Cytometry Development Workshop
Carnegie Mellon University
Computational Biology Department
Center for Bioimage Informatics
Biological Sciences Department
Biomedical Engineering Department
Machine Learning Department
Welcome to the Murphy lab at Carnegie Mellon University. The lab
is a multidisciplinary environment with people working on
projects in computational cell biology.
|December 8, 2014||Our 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
|October 5, 2014||The 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 firstname.lastname@example.org
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, 2014||Our paper on using active learning to identify drug-target interactions using PubChem data has been published in BMC Bioinformatics.
|April 29, 2014||CellOrganizer 2.1 released.
|April 17, 2014||A 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, 2014||The 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, 2013||Our 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, 2013||CellOrganizer 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, 2013||A 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
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
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
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
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
|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.
|CellOrganizer v1.7 released. Support added for output as indexed images, blender object files, and SBML Spatial extension.
|OMERO.searcher v.1.1.2 released! Provides content-based searching of OMERO databases with local or remote images.
|CellOrganizer v1.6 released! Supports 2D/3D images and vesicle and microtubule pattern models.
|December 19, 2011||Dr. Murphy named to the NIH Council of Councils.
|September 7, 2011||Murphy 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, 2011||Video of Dr. Murphy's talk at the COMBINE 2011 meeting is available online.
|January 10, 2011||Work from Murphy group featured in Nature Biotechnology article on Computational Biology breakthroughs in 2010.
|September 18, 2010||Murphy 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.
|New release 2.0 of PatternUnmixer
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, 2009||Murphy Lab member Luis Pedro Coelho wins the CPCB Outstanding Research Achievement Award.
|Collection of hand-segmented nuclear images
and python code for comparing segmentation methods released. See the Software link.
|New PSLID release containing images from
over 2,500 clones generated by the RandTag project.
|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
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.