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

Alyssa Imbert (1) , Armand Valsesia (2) , Caroline Le Gall (3) , Claudia Armenise (4) , Gregory Lefebvre (5) , Pierre-Antoine Gourraud (3) , Nathalie Viguerie (6) , Nathalie Vialaneix (1)

1 MIAT INRA - Unité de Mathématiques et Informatique Appliquées de Toulouse
2 SIB - Swiss Institute of Bioinformatics [Lausanne]
3 Methodomics
4 Quartzbio
5 Protéines de défense des réponses immune et inflammatoire : identification, régulation et rôles physiopathologiques
6 I2MC - Institut des Maladies Métaboliques et Cardiovasculaires

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).

Domaines: Applications [stat.AP]

Alyssa Imbert, Armand Valsesia, Caroline Le Gall, Claudia Armenise, Gregory Lefebvre, et al.. Multiple hot-deck imputation for network inference from RNA sequencing data. Bioinformatics, 2018, 34 (10), pp.1726 - 1732. ⟨10.1093/bioinformatics/btx819⟩. ⟨hal-01794575⟩