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Beyond t test and ANOVA: applications of mixed-effects models for more rigorous statistical analysis in neuroscience research.

ABSTRACT: In basic neuroscience research, data are often clustered or collected with repeated measures, hence correlated. The most widely used methods such as t test and ANOVA do not take data dependence into account and thus are often misused. This Primer introduces linear and generalized mixed-effects models that consider data dependence and provides clear instruction on how to recognize when they are needed and how to apply them. The appropriate use of mixed-effects models will help researchers improve their experimental design and will lead to data analyses with greater validity and higher reproducibility of the experimental findings.

SUBMITTER: Yu Z

PROVIDER: S-EPMC8763600 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Publications

Beyond t test and ANOVA: applications of mixed-effects models for more rigorous statistical analysis in neuroscience research.

Yu Zhaoxia Z Guindani Michele M Grieco Steven F SF Chen Lujia L Holmes Todd C TC Xu Xiangmin X

Neuron 20211115 1

In basic neuroscience research, data are often clustered or collected with repeated measures, hence correlated. The most widely used methods such as t test and ANOVA do not take data dependence into account and thus are often misused. This Primer introduces linear and generalized mixed-effects models that consider data dependence and provides clear instruction on how to recognize when they are needed and how to apply them. The appropriate use of mixed-effects models will help researchers improve ...[more]

PMID: 34784504

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Project description:Tissues are often made up of multiple cell-types. Blood, for example, contains many different cell-types, each with its own functional attributes and molecular signature. In humans, because of its accessibility and immune functionality, blood cells have been used as a source for RNA-based biomarkers for many diseases. Yet, the proportions of any given cell-type in the blood can vary markedly, even between normal individuals. This results in a significant loss of sensitivity in gene expression studies of blood cells and great difficulty in identifying the cellular source of any perturbations. Ideally, one would like to perform differential expression analysis between patient groups for each of the cell-types within a tissue but this is impractical and prohibitively expensive. To test the relationship between measured gene expression in mixed samples and the expression of genes in the isolated pure subsets, we begin with a situation in which all factors are known. Tissue samples from the brain, liver and lung of a single rat were analyzed using expression arrays (Affymetrix) in triplicate. Homogenates of those three tissues were then mixed together at the cRNA level. We then measured the gene expression pattern of each mixed sample. Such mixtures mimic the common scenario in which biological samples in a dataset are heterogeneous and vary in the relative frequency of the component subsets from one another. We mixed rat brain, liver and lung biospecimens derived from one animal at the cRNA homogenate level in different proportions. 3 technical replicates each. Snap frozen rat liver and brain was kept frozen while cutting it into pieces. cDNA synthesis and labeling was done with a starting amount of 1 Î¼g, using the Affymetrix Eukaryotic One-Cycle Target Hybridization. Washing, Staining and scanning protocol for Eukaryotic Cartridge Arrays with User-Prepared Buffers and Sloutions according to the technical manuals (Affymetrix GeneChip Expression, Analysis for Cartridge Arrays using the GCAS version 1.4, Affymetrix GeneChip Expression Analysis (P/N 701021,Rev. 5) , Affymetrix GeneChip Expression Wash, Stain and Scan (P/N 702731, Rev. 3) , following the manufacturer/s instructions. Data was RMA normalized.

Dataset Information

Beyond t test and ANOVA: applications of mixed-effects models for more rigorous statistical analysis in neuroscience research.

Publications

Beyond t test and ANOVA: applications of mixed-effects models for more rigorous statistical analysis in neuroscience research.

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