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 RNAseqNet: Log-Linear Poisson Graphical Model with Hot-Deck Multiple Imputation (R package, available on CRAN) Nathalie Vialaneix 5641d260203f14d4cbd4b0c7c93afee2 inrae.fr nathalie-vialaneix 4221 21763-4221 https://orcid.org/0000-0003-1156-0639 https://www.idref.fr/101680503 Alyssa Imbert 5e2cb9827453f5139cffee46bfdc3402 inrae.fr alyssa-imbert 753743 30251-753743 https://orcid.org/0000-0003-4868-8927 https://www.idref.fr/231857365 https://viaf.org/viaf/226154440230935342854 Migration ProdInra 9e5ed7d56b52b3d4383123b1ffb42236 inrae.fr 2020-06-05 02:44:33 2025-03-21 15:12:21 2020-06-05 02:44:33 2018 Scientists contributor Migration ProdInra 9e5ed7d56b52b3d4383123b1ffb42236 inrae.fr CCSD hal-02788053 https://hal.inrae.fr/hal-02788053 vialaneix:hal-02788053 2018 2018 INRA - Institut national de la recherche agronomique Anne Archive ouverte en agrobiosciences SOFTWARE Institut National de Recherche en Agriculture, Alimentation et Environnement INRAE Occitanie Toulouse Département MathNum Unité de Mathématiques et Informatique Appliquées de Toulouse Test MathNum Année de la première version : 2018Mode de diffusion : téléchargement anonymePrérequis : R (≥ 3.1.0) No 0.1.2 en production RNAseqNet: Log-Linear Poisson Graphical Model with Hot-Deck Multiple Imputation (R package, available on CRAN) Nathalie Vialaneix 5641d260203f14d4cbd4b0c7c93afee2 inrae.fr nathalie-vialaneix 4221 21763-4221 https://orcid.org/0000-0003-1156-0639 https://www.idref.fr/101680503 Alyssa Imbert 5e2cb9827453f5139cffee46bfdc3402 inrae.fr alyssa-imbert 753743 30251-753743 https://orcid.org/0000-0003-4868-8927 https://www.idref.fr/231857365 https://viaf.org/viaf/226154440230935342854 2018 466837 English network inference missing data RNA-seq Mathematics [math] Computer Science [cs] Software Software Software

Infer log-linear Poisson Graphical Model with an auxiliary data set. Hot-deck multiple imputation method is used to improve the reliability of the inference with an auxiliary dataset. Standard log-linear Poisson graphical model can also be used for the inference and the Stability Approach for Regularization Selection (StARS) is implemented to drive the selection of the regularization parameter. The method is fully described in <doi:10.1093/bioinformatics/btx819>.

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