@article{01HQ8GFWH81YM97YWYSY1ADJNQ,
  abstract     = {{In biomedical research, high-throughput screening is often applied as it comes with automatization, higher-efficiency, and more and faster results. High-throughput screening experiments encompass drug, drug combination, genetic perturbagen or a combination of genetic and chemical perturbagen screens. These experiments are conducted in real-time assays over time or in an endpoint assay. The data analysis consists of data cleaning and structuring, as well as further data processing and visualisation, which, due to the amount of data, can easily become laborious, time-consuming and error-prone. Therefore, several tools have been developed to aid researchers in this process, but these typically focus on specific experimental set-ups and are unable to process data of several time points and genetic-chemical perturbagen screens. To meet these needs, we developed HTSplotter, a web tool and Python module that performs automatic data analysis and visualization of visualization of eitherendpoint or real-time assays from different high-throughput screening experiments: drug, drug combination, genetic perturbagen and genetic-chemical perturbagen screens. HTSplotter implements an algorithm based on conditional statements to identify experiment types and controls. After appropriate data normalization, including growth rate normalization, HTSplotter executes downstream analyses such as dose-response relationship and drug synergism assessment by the Bliss independence (BI), Zero Interaction Potency (ZIP) and Highest Single Agent (HSA) methods. All results are exported as a text file and plots are saved in a PDF file. The main advantage of HTSplotter over other available tools is the automatic analysis of genetic-chemical perturbagen screens and real-time assays where growth rate and perturbagen effect results are plotted over time. In conclusion, HTSplotter allows for the automatic end-to-end data processing, analysis and visualisation of various high-throughput in vitro cell culture screens, offering major improvements in terms of versatility, efficiency and time over existing tools.}},
  articleno    = {{e0296322}},
  author       = {{de Carvalho Nunes, Carolina and Anckaert, Jasper and De Vloed, Fanny and De Wyn, Jolien and Durinck, Kaat and Vandesompele, Jo and Speleman, Franki and Vermeirssen, Vanessa}},
  issn         = {{1932-6203}},
  journal      = {{PLOS ONE}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{19}},
  title        = {{HTSplotter : an end-to-end data processing, analysis and visualisation tool for chemical and genetic in vitro perturbation screening}},
  url          = {{http://doi.org/10.1371/journal.pone.0296322}},
  volume       = {{19}},
  year         = {{2024}},
}

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