Update September 12, 2017: A new version of the Connectivity Map using the L1000 assay has been accepted for publication, and is currently available as a pre-print (see Subramanian et. al). Tools for
analysis of these data are at clue.io.
The earlier Affymetrix-microarray based Connectivity Map data (build02) is available in archive mode on this page (see below).
The Connectivity Map (also known as cmap) is a collection of genome-wide transcriptional expression data from cultured human cells treated with bioactive small molecules and simple pattern-matching algorithms that together enable the discovery of functional connections between drugs, genes and diseases through the transitory feature of common gene-expression changes. You can learn more about cmap from our papers in Science and Nature Reviews Cancer.
This web interface provides access to the current version (build 02) of Connectivity Map which contains more than 7,000 expression profiles representing 1,309 compounds. It is designed to allow biologists, pharmacologists, chemists and clinical scientists to use cmap without the need for any specialist ability in the analysis of gene-expression data.
A brief tutorial can be found by clicking 'getting started' under the 'help' tab after log in. Detailed help and a definition of cmap terms can be found by clicking 'topics', also under the 'help' tab. For everything else, please contact us.
The Connectivity Map is based at The Broad Institute of MIT and Harvard in Cambridge, Massachusetts. The cmap team is Justin Lamb, Xiaodong Lu, Dave Peck, Matt Wrobel, Aravind Subramanian, Irene Blat, Josh Modell, Jim Lerner, Elizabeth Liu and Emily Crawford. Jean-Philippe Brunet, Ken Ross, Michael Reich, Paul Clemons, Kathy Seiler, Steve Haggarty, Bang Wong, Maria Nemchuk, Ru Wei, Steve Carr, Christopher Johnson, Stephen Johnson, the MSigDB curation team, and the Genetic Analysis Platform contribute invaluable expertise and assistance. Todd Golub and Eric Lander provide institutional leadership for the project.