A dimensionality reduction technique particularly well suited for visualizing data. (For references, see https://lvdmaaten.github.io/tsne)

The parameters that were used for running t-SNE here are: 50 initial dimensions, perplexity of 30, and theta of 0.5. For datasets with <= 5000 samples, the standard t-SNE algorithm is used. For larger datasets, the Barnes-Hut algorithm is employed.

A dimensionality reduction technique in which the two principal components are chosen to have the largest possible variance.

To analyze relationships between perturbations, we utilize the framework of connectivity. A connectivity score between two perturbations quantifies the similarity of the cellular responses evoked by these perturbations. A score of 1 means that these two perturbations are more similar to each other than 100% of other perturbation pairs. A score of -1 means that these two perturbations are more dissimilar to each other than 100% of other perturbation pairs.

See a heatmap of connections between individual perturbagens in cell lines and all other perturbagens used for the P100 assay or the GCP assay. The tutorial describes the features of the heatmap.

Bring data, in GCT format, from your own P100 or GCP studies to query against our datasets.

Introspect means querying your dataset against itself. Make sure to "Include Introspect" if you would like to see connections within your dataset (in addition to connections between your dataset and Touchstone-P).

In computing connectivity, biological or technical replicates can be aggregated together. Please select which metadata fields should be used to recognize replicates. For example, if you wish to distinguish between different doses of the same compound, make sure to select "pert_dose" (or something similar) as one of the metadata fields by which to group replicates. The possible metadata fields by which to group replicates only appear after you have upload your GCT and selected "Yes" for "Are there replicates in your data?".

Matched mode: When running GUTC, incorporates cell-line information to match query data against matching cell types in Touchstone. Currently this includes the following 9 cell types : [A375, A549, HEPG2, HCC515, HA1E, HT29, MCF7, PC3, VCAP].
Unmatched mode (recommended): When running GUTC, does not incorporate cell-line information when querying the data against Touchstone signatures.

L-Build ("Light" Build):  All levels of L1000 data up to aggregated signatures.
Full Build:  All levels of L1000 data up to aggregated signatures, as well as all relevant additional analyses of the data (Introspect, t-SNE, PCA, etc.).

When querying Touchstone, Feature Space determines what set of genes to query against. When perturbagens are profiled on the L1000 platform, Landmark is recommended. When the queries you wish to use are not landmarks, use BING instead.

Root location within a brew folder that contains the instance matrices and the brew_group folder. Default is brew/pc

List of expected treatment doses in micromolar as a listmaker list. If provided, dose discretization is applied to the pert_dose metadata field to generate a canonicalized pert_idose field. Note this assumes that the pert_dose annotations are in micromolar.

Generates TAS plots and connectivity heatmap of preliminary callibration plates to identify the most suitable experimental conditions of specified parameters. Tool should be run on small pilot experiments, with a variety of experimental parameters such as seeding density and time point. Plots can also be decoupled by parameters such as cell id.

Column filter to sig_build_tool as a listmaker collection

The name of the build used when generating all associated files and folders (e.g. <BUILD_CODE>_metadata). For this reason, the code must be filename compatible.

When merging replicates for L1000, several versions of the merged data are made. This parameter determines which version to use when creating your build. by_rna_well is the default. by_rna_well is recommended.

All data is from the Cancer Cell Line Encyclopedia resource. Expression data was released 15-Aug-2017, copy number data is dated 27-May-2014, and mutational data is dated 15-Aug-2017.

Feature Mapping: Ensembl Ids from the source data were mapped to Entrez Gene Ids using gene annotations from NCBI (downloaded on 02-Mar-2016).
Normalization:  RNAseq RPKM values were log2 transformed using log2(max(RPKM, eps)). The data were then normalized such that the expression values were comparable across cell lines, by minimizing technical variation and equalizing their distributions (for details of the normalization, see LISS and QNORM entries in the Connectopedia glossary). Post-normalization, the expression values range between 4 and 15 log2 units, with 4 indicating that a gene is minimally or not expressed and 15 indicating the maximum readout.
Z-scores: The number of standard deviations that a gene is above or below the population mean is called its z-score. The "robust" z-score is resistant to outliers by using median instead of mean and median absolute deviation (MAD) instead of standard deviation. The reference population used to compute the median and MAD for a particular gene is all CCLE lines with data for that gene.
Z-scores Within Primary Site: Similar to z-scores, but the reference population used to compute the median and MAD is all CCLE lines from the same lineage with data for that gene.

All scores indicated are in log 2 ratios to reference, binned using the heuristics described in CNVkit.

Deletion:  score < -1.1
Loss:  -1.1 ≤ score ≤ -0.25
No change:  -0.25 < score < +0.2
Gain: +0.2 ≤ score < +0.7
Amplification: +0.7 ≤ score

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

Switch between running a single query and running a batch query.

Give each query a descriptive name that will help you identify your results.

Tip: Each list can have a different number of genes; in fact, you can run a query with only one list (up OR down).

Your query will take about 5 minutes to process; check the History section in the Menu for your results!

Valid genes used in the query have HUGO symbols or Entrez IDs and are well-inferred or directly measured by L1000 (member of the BING gene set). Valid genes not used in a query are those that have a valid HUGO or Entrez identifier but are not part of the BING set. Invalid genes do not have HUGO or Entrez IDs.

Give each query a descriptive name that will help you identify your results.

Your query will take about 5 minutes to process; check the History section in the Menu for your results!

The sig_fastgutc_tool is a reimplementation of our query algorithm that enables faster query results, especially at larger batch sizes. It is the result of crowd-sourced contest. It is currently in beta mode.

Filter datasets by category to see only those of interest.

Data Icons identify published and proprietary datasets.

Click on a row to see a summary of that dataset, including cell lines and treatment conditions, assay type, and dates.

Arrange the table to display the information most important for your work, and add key datasets to favorites.

View details about the collection as a whole and about individual compounds.

View subsets of compounds based on mechanism, drug target, or known disease application.

Purity is assessed by ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) of compounds after receipt from the vendor.

Status as of publication of this resource (March 2017). We will be updating this but let us know if you notice a discrepancy.

Click on a compound to see details about its structure, mechanism, targets, approval status, and vendor.

Mouse over this graphic to see the classes of proteins targeted by drugs in the hub.

This is the current count of perturbagens in the reference (touchstone) dataset.

Select data from perturbagens grouped by their MoA or role in the cell.

Choose a perturbagen type, or view them all.

Touchstone is our reference dataset, made from well-annotated perturbagens profiled in a core set of 9 cell lines.

Detailed List is unavailable for Touchstone v1.1.1.1. A new data visualization approach is in development, but to get results in a table format (similar to Detailed View), please click on Heat Map and download the dataset as a GCT file that can be viewed in Excel or similar apps. Please see here for a detailed explanation.

Articles are tagged with topics. Click on a topic tag to see all related articles.

Look it up! A quick reference guide of CMap terms and their meanings.

Email us with your questions.

Click on the heading to read all the articles in this section on a single page, or open each article separately.

Click on a heading to open a menu of articles.

Each article is tagged with key words that describe its content.

Underlined words link to their definition in the CMap glossary.

Your feedback helps us make Connectopedia more useful.

Average transcriptional impact

TAS is a metric that incorporates the signature strength (the number of significantly differentially expressed transcripts) and signature concordance (the reproducibility of those changes across biological replicates) to capture activity of a compound. The score is computed as the geometric mean of the signature strength and the 75th quantile of pairwise replicate correlations for a given signature. Prior to computing the geometric mean, the signature strength is multiplied by the square root of the number of replicates. This serves to mitigate score shrinkage with increasing replicate number and allows TAS values derived from signatures of different numbers of replicates to be compared with each other.

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.)

L1000 Query Tutorial

L1000 Query Tutorial

This is a tutorial on how to perform L1000 queries from the CMap Query app using some example files (downloadable from the "Example gene lists" section below).


CLUE allows for users to query CMap in two ways: through individual queries, or in batches of up to 25 queries run at once. For an individual query, the input is a list of upregulated genes, and an optional list of downregulated genes. These can be entered as plain text or imported from a file, such as a txt or gcp file. For a batch query, the input is a collection of gene lists, each separate list representing a query (i.e. upregulated genes), and an optional collection of "down" lists. CMap usually stores these collections of gene lists as GMT files, where each row in the file represents a different list of upregulated/downregulated genes.

Example gene lists

For this tutorial, we will be using various example sets. Individual query has these examples loaded into the app itself, but batch query requires a file download.

For batch query, we will be using a set of four signatures. The up and downregulated gene lists are stored in these two GMT files: example_uptag_CRCGN009.gmt (representing upregulated genes) and example_dntag_CRCGN009.gmt (for downregulated genes). If you're curious, these signatures are from the CRCGN dataset.

Download both of these files by clicking on their links. Each row in the file is a distinct query, where the first column is the name for that query, the second is the description, and the remaining columns are Entrez gene IDs. For more information on the necessary format for these lists, see the "Other Tips" section in the bottom of this tutorial.

In the following steps, we will use these two files together to submit a batch of 4 queries with an "up" (uptag) and "down" (dntag) collection.

Optional: Creating gene set collections using Listmaker

The Query app uses files or the Listmaker app to load gene lists for queries. If you'd prefer to upload a file directly to Query without saving your gene lists in Listmaker, you can skip ahead to the next section.

In order to create a collection, go to Listmaker. From this page, you can upload a GMT file to create your collection of "up" gene lists and collection of "down" gene lists. For this tutorial, we will upload multiple lists at time using one GMT file, but you can also add lists individually. We will upload the GMT file with our up lists (uptag) and create a new collection. To do so, click the "+ Add" button, and drag and drop the GMT file in the box. Specify the type of your collection as "Gene" (this is so we can use it in the Query app), and create a new collection by typing in "CRCGN up" into the Collection Name field and pressing Enter.** **You may leave the tags field empty. Click "Create Lists", and click "Finish" to refresh the page and view your new collection.

Screenshots showing the steps from the section above:

We then repeat this process and create a separate collection "CRCGN_dn" with our down sets (dntag). At the end of the process, we should have two new collections with four lists each.

We can also use Listmaker to upload a list for our individual query example using the same process. However, this will upload a single list, not a collection. You must specify the collection you would like the list to belong to.


Submitting an individual query

You can launch individual L1000 queries from the Query app by selecting the dropdown on the top of the page and switching to "Individual query." Make sure that the dropdowns for Gene expression (L1000) and your chosen dataset are already selected in order to proceed to this option.

You must also name the query in order to submit. Once this is done, you may start to load in your genes to the Up and Down boxes below the dropdowns. In individual query, you may load genes via drag and drop of a plain text file, loading a list from Listmaker, or typing in genes one by one in the boxes. You may also choose to load one of the examples mentioned in the instruction paragraph on the page, which will auto-populate the name and gene boxes.

Only genes marked with the valid symbol will be used in the query, and you may choose to remove genes with the invalid or unused symbol. Once your gene lists are ready in the boxes, you may click the Submit button to submit your query.

Submitting a batch query

Submitting a batch query is very similar to submitting an individual query, only you will be submitting a collection of lists of genes instead of a single gene list. In the dropdown, select "Batch query" instead of "Individual query" to get started. In this mode, there is no option to type in genes, you must either upload files or select a Listmaker collection. If you are using file upload, you can drag and drop the files into the box. If using Listmaker, you can do so by clicking the Load Collection button under "UP-regulated genes" and "DOWN-regulated genes." If you don't see your collection as available, it is possible you didn't set the type of the lists as "Genes" when creating the collection. You can also check the "Compute with sig_fastgutc_tool" option, which will reduce the runtime of your query substantially. After you have loaded both UP and DOWN, click the submit button to submit your queries.

Your results in History

After you've submitted your query, you can check its progress from the History page. Once the status is marked as "complete", you can check the box next to it and click the "Heat Map" button to view its connectivity results. If your query's status is "error" and you are using your own gene sets, check out the next section for debugging tips.

If you see the download icon next to your query, you may click the icon to download your results. The downloaded files comprise outputs of the CMap query tool at various levels of granularity. They include the query inputs, raw connectivity scores, normalized scores and the background adjusted tau values (For computational details see: Subramanian, A. et al. A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles. Cell 171, 1437–1452.e17 (2017)).

The following provides a brief description of the files provided:

uptag.gmt - Up component of the query (user input)

dntag.gmt - Down component of the query (user input)

cs_.gct - Text matrix of the weighted connectivity scores with row and column annotations. Scores match matrices/query/cs_n.gct

gutc_config.yaml - configuration file

/matrices/query/ Connectivity scores** **

up.gmt - Up component of the query (used by the query algorithm, uptag.gmt filtered to the requested feature space e.g. bing)

dn.gmt - Down component of the query (used by the query algorithm)

cs_n*.gctx - Combined weighted enrichment score (two-tailed weighted enrichment statistic of the query genesets applied to each signature in the database). Range [-1, +1]

cs_up_n*.gctx - Enrichment scores for the UP component of the query

cs_dn_n*.gctx - Enrichment scores for the Down component of the query

leadf_up_n*.gctx - Fraction of the query set in the leading edge of the enrichment (Up)

leadf_dn_n*.gctx - Fraction of the query set in the leading edge of the enrichment (Down)

/matrices/gutc/ Normalized and Summarized scores

cs_sig.gctx - Connectivity scores in cs_n*.gct filtered to Touchstone signatures

Normalized Connectivity scores

ns_sig.gctx - Signature level scores

ns_pert_cell.gctx - Per-perturbagen scores for each cell line

ns_pcl_cell.gctx - Per-PCL (Perturbation class) scores for each cell line

ns_pert_summary.gctx - Per-perturbagen scores summarized across cell lines via the Summly algorithm

ns_pcl_summary.gctx - Per-PCL scores summarized across cell lines

Background adjusted tau scores ranging [-100 to +100]. Correspond to the heatmap displayed in the connectivity viewer on clue.io

ps_pert_cell.gctx - Tau values for ns_pert_cell

ps_pcl_cell.gctx - Tau values for ns_pcl_cell

ps_pert_summary.gctx - Tau values for ns_pert_summary

ps_pcl_summary.gctx - Tau values for ns_pcl_summary

query_info.txt - Query metadata

Regarding extracting cell-line specific connectivity results:

I would recommend looking at the Tau scores which are available in the ps_pert_cell and ps_pcl_cell matrices and match the results displayed in clue.

The cs_n*.gct file in the top level has the signature-level connectivity scores matrix has annotation including the cell line for each signature

Other Tips

GMT format: For more information on the GMT format, please see the section on the GMT file format on the GSEA wiki.

Entrez Gene IDs: L1000 queries use Entrez Gene IDs as input. Before submitting a batch query, all genes must be converted to Entrez Gene IDs. For individual genes, we recommend using the NCBI gene database and looking at the "Gene ID" field. In order to convert many genes, there are several different online tools and packages including DAVID, MyGene.info, or the clue.io API gene service that you may find useful.

BING genes: L1000 queries are run against BING space signatures (i.e. including roughly 10,000 genes), so when you submit your batch query, only the BING space genes are used. Genes that are not in BING space will not affect the result. In order to determine which genes are in BING space and are used in the query, you may use the /gene-space command in Command or the clue.io API gene service and look for genes marked "landmark" or "best inferred".

Gene list names in collections: In order to match up and down collection contents, the names of your up and down gene lists must match. You can either match with identical names (e.g. "IMATINIB_LOW_DOSE" in the up collection matches to "IMATINIB_LOW_DOSE" in the down collection) or by including _UP and _DN suffixes (e.g. "IMATINIB_LOW_DOSE_UP" in the up collection matches to "IMATINIB_LOW_DOSE_DN" in the down collection).

Last modified: Thu Sep 05 2019 16:32:14 GMT-0400 (EDT)

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