieyue Li for successfully defending his thesis entitled, "Automated Learning of Subcellular Location Patterns in Confocal Fluorescence Images from Human Protein Atlas." Dr. Li has accepted a position as Machine Learning Expert at ZestFinance in Los Angeles, California.
September 4, 2012 CellOrganizer v1.7.1 released. Support added for exporting object files from TIF files of synthesized images.
August 8,
CellOrganizer v1.7 released. Support added for output as indexed images, blender object files, and SBML Spatial extension.
May 16,
OMERO.searcher v.1.1.2 released! Provides content-based searching of OMERO databases with local or remote images.
April 13,
CellOrganizer v1.6 released! Supports 2D/3D images and vesicle and microtubule pattern models.
December 19, 2011Dr. Murphy named to the NIH Council of Councils.
September 7, 2011Murphy Lab member Luis Pedro Coelho named to the 2012 class of Siebel Scholars. The Siebel Scholars program recognizes the most talented students at the world's leading graduate schools of business, bioengineering, and computer science.
September 5, 2011Video of Dr. Murphy's talk at the COMBINE 2011 meeting is available online.
January 10, 2011Work from Murphy group featured in Nature Biotechnology article on Computational Biology breakthroughs in 2010.
September 18, 2010Murphy Lab member Tao Peng wins the 2009 Research Award from Carnegie Mellon's Biomedical Engineering Department. One award is given each year to the BME graduate student judged to have the most outstanding research achievement.
July 11,
New release 2.0 of PatternUnmixer (formerly called PUnmix). The new version supports reading images from OMERO servers, displaying object distributions, checking for the presence of unknown patterns, and exporting unmixing fractions. See the Software link.
August 22, 2009Murphy Lab member Luis Pedro Coelho wins the CPCB Outstanding Research Achievement Award.
July 8,
Collection of hand-segmented nuclear images and python code for comparing segmentation methods released. See the Software link.
July 1,
New PSLID release containing images from over 2,500 clones generated by the RandTag project.
April 6,
Releases of SLML Tools and PUnmix are available under the Software link. These packages implement learned, generative models of subcellular patterns and estimation of pattern unmixing fractions, respectively. Matlab source code, as well as compiled versions for Linux, Mac OS, and Windows, are available.


The primary focus of current work in the lab is on automated interpretation of fluorescence microscope images.

If you are interested in reading more about our work, a list of publications is available.

Slides from Dr. Murphy's tutorials at meetings like the ISAC Congress and the SBS Conference are available under the presentations link.

Data Available for Download

Select data generated from Murphy Lab projects is available for download.

Last Updated: 12 Feb 2015

Copyright © 1996-2013 by the Murphy Lab, Carnegie Mellon University