Abstract
BackgroundThe aim of connectivity mapping is to match drugs using drug-treatment gene expression profiles from multiple cell lines. This can be viewed as an information retrieval task, with the goal of finding the most relevant profiles for a given query drug. We infer the relevance for retrieval by data-driven probabilistic modeling of the drug responses, resulting in probabilistic connectivity mapping, and further consider the available cell lines as different data sources. We use a special type of probabilistic model to separate what is shared and specific between the sources, in contrast to earlier connectivity mapping methods that have intentionally aggregated all available data, neglecting information about the differences between the cell lines.ResultsWe show that the probabilistic multi-source connectivity mapping method is superior to alternatives in finding functionally and chemically similar drugs from the Connectivity Map data set. We also demonstrate that an extension of the method is capable of retrieving combinations of drugs that match different relevant parts of the query drug response profile.ConclusionsThe probabilistic modeling-based connectivity mapping method provides a promising alternative to earlier methods. Principled integration of data from different cell lines helps to identify relevant responses for specific drug repositioning applications.
Highlights
The aim of connectivity mapping is to match drugs using drug-treatment gene expression profiles from multiple cell lines
We used two earlier connectivity mapping methods: rank-based average enrichment-score distance (AESD, [2]) and correlation (COR) on the differential expression data averaged over the cell lines
Our first contribution was to define the relevance for the information retrieval task based on a probabilistic model that captures the relevant gene expression effects for the query drug in the form of probabilistic latent factors inferred from data
Summary
The aim of connectivity mapping is to match drugs using drug-treatment gene expression profiles from multiple cell lines. This can be viewed as an information retrieval task, with the goal of finding the most relevant profiles for a given query drug. CMap’s successor, the Library of Integrated Network-based Cellular Signatures (LINCS, http:// www.lincsproject.org/), will offer data for thousands of Connectivity mapping can be seen as an information retrieval problem, where the task is to find the most relevant gene expression profile for a given query drug profile. Current connectivity mapping methods define relevance based on similarity in the sets of top up- and down-regulated genes between the two measurement profiles [1] or the consensus profiles constructed by combining all measurement samples for a given drug [2]. One could use the Pearson correlation to compute the similarity, but it is more sensitive to platform differences [1]
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