Sample-Size Planning for Multivariate Data: A Raman-Spectroscopy-Based Example

Ali, Nairveen; Girnus, Sophie; Rösch, Petra; Popp, Jürgen; Bocklitz, Thomas

doi:10.1021/acs.analchem.8b02167

Cited by 39 publications

(33 citation statements)

References 35 publications

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“…Otherwise, the intragroup variations cannot be well extracted or removed properly from the calculation. The influence of sample size in statistical modeling falls into the field of sample size planning and was investigated in one of our latest studies …”

Section: Resultsmentioning

confidence: 99%

“…All species were independently cultivated in nine replicates. The sample preparation and Raman spectroscopy has been described in one of our previous studies . The sample size of each species was summarized in Table .…”

Section: Experimental and Methodsmentioning

confidence: 99%

“…The sample preparation and Raman spectroscopy has been described in one of our previous studies. 14 The sample size of each species was summarized in Table 2. The mean spectra of the six species are shown in Figure 2A.…”

Section: Bacteria Datasetmentioning

confidence: 99%

“…The influence of sample size in statistical modeling falls into the field of sample size planning and was investigated in one of our latest studies. 14…”

Section: Bacteria Datamentioning

confidence: 99%

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Modified PCA and PLS: Towards a better classification in Raman spectroscopy–based biological applications

Guo

Rösch

Popp

et al. 2019

Journal of Chemometrics

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Raman spectra of biological samples often exhibit variations originating from changes of spectrometers, measurement conditions, and cultivation conditions. Such unwanted variations make a classification extremely challenging, especially if they are more significant compared with the differences between groups to be separated. A classifier is prone to such unwanted variations (ie, intragroup variations) and can fail to learn the patterns that can help separate different groups (ie, intergroup differences). This often leads to a poor generalization performance and a degraded transferability of the trained model. A natural solution is to separate the intragroup variations from the intergroup differences and build the classifier based on merely the latter information, for example, by a well‐designed feature extraction. This forms the idea of this contribution. Herein, we modified two commonly applied feature extraction approaches, principal component analysis (PCA) and partial least squares (PLS), in order to extract merely the features representing the intergroup differences. Both of the methods were verified with two Raman spectral datasets measured from bacterial cultures and colon tissues of mice, respectively. In comparison to ordinary PCA and PLS, the modified PCA was able to improve the prediction on the testing data that bears significant difference to the training data, while the modified PLS could help avoid overfitting and lead to a more stable classification.

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Section: Resultsmentioning

confidence: 99%

Section: Experimental and Methodsmentioning

confidence: 99%

Section: Bacteria Datasetmentioning

confidence: 99%

“…The influence of sample size in statistical modeling falls into the field of sample size planning and was investigated in one of our latest studies. 14…”

Section: Bacteria Datamentioning

confidence: 99%

See 2 more Smart Citations

Modified PCA and PLS: Towards a better classification in Raman spectroscopy–based biological applications

Guo

Rösch

Popp

et al. 2019

Journal of Chemometrics

Self Cite

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show abstract

“…Sampling size assessment of SCRS from environmental samples were discussed in previous studies but is often restricted to situational applications. Learning-curve (LC) based technique targeting 5% Bayes error rate was proven effective to investigate proper sample size to train a classifier 31,32 ; however it is a quite different objective and this method is not suitable for unsupervised applications. Majed et al (2009) first attempted a practical solution by iteratively sampling and classifying samples, tracking abundance changes of classified categories 25 .…”

Section: Introductionmentioning

confidence: 99%

Machine Leaning-based Determination of Sampling Depth for Complex Environmental Systems: Case Study with Single-Cell Raman Spectroscopy Data in EBPR Systems

Wang

et al. 2020

Preprint

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Rapid progress in various advanced analytical methods such as single-cell technologies enable unprecedented and deeper understanding of microbial ecology beyond the resolution of conventional approaches. A major application challenge exists in the determination of sufficient sample size without sufficient prior knowledge of the community complexity and, the need to balance between statistical power and limited time or resources. This hinders the desired standardization and wider application of these technologies. Here, we proposed, tested and validated a computational sampling size assessment protocol taking advantage of a metric, named kernel divergence. This metric has two advantages: First, it directly compares dataset-wise distributional differences with no requirements on human intervention or prior knowledge-based pre-classification. Second, minimal assumptions in distribution and sample space are made in data processing to enhance its application domain. This enables test-verified appropriate handling of datasets with both linear and non-linear relationships. The model was then validated in a case study with eight SCRS phenotyping datasets each sampled from a different enhanced biological phosphorus removal (EBPR) activated sludge community located across North America. The model allows the determination of sufficient sampling size for any targeted or customized information capture capacity or resolution level. For example, an approximated sampling size of 50 or 100 spectra for full-scale EBPR-related ecosystems at 5% or 2% OPU cluster resolution. Promised by its flexibility and minimal restriction of input data types, the proposed method is expected to be a standardized approach for sampling size optimization, enabling more comparable and reproducible experiments and analysis on complex environmental samples. Finally, these advantages exhibit the capability of generalizing to other single-cell technologies or environmental applications, provided that the input datasets contain only continuous features.TOC

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Photonic Technology for In Vivo Monitoring of Hypoxia–Ischemia

et al. 2022

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Surveillance of physiological parameters of newborns during delivery triggers medical decision-making, can rescue life and health, and helps avoid unnecessary cesareans. Here, the development of a photonic technology for monitoring perinatal asphyxia is presented and validated in vivo in a preclinical stage. Contrary to state of the art, the technology provides continuous data in real-time in a non-invasive manner. Moreover, the technology does not rely on a single parameter as pH or lactate, instead monitors changes of the entirety of physiological parameters accessible by Raman spectroscopy. By a fiber-coupled Raman probe that is in controlled contact with the skin of the subject, near-infrared Raman spectra are measured and analyzed by machine learning algorithms to develop classification models. As a performance benchmarking, various hybrid and non-hybrid classifiers are tested. In an asphyxia model in newborn pigs, more than 1000 Raman spectra are acquired at three different clinical phases-basal condition, hypoxia-ischemia, and post-hypoxia-ischemia stage. In this preclinical proof-of-concept study, figures of merit reach 90% levels for classifying the clinical phases and demonstrate the power of the technology as an innovative medical tool for diagnosing a perinatal adverse outcome.

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Sample-Size Planning for Multivariate Data: A Raman-Spectroscopy-Based Example

Cited by 39 publications

References 35 publications

Modified PCA and PLS: Towards a better classification in Raman spectroscopy–based biological applications

Modified PCA and PLS: Towards a better classification in Raman spectroscopy–based biological applications

Machine Leaning-based Determination of Sampling Depth for Complex Environmental Systems: Case Study with Single-Cell Raman Spectroscopy Data in EBPR Systems

Photonic Technology for In Vivo Monitoring of Hypoxia–Ischemia

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