Cardiovascular Function and Structure are Preserved Despite Induced Ablation of BMP1-Related Proteinases

Golob, Mark J.; Massoudi, Dawiyat; Tabima, Diana M.; Johnston, James L.; Wolf, Gregory D.; Hacker, Timothy A.; Greenspan, Daniel S.; Chesler, Naomi C.

doi:10.1007/s12195-018-0534-y

Cited by 3 publications

(1 citation statement)

References 53 publications

(84 reference statements)

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“…The gap is unfortunate, because in vivo studies are the gold standard to compare phenotypes across species 18,19 , disease models 20,21 , and laboratories 22–26 . Animal surrogates can offer insight into the (patho)physiologic function of individual proteins, but interpreting the consequences of in vivo perturbations is complicated 27,28 . Applying PLSR quantitatively to in vivo data may better identify the underlying networks that, when perturbed, yield clinically relevant phenotypes.…”

Section: Introductionmentioning

confidence: 99%

Robust latent-variable interpretation of in vivo regression models by nested resampling

Caulk

Janes

2019

Sci Rep

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Simple multilinear methods, such as partial least squares regression (PLSR), are effective at interrelating dynamic, multivariate datasets of cell–molecular biology through high-dimensional arrays. However, data collected in vivo are more difficult, because animal-to-animal variability is often high, and each time-point measured is usually a terminal endpoint for that animal. Observations are further complicated by the nesting of cells within tissues or tissue sections, which themselves are nested within animals. Here, we introduce principled resampling strategies that preserve the tissue-animal hierarchy of individual replicates and compute the uncertainty of multidimensional decompositions applied to global averages. Using molecular–phenotypic data from the mouse aorta and colon, we find that interpretation of decomposed latent variables (LVs) changes when PLSR models are resampled. Lagging LVs, which statistically improve global-average models, are unstable in resampled iterations that preserve nesting relationships, arguing that these LVs should not be mined for biological insight. Interestingly, resampling is less discriminatory for multidimensional regressions of in vitro data, where replicate-to-replicate variance is sufficiently low. Our work illustrates the challenges and opportunities in translating systems-biology approaches from cultured cells to living organisms. Nested resampling adds a straightforward quality-control step for interpreting the robustness of in vivo regression models.

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Section: Introductionmentioning

confidence: 99%

Robust latent-variable interpretation of in vivo regression models by nested resampling

Caulk

Janes

2019

Sci Rep

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IGF-II regulates lysyl oxidase propeptide and mediates its effects in part via basic helix-loop-helix E40

Adewale,

Sharma,

Mouawad

et al. 2024

Matrix Biology

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Robust latent-variable interpretation ofin vivoregression models by nested resampling

Caulk¹,

Janes

2019

Preprint

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42Simple multilinear methods, such as partial least squares regression (PLSR), are effective 43 at interrelating dynamic, multivariate datasets of cell-molecular biology through high-dimensional 44 arrays. However, data collected in vivo are more difficult, because animal-to-animal variability is 45 often high, and each time-point measured is usually a terminal endpoint for that animal. 46Observations are further complicated by the nesting of cells within tissues or tissue sections, which 47 themselves are nested within animals. Here, we introduce principled resampling strategies that 48 preserve the tissue-animal hierarchy of individual replicates and compute the uncertainty of 49 multidimensional decompositions applied to global averages. Using molecular-phenotypic data 50 from the mouse aorta and colon, we find that interpretation of decomposed latent variables (LVs) 51 changes when PLSR models are resampled. Lagging LVs, which statistically improve global-52 average models, are unstable in resampled iterations that preserve nesting relationships, arguing 53 that these LVs should not be mined for biological insight. Interestingly, resampling is less 54 discriminatory for multidimensional regressions of in vitro data, suggesting it is unnecessary when 55 replicate-to-replicate variance is low. Our work illustrates the challenges and opportunities in 56 translating systems-biology approaches from cultured cells to living organisms. Nested resampling 57 adds a straightforward quality-control step aiding the interpretability of in vivo regression models. 58 59

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Cardiovascular Function and Structure are Preserved Despite Induced Ablation of BMP1-Related Proteinases

Cited by 3 publications

References 53 publications

Robust latent-variable interpretation of in vivo regression models by nested resampling

Robust latent-variable interpretation of in vivo regression models by nested resampling

IGF-II regulates lysyl oxidase propeptide and mediates its effects in part via basic helix-loop-helix E40

Robust latent-variable interpretation ofin vivoregression models by nested resampling

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