MusMorph, a database of standardized mouse morphology data for morphometric meta-analyses

Devine, Jay; Vidal‐García, Marta; Liu, Wei; Neves, Amanda; Vercio, Lucas Lo; Green, Rebecca M.; Richbourg, Heather A.; Marchini, Marta; Unger, Colton Michael; Nickle, Audrey C.; Radford, Bethany; Young, Nathan M.; González, Paula N.; Schuler, Robert; Bugacov, Alejandro; Rolian, Campbell; Percival, Christopher J.; Williams, Trevor; Niswander, Lee; Calof, Anne L.; Lander, Arthur D.; Visel, Axel; Jirik, Frank R.; Cheverud, James M.; Klein, Ophir D.; Birnbaum, Ramon Y.; Merrill, Amy E.; Ackermann, Rebecca Rogers; Graf, Daniel; Hemberger, Myriam; Dean, Wendy; Forkert, Nils D.; Murray, Stephen A.; Westerberg, Henrik; Marcucio, Ralph; Hallgrímsson, Benedikt

doi:10.1038/s41597-022-01338-x

Cited by 4 publications

(4 citation statements)

References 105 publications

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“…(1) (Schutz et al, 2022), Gasterosteidae (2) (Fraser & El-Sabaawi, 2022), Hominidae (Krenn et al, 2022), Hynobiidae (Jia et al, 2022), Muridae (a/b) (Devine et al, 2022), Poeciliidae (Riesch et al, 2016), Serranidae (Alós et al, 2014), Cichlidae (Ronco et al, 2020), Colubridae+ (Head & Polly, 2015), Crocodylidae (Watanabe & Slice, 2014), Formicidae (Kennedy et al, 2014), Ocypodidae (Hopkins et al, 2016), Percidae (Martin & Mendelson, 2014), Vespidae (Perrard et al, 2014), andViviparidae (Van Bocxlaer &Hunt, 2013). Dataset details are listed in Table S1.…”

Section: Conflict Of Interest Statementmentioning

confidence: 99%

Classifying high-dimensional phenotypes with ensemble learning

Devine

Kurki

Epp

et al. 2023

Preprint

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Classification is a fundamental task in biology used to assign members to a class. While linear discriminant functions have long been effective, advances in phenotypic data collection are yielding increasingly high-dimensional datasets with more classes, unequal class covariances, and non-linear distributions. Numerous studies have deployed machine learning techniques to classify such distributions, but they are often restricted to a particular organism, a limited set of algorithms, and/or a specific classification task. In addition, the utility of ensemble learning or the strategic combination of models has not been fully explored.We performed a meta-analysis of 33 algorithms across 20 datasets containing over 20,000 high-dimensional shape phenotypes using an ensemble learning framework. Both binary (e.g., sex, environment) and multi-class (e.g., species, genotype, population) classification tasks were considered. The ensemble workflow contains functions for preprocessing, training individual learners and ensembles, and model evaluation. We evaluated algorithm performance within and among datasets. Furthermore, we quantified the extent to which various dataset and phenotypic properties impact performance.We found that discriminant analysis variants and neural networks were the most accurate base learners on average. However, their performance varied substantially between datasets. Ensemble models achieved the highest performance on average, both within and among datasets, increasing average accuracy by up to 3% over the top base learner. Higher class R2values, mean class shape distances, and between– vs. within-class variances were positively associated with performance, whereas higher class covariance distances were negatively associated. Class balance and total sample size were not predictive.Learning-based classification is a complex task driven by many hyperparameters. We demonstrate that selecting and optimizing an algorithm based on the results of another study is a flawed strategy. Ensemble models instead offer a flexible approach that is data agnostic and exceptionally accurate. By assessing the impact of various dataset and phenotypic properties on classification performance, we also offer potential explanations for variation in performance. Researchers interested in maximizing performance stand to benefit from the simplicity and effectiveness of our approach made accessible via the R packagepheble.

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Section: Conflict Of Interest Statementmentioning

confidence: 99%

Classifying high-dimensional phenotypes with ensemble learning

Devine

Kurki

Epp

et al. 2023

Preprint

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“…The five samples of each stage were mirrored to double the amount of data. Then, a landmark-based rigid registration was performed between each of the remaining samples in the age group and their reference sample, using five landmarks placed in the head by an expert observer (Devine et al, 2022) via SimpleITK in Python (Lowekamp et al, 2013)). Then, the resulting transformation was applied to the correspondent tissue segmentation.…”

Section: Registration For Atlasesmentioning

confidence: 99%

Quantifying the relationship between cell proliferation and morphology during development of the face

Green

Vercio

Dauter

et al. 2023

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Morphogenesis requires highly coordinated, complex interactions between cellular processes: proliferation, migration, and apoptosis, along with physical tissue interactions. How these cellular and tissue dynamics drive morphogenesis remains elusive. Three dimensional (3D) microscopic imaging poses great promise, and generates beautiful images. However, generating even moderate through-put quantified images is challenging for many reasons. As a result, the association between morphogenesis and cellular processes in 3D developing tissues has not been fully explored. To address this critical gap, we have developed an imaging and image analysis pipeline to enable 3D quantification of cellular dynamics along with 3D morphology for the same individual embryo. Specifically, we focus on how 3D distribution of proliferation relates to morphogenesis during mouse facial development. Our method involves imaging with light-sheet microscopy, automated segmentation of cells and tissues using machine learning-based tools, and quantification of external morphology via geometric morphometrics. Applying this framework, we show that changes in proliferation are tightly correlated to changes in morphology over the course of facial morphogenesis. These analyses illustrate the potential of this pipeline to investigate mechanistic relationships between cellular dynamics and morphogenesis during embryonic development.

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“…, Wong et al ., 2014; Rolfe, Whikehart and Maga, 2023), comparing size and shape of regions of interest using geometric morphometrics ( e.g. , Devine et al ., 2022), comparing voxel intensities ( e.g. , Ashburner & Friston 2000; Wong et al ., 2014), or comparing deformation fields and their derivatives ( e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Quantitative computational approaches support the identification of more subtle phenotypes often missed by qualitative observation; the detection of novel phenotypes at earlier developmental stages, before they become apparent to human observers; and systematization and automation. 3D image analysis techniques for phenotype discovery may include automated segmentation of organs to calculate their volumes (e.g., Wong et al, 2014;Rolfe, Whikehart and Maga, 2023), comparing size and shape of regions of interest using geometric morphometrics (e.g., Devine et al, 2022), comparing voxel intensities (e.g., Ashburner & Friston 2000;Wong et al, 2014), or comparing deformation fields and their derivatives (e.g., Horner et al, 2021;Roy et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Morphological simulation tests the limits on phenotype discovery in 3D image analysis

Roston,

Whikehart,

Rolfe

et al. 2024

Preprint

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In the past few decades, advances in 3D imaging have created new opportunities for reverse genetic screens. Rapidly growing datasets of 3D images of genetic knockouts require high-throughput, automated computational approaches for identifying and characterizing new phenotypes. However, exploratory, discovery-oriented image analysis pipelines used to discover these phenotypes can be difficult to validate because, by their nature, the expected outcome is not knowna priori. Introducingknownmorphological variation through simulation can help distinguish between real phenotypic differences and random variation; elucidate the effects of sample size; and test the sensitivity and reproducibility of morphometric analyses. Here we present a novel approach for 3D morphological simulation that uses open-source, open-access tools available in 3D Slicer, SlicerMorph, and Advanced Normalization Tools in R (ANTsR). While we focus on diffusible-iodine contrast-enhanced micro-CT (diceCT) images, this approach can be used on any volumetric image. We then use our simulated datasets to test whether tensor-based morphometry (TBM) can recover our introduced differences; to test how effect size and sample size affect detectability; and to determine the reproducibility of our results. In our approach to morphological simulation, we first generate a simulated deformation based on a reference image and then propagate this deformation to subjects using inverse transforms obtained from the registration of subjects to the reference. This produces a new dataset with a shifted population mean while retaining individual variability because each sample deforms more or less based on how different or similar it is from the reference. TBM is a widely-used technique that statistically compares local volume differences associated with local deformations. Our results showed that TBM recovered our introduced morphological differences, but that detectability was dependent on the effect size, the sample size, and the region of interest (ROI) included in the analysis. Detectability of subtle phenotypes can be improved both by increasing the sample size and by limiting analyses to specific body regions. However, it is not always feasible to increase sample sizes in screens of essential genes. Therefore, methodical use of ROIs is a promising way to increase the power of TBM to detect subtle phenotypes. Generating known morphological variation through simulation has broad applicability in developmental, evolutionary, and biomedical morphometrics and is a useful way to distinguish between a failure to detect morphological difference and a true lack of morphological difference. Morphological simulation can also be applied to AI-based supervised learning to augment datasets and overcome dataset limitations.

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MusMorph, a database of standardized mouse morphology data for morphometric meta-analyses

Cited by 4 publications

References 105 publications

Classifying high-dimensional phenotypes with ensemble learning

Classifying high-dimensional phenotypes with ensemble learning

Quantifying the relationship between cell proliferation and morphology during development of the face

Morphological simulation tests the limits on phenotype discovery in 3D image analysis

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