Access a suite of analysis apps by clicking on the menu (or type command-K to open)

The first step in using the Query App to compute connections with your gene expression data is to assign a name to your query. Results will be stored in your Analysis History after your query is submitted.

Enter an up-regulated gene of interest, hit enter, and type in subsequent genes in the set you would like to query. You may also have down-regulated genes of interest. They can be entered in the box to the right.

Hit submit and the query algorithm will find connections between your genes of interest and perturbagens in CMap that have signatures most similar to your query. Data are generated in approximately 5 minutes and will be stored in your Analysis History.

The L1000 assay directly measures or infers the expression levels of 12,328 genes. By evaluating the current statistical model against a large compendium of RNA-Seq profiles from over 100 tissues from the GTEx consortium, we have identified a subset of 10,174 genes that are either measured or well inferred. This subset is known as the Best INferred Gene (BING) space. The Query App uses BING space to compute similarities between users' gene sets and the gene expression signatures in the CMap database. Each user entry is therefore mapped into one of the three following categories.
Invalid gene: Not a valid HUGO symbol or Entrez ID, and therefore not used in the query.
Valid gene: A valid HUGO symbol or Entrez ID that is also part of BING space, and therefore is used in the query.
Valid but not used in query: A valid HUGO symbol or Entrez ID that is not part of BING space, and therefore is not used in the query.

Click on a perturbagen in this table to see a CLUE Card that contains all of the information available for this perturbagen. You can also select any compound in the table to query connections with all other compounds in Touchstone. Click on Detailed List to view connections in a table, or click Heatmap to see connections in a matrix powered by the Morpheus App.

Filter the Touchstone data table by selecting perturbagen type or perturbational classes of interest.

Average transcriptional impact

Impact is assessed as a transcriptional activity score, which is calculated as a mean value of median replicate correlation and median signature strength of a perturbagen across multiple cell lines and doses. The score describes a perturbagen’s transcriptional activity, relative to all other perturbagens, as derived from its replicate reproducibility and magnitude of differential gene expression.

PCTCCi =  rank( median( CCi ) )N

PCTSSi =  rank( median( SSi ) )N



TASi is the transcriptional impact score for the i-th perturbagen

PCTCCi is the percentile, relative to all other perturbagens, of the i-th perturbagen’s median replicate correlation coefficient (CC) across all of its signatures

PCTSSi is the percentile, relative to all other perturbagens, of the i-th perturbagen’s signature strength (SS) across all of its signatures

N is the total number of perturbagens

Signature diversity

Thick black bars signify Transcriptional Activity Scores greater than or equal to 0.5; thinner black bars denote scores less than 0.5. Absence of a bar means no data available. Colored lines (chords) signify similar connectivity scores between cell lines; red for positive connectivity scores of 80-100 (pale to intense color according to the score); blue for negative connectivity. Chords are only shown when TAS scores are > 0.5; thus absence of a chord either means that the perturbagen TAS score is very low, or that no data is available. Chords for individual cell lines can be isolated from the rest of the figure by hovering over the cell line name.

Baseline expression of this gene in each cell line is represented as a z-score (top numbers). Scores were calculated using robust z-score formula:

z-scorei = ( xi - median( X ) )/( MAD( X ) * 1.4826 ),


xi is expression value of a given gene in i-th cell line

X = [ x1, x2 ... xn ] is a vector of expression values for a given gene across n cell lines

MAD( X ) is a median absolute deviation of X

1.4826 is a constant to rescale the score as if the standard deviation of X instead of MAD was used

Median and MAD expression values were calculated using RNA-Seq profiles from a total of 1022 cell lines, comprising data from the Cancer Cell Line Encyclopedia (CCLE; Barretina, et al.) and cell lines nominated by the CMap team. Plots show z-score values only for the core LINCS lines used by CMap in L1000 experiments. Light red or light blue regions indicate positive or negative outlier expression, respectively, of the gene relative to the other lines shown; z-score of a positive outlier in the corresponding cell line is in dark red and a negative outlier is in dark blue.

Summary class connectivity shows a boxplot that summarizes the connectivity of a class. Each data point, shown as a light gray dot, represents the median value of connectivity of one member to the other class members. (This corresponds to the median for each row, excluding the main diagonal, in the heatmap shown below.) The box is the distribution of those data points, where the box boundary represents the interquartile range, the vertical line within the box is the median, and the whiskers reflect the minimum and maximum values of the data (exclusive of extreme outliers, which may appear beyond the whiskers).

Connectivity between members of class is a standard heat map of the connectivity scores, summarized across cell lines, between members of the class, where dark red represents the highest positive scores and deep blue the highest negative scores. Individual scores are revealed to the left below the map by hovering over each cell of the map.

Class inter-cell line connectivity is a plot of the median (black line) and Q25-Q75 connectivity scores (blue area around black line) for each cell line as well as the summary scores across cell lines. In some cases perturbations have not been tested in every cell line; the absence of data is indicated by a “0” for that cell line. The example shown reveals that these estrogen agonists show the strongest connectivity to each other in MCF7, a human breast cancer cell line that expresses the estrogen receptor.

Profile status

Colored portion of top bar indicates the Broad assays in which this compound has been profiled.

L1000 cell/dose coverage

For compounds profiled by L1000, cell lines and dose range for which signatures are available are indicated by dark gray bars (lighter gray bar indicates no data is available for that cell line/dose combination). A bar displayed one row above the 10 uM row indicates that doses higher than 10uM were tested. The 6 rows correspond to 6 canonical doses: 20 nM, 100 nM, 500 nM, 1 uM, 2.5 uM, and 10 uM. (In some cases non-canonical doses were tested; these are rounded to the nearest canonical dose for the purpose of this display. For example, if the dose tested was 3.33uM, the 2.5uM bar is shown in dark gray here.)

About CMap

We are creating a genome-scale library of cellular signatures that catalogs transcriptional responses to chemical, genetic, and disease perturbation. To date, the library contains more than {1.3 Million} profiles resulting from perturbations of multiple cell types.

What is the Connectivity Map?

The Connectivity Map, or CMap, is a resource that uses transcriptional expression data to probe relationships between diseases, cell physiology, and therapeutics. The changes in gene expression, or “signatures,” that arise from a disease, genetic perturbation (knockdown or overexpression of a gene) or treatment with a small molecule are compared for similarity to all perturbational signatures in the database. Perturbations that elicit highly similar, or highly dissimilar, expression signatures are termed “connected”; their related transcriptional effects suggest they confer related physiological effects on the cell. Our goal is to use these connections to uncover novel treatments for a variety of diseases, including cancers, neurological diseases, and infectious diseases.

CMap is a dynamic database and we will release new versions as new data becomes available; version numbers are identified on the home page. We invite you to use CMap and our tools to analyze your gene expression profiles for connectivity to known perturbagens.

What are examples of Connectivity Map applications?

For the biologist: use CMap to reveal connections between steps of biological pathways.

For the chemist: use CMap to uncover structure-function relationships between novel and well-studied compounds

For the pharmacologist: use CMap as a first step in the drug discovery process

What is CLUE?

The dramatic increase in high-dimensional perturbational datasets available to the biomedical community has revealed the need for intuitive and performant user-interfaces to explore and query these data. We have developed a computational environment, called CLUE, to execute on state-of-the-art cloud-based systems. This environment makes data and tools available on the cloud, harmonizes datasets to facilitate interoperability between perturbational data types, and Implements web applications with user friendly graphical user interfaces that access underlying sophisticated algorithms.

Currently CLUE contains over 1.3 million gene expression profiles, related perturbational datasets, analytical tools, and web-based applications, all of which are freely available to academic users. For drug-discovery companies who want to leverage this work for their proprietary research programs while maintaining confidentiality, we offer CLUE as a subscription. See details at subscribe.

How are the gene expression signatures in CLUE generated?

Gene expression is determined using the L1000 assay, which measures the mRNA of ~1000 “landmark” genes from cells treated with chemical or genetic perturbations. (While earlier versions of CMap contained gene expression signatures obtained using Affymetrix microarray chips, the current CLUE data contains only signatures prepared from the L1000 assay.)

L1000 Assay

Cells are lysed in 384-well plates (where each well is an experiment) and the mRNA transcripts are captured on oligo-dT-coated plates. cDNAs are synthesized from the captured transcripts and subjected to ligation-mediated amplification (LMA) using locus-specific oligonucleotides harboring a unique 24-mer barcode sequence and a 5’biotin label. The biotinylated LMA products are detected by hybridization to polystyrene microspheres of distinct fluorescent color, each coupled to an oligonucleotide complementary to a barcode, and staining with streptavidin-phycoerythrin.

Tag Duo

Because only 500 bead colors are commercially available, we developed a strategy that allows two transcripts to be identified by a single bead color. Each bead is analyzed for its bead color (denoting the landmark gene identity) and the fluorescence intensity of the phycoerythrin signal (denoting the landmark transcript abundance). The final L1000 assay measures 978 landmark transcripts and 80 control transcripts chosen for their invariant expression, which serve as quality control indicators.

Data processing

Data is processed through a computational pipeline that generates a gene expression signature for each experiment. The process begins by a deconvolution step that determines the raw fluorescence value for each of the two genes assigned to each bead. These values are then normalized using a set of control genes. In addition to the directly measured 978 landmark genes, we use a regression model to impute the expression of an additional 9196 genes. We refer to this combination of landmark and inferred genes (10174 gene) as a gene expression profile. To generate signatures, expression of each gene in cells that have been treated with perturbagen is compared to expression of that gene in untreated cells, and differential expression is computed using a Z-scoring method. Scores are ranked, revealing the most up-regulated and down-regulated genes in response to a treatment; this gene set, or signature, can then be used to query CMap.

How do the CLUE tools facilitate using gene expression signatures for biological discovery?

CLUE offers a number of web-based apps, described below, for analysis of gene expression signatures. Our API provides metadata about compounds, genes, cell lines, and signatures. We have also developed command line interfaces with tools for computationalists and developers.

Analysis Tools

Touchstone App

“Touchstone” is our term for compound and genetic perturbagens (~5000) that are well-studied and generate robust gene expression signatures in cells. Thus the Touchstone data set serves as a benchmark for assessing connectivity among perturbagens. Use the Touchstone app to learn more about these perturbagens and explore their connectivities.

Query App

Use the query app to find positive and negative connections between your gene expression signature of interest and all the signatures in CMap.


The Integrated Connectivity Viewer presents connectivity data as a matrix-based interactive heatmap that provides a comprehensive view of connections and allows one to easily explore relationships within the data.

Morpheus App

Morpheus is an interactive version of the ICV that lets you manipulate and annotate an existing dataset or one of your choice.

Repurposing App

Explore our repurposing collection of ~5000 tool compounds and drugs for drug discovery opportunities.

Where can I get help?

See our Knowledge Base, email us at, or call in to Office Hours on Thursdays from 1-2PM EST.

How can I collaborate with CMap?

Email us at