Abstract
Systems-scale profiling approaches have become widely used in translational research settings. The resulting accumulation of large-scale datasets in public repositories represents a critical opportunity to promote insight and foster knowledge discovery. However, resources that can serve as an interface between biomedical researchers and such vast and heterogeneous dataset collections are needed in order to fulfill this potential. Recently, we have developed an interactive data browsing and visualization web application, the Gene Expression Browser (GXB). This tool can be used to overlay deep molecular phenotyping data with rich contextual information about analytes, samples and studies along with ancillary clinical or immunological profiling data. In this note, we describe a curated compendium of 93 public datasets generated in the context of human monocyte immunological studies, representing a total of 4,516 transcriptome profiles. Datasets were uploaded to an instance of GXB along with study description and sample annotations. Study samples were arranged in different groups. Ranked gene lists were generated based on relevant group comparisons. This resource is publicly available online at http://monocyte.gxbsidra.org/dm3/landing.gsp.
Highlights
Platforms such as microarrays and, more recently, generation sequencing have been leveraged to generate molecular profiles at the scale of entire systems
This in turn has provided opportunities for the discovery of biomarkers and for the development of novel therapeutic modalities3,11–13. More recently such systems-scale profiling of the blood transcriptome has been used to monitor response to vaccines or therapeutic drugs14–19. The democratization of these approaches has led to proliferation of data in public repositories: over 1.7 million individual transcriptome profiles from more than 65,000 studies have been deposited to date in the NCBI Gene Expression Omnibus (GEO), a public repository of transcriptome profiles
Furthering our understanding of the role of monocyte subsets in health and disease will require many more studies, we hope that the dataset compendium that we are making available to the research community via this publication can help support these endeavors
Summary
As the Gene Expression Browser has been described in detail previously, the emphasis of this data note should be on the curated data. It would be helpful to discuss the motivation for creating this particular compendium of monocyte transcriptome datasets as well as the intended use of the curated data given the breadth and heterogeneity of diseases, cell types, and experiments that it includes
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