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A principal component analysis of 39 scientific impact measures.

ABSTRACT: BACKGROUND: The impact of scientific publications has traditionally been expressed in terms of citation counts. However, scientific activity has moved online over the past decade. To better capture scientific impact in the digital era, a variety of new impact measures has been proposed on the basis of social network analysis and usage log data. Here we investigate how these new measures relate to each other, and how accurately and completely they express scientific impact. METHODOLOGY: We performed a principal component analysis of the rankings produced by 39 existing and proposed measures of scholarly impact that were calculated on the basis of both citation and usage log data. CONCLUSIONS: Our results indicate that the notion of scientific impact is a multi-dimensional construct that can not be adequately measured by any single indicator, although some measures are more suitable than others. The commonly used citation Impact Factor is not positioned at the core of this construct, but at its periphery, and should thus be used with caution.

SUBMITTER: Bollen J

PROVIDER: S-EPMC2699100 | biostudies-literature | 2009

REPOSITORIES: biostudies-literature

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A principal component analysis of 39 scientific impact measures.

Bollen Johan J Van de Sompel Herbert H Hagberg Aric A Chute Ryan R

PloS one 20090629 6

<h4>Background</h4>The impact of scientific publications has traditionally been expressed in terms of citation counts. However, scientific activity has moved online over the past decade. To better capture scientific impact in the digital era, a variety of new impact measures has been proposed on the basis of social network analysis and usage log data. Here we investigate how these new measures relate to each other, and how accurately and completely they express scientific impact.<h4>Methodology< ...[more]

PMID: 19562078

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Project description:Voxel-based principal-component analysis allows for an identification of patterns of glucose metabolism and amyloid deposition related to the conversion from mild cognitive impairment (MCI) to Alzheimer disease (AD). The present study aimed to validate these AD conversion-related patterns (ADCRPs) against neuropathologic findings. Methods: We included patients from the Alzheimer's Disease Neuroimaging Initiative who underwent autopsy and for whom 18F-FDG PET (30 AD, 6 MCI, 2 cognitively normal) and amyloid-β (Aβ) PET (17 AD, 3 MCI, 2 cognitively normal) were available. Pattern expression scores (PESs) of the 18F-FDG- and Aβ-ADCRP were compared with Braak tangle stage and Thal amyloid phase, respectively. Mean 18F-FDG uptake and mean 18F-AV-45 SUV ratio (SUVr) in regions of hypometabolism and elevated amyloid load typical of AD, respectively, were used as volume-of-interest-based PET measures. The diagnostic performance for identifying none-to-low vs. intermediate-to-high AD neuropathologic change (ADNC) was assessed for all biomarkers. Results: We observed significant associations between PES of 18F-FDG-ADCRP and Braak stage (ρ > 0.48, P < 0.005) and between PES of Aβ-ADCRP and Thal phase (ρ > 0.66, P < 0.001). PES of 18F-FDG-ADCRP, PES of Aβ-ADCRP, and their combination identified intermediate-to-high ADNC with an area under the receiver-operating-characteristic curve (AUC) of 0.80, 0.95, and 0.98 (n = 22), respectively. Mean 18F-FDG uptake and mean 18F-AV-45 SUVr in AD-typical regions were also significantly associated with Braak stage (|ρ| > 0.45, P < 0.01) and Thal phase (ρ > 0.55, P < 0.01), respectively. Volume-of-interest-based PET measures discriminated between ADNC stages with an AUC of 0.79, 0.88, and 0.90 for mean 18F-FDG uptake, mean 18F-AV-45 SUVr, and their combination (n = 22), respectively. Contemplating all subjects with available 18F-FDG PET and neuropathology information (n = 38), PES of 18F-FDG-ADCRP was a significant predictor of intermediate-to-high ADNC (AUC = 0.72), whereas mean 18F-FDG uptake was not (AUC = 0.66), although the difference between methods was not significant. Conclusion: PES of 18F-FDG-ADCRP, a measure of neurodegeneration, shows close correspondence with the extent of tau pathology, as assessed by Braak tangle stage. PES of Aβ-ADCRP is a valid biomarker of underlying amyloid pathology, as demonstrated by its strong correlation with Thal phase. The combination of ADCRPs performed better than 18F-FDG-ADCRP alone, although there was only negligible improvement compared with Aβ-ADCRP.

Project description:BackgroundPrincipal component analysis is used to summarize matrix data, such as found in transcriptome, proteome or metabolome and medical examinations, into fewer dimensions by fitting the matrix to orthogonal axes. Although this methodology is frequently used in multivariate analyses, it has disadvantages when applied to experimental data. First, the identified principal components have poor generality; since the size and directions of the components are dependent on the particular data set, the components are valid only within the data set. Second, the method is sensitive to experimental noise and bias between sample groups. It cannot reflect the experimental design that is planned to manage the noise and bias; rather, it estimates the same weight and independence to all the samples in the matrix. Third, the resulting components are often difficult to interpret. To address these issues, several options were introduced to the methodology. First, the principal axes were identified using training data sets and shared across experiments. These training data reflect the design of experiments, and their preparation allows noise to be reduced and group bias to be removed. Second, the center of the rotation was determined in accordance with the experimental design. Third, the resulting components were scaled to unify their size unit.ResultsThe effects of these options were observed in microarray experiments, and showed an improvement in the separation of groups and robustness to noise. The range of scaled scores was unaffected by the number of items. Additionally, unknown samples were appropriately classified using pre-arranged axes. Furthermore, these axes well reflected the characteristics of groups in the experiments. As was observed, the scaling of the components and sharing of axes enabled comparisons of the components beyond experiments. The use of training data reduced the effects of noise and bias in the data, facilitating the physical interpretation of the principal axes.ConclusionsTogether, these introduced options result in improved generality and objectivity of the analytical results. The methodology has thus become more like a set of multiple regression analyses that find independent models that specify each of the axes.

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A principal component analysis of 39 scientific impact measures.

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A principal component analysis of 39 scientific impact measures.

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