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A Range Finding Protocol to Support Design for Transcriptomics Experimentation: Examples of In-Vitro and In-Vivo Murine UV Exposure

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Figshare2016-01-18 更新2026-04-29 收录
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https://figshare.com/articles/dataset/A_Range_Finding_Protocol_to_Support_Design_for_Transcriptomics_Experimentation_Examples_of_In_Vitro_and_In_Vivo_Murine_UV_Exposure/1025016
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In transcriptomics research, design for experimentation by carefully considering biological, technological, practical and statistical aspects is very important, because the experimental design space is essentially limitless. Usually, the ranges of variable biological parameters of the design space are based on common practices and in turn on phenotypic endpoints. However, specific sub-cellular processes might only be partially reflected by phenotypic endpoints or outside the associated parameter range. Here, we provide a generic protocol for range finding in design for transcriptomics experimentation based on small-scale gene-expression experiments to help in the search for the right location in the design space by analyzing the activity of already known genes of relevant molecular mechanisms. Two examples illustrate the applicability: in-vitro UV-C exposure of mouse embryonic fibroblasts and in-vivo UV-B exposure of mouse skin. Our pragmatic approach is based on: framing a specific biological question and associated gene-set, performing a wide-ranged experiment without replication, eliminating potentially non-relevant genes, and determining the experimental ‘sweet spot’ by gene-set enrichment plus dose-response correlation analysis. Examination of many cellular processes that are related to UV response, such as DNA repair and cell-cycle arrest, revealed that basically each cellular (sub-) process is active at its own specific spot(s) in the experimental design space. Hence, the use of range finding, based on an affordable protocol like this, enables researchers to conveniently identify the ‘sweet spot’ for their cellular process of interest in an experimental design space and might have far-reaching implications for experimental standardization.
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2016-01-18
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