Multiple hot-deck imputation for network inference from RNA sequencing data

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Imbert, Alyssa | Valsesia, Armand | Le Gall, Caroline | Armenise, Claudia | Lefebvre, Gregory | Gourraud, Pierre-Antoine | Viguerie, Nathalie | Vialaneix, Nathalie

Edité par CCSD ; Oxford University Press (OUP) -

International audience. Motivation: Network inference provides a global view of the relations existing between gene expression in a given transcriptomic experiment (often only for a restricted list of chosen genes). However, it is still a challenging problem: even if the cost of sequencing techniques has decreased over the last years, the number of samples in a given experiment is still (very) small compared to the number of genes. Results: We propose a method to increase the reliability of the inference when RNA-seq expression data have been measured together with an auxiliary dataset that can provide external information on gene expression similarity between samples. Our statistical approach, hd-MI, is based on imputation for samples without available RNA-seq data that are considered as missing data but are observed on the secondary dataset. hd-MI can improve the reliability of the inference for missing rates up to 30% and provides more stable networks with a smaller number of false positive edges. On a biological point of view, hd-MI was also found relevant to infer networks from RNA-seq data acquired in adipose tissue during a nutritional intervention in obese individuals. In these networks, novel links between genes were highlighted, as well as an improved comparability between the two steps of the nutritional intervention. Availability: Software and sample data are available as an R package, RNAseqNet, that can be downloaded from the Comprehensive R Archive Network (CRAN).

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