Dataset Information

Semantic concept schema of the linear mixed model of experimental observations.

ABSTRACT: In the information age, smart data modelling and data management can be carried out to address the wealth of data produced in scientific experiments. In this paper, we propose a semantic model for the statistical analysis of datasets by linear mixed models. We tie together disparate statistical concepts in an interdisciplinary context through the application of ontologies, in particular the Statistics Ontology (STATO), to produce FAIR data summaries. We hope to improve the general understanding of statistical modelling and thus contribute to a better description of the statistical conclusions from data analysis, allowing their efficient exploration and automated processing.

SUBMITTER: Cwiek-Kupczynska H

PROVIDER: S-EPMC7046786 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Semantic concept schema of the linear mixed model of experimental observations.

Ćwiek-Kupczyńska Hanna H Filipiak Katarzyna K Markiewicz Augustyn A Rocca-Serra Philippe P Gonzalez-Beltran Alejandra N AN Sansone Susanna-Assunta SA Millet Emilie J EJ van Eeuwijk Fred F Ławrynowicz Agnieszka A Krajewski Paweł P

Scientific data 20200227 1

In the information age, smart data modelling and data management can be carried out to address the wealth of data produced in scientific experiments. In this paper, we propose a semantic model for the statistical analysis of datasets by linear mixed models. We tie together disparate statistical concepts in an interdisciplinary context through the application of ontologies, in particular the Statistics Ontology (STATO), to produce FAIR data summaries. We hope to improve the general understanding ...[more]

PMID: 32109232

Similar Datasets

Project description:Background: Genome-wide detection of single feature polymorphisms (SFP) in swine using transcriptome profiling of day 25 placental RNA by contrasting probe intensities from either Meishan or an occidental composite breed with Affymetrix porcine microarrays is presented. A linear mixed model analysis was used to identify significant breed-by-probe interactions. Results: Gene specific linear mixed models were fit to each of the log2 transformed probe intensities on these arrays, using fixed effects for breed, probe, breed-by-probe interaction, and a random effect for array. After surveying the day 25 placental transcriptome, 789 probes with a q-value ≤ 0.05 and |fold change| ≥ 2 for the breed-by-probe interaction were identified as candidates containing SFP. To address the quality of the bioinformatics approach, universal pyrosequencing assays were designed from Affymetrix exemplar sequences to independently assess polymorphisms within a subset of probes. Of those probes sampled from high-, medium-, and low-ranking categories, 20 of 27 were confirmed by pyrosequencing to contain SFPs. In most cases, the 25-mer probe sequence printed on the microarray diverged from Meishan, not occidental crosses. This analysis was used to define a set of highly reliable predicted SFPs according to their probability scores. Conclusions: By this method we detected transition and transversion single nucleotide polymorphisms, as well as insertions/deletions. These results demonstrate that this approach can identify polymorphisms between two breeds and/or lines of any species for which a short oligonucleotide array is available, and can be used to rapidly develop markers for genetic mapping and association analysis in species where high density genotyping platforms are otherwise unavailable. SNPs and INDELS discovered by this approach have been publicly deposited in NCBI’s SNP repository dbSNP. This method is an attractive bioinformatics tool for uncovering breed-by-probe interactions, for rapidly identifying expressed SNPs, for investigating potential functional correlations between gene expression and breed polymorphisms, and is robust enough to be used on any Affymetrix gene expression platform. Keywords: Transcriptional profiling of Day 25 porcine placentas

Dataset Information

Semantic concept schema of the linear mixed model of experimental observations.

Publications

Semantic concept schema of the linear mixed model of experimental observations.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets