Abstract | ||
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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. |
Year | DOI | Venue |
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2018 | 10.1093/bioinformatics/btx819 | BIOINFORMATICS |
Field | DocType | Volume |
Data mining,Inference,Computer science,Software,Imputation (statistics),Missing data,Comparability,R package | Journal | 34 |
Issue | ISSN | Citations |
10 | 1367-4803 | 0 |
PageRank | References | Authors |
0.34 | 5 | 8 |
Name | Order | Citations | PageRank |
---|---|---|---|
Alyssa Imbert | 1 | 0 | 0.34 |
Armand Valsesia | 2 | 1 | 1.05 |
Caroline Le Gall | 3 | 0 | 0.34 |
Claudia Armenise | 4 | 0 | 0.34 |
Gregory Lefebvre | 5 | 0 | 0.34 |
Pierre-Antoine Gourraud | 6 | 2 | 1.80 |
Nathalie Viguerie | 7 | 1 | 0.73 |
Nathalie Villa-Vialaneix | 8 | 72 | 10.94 |