Partial least squares: a versatile tool for the analysis of high-dimensional genomic data

Boulesteix, Anne‐Laure; Strimmer, Korbinian

doi:10.1093/bib/bbl016

Cited by 675 publications

(514 citation statements)

References 79 publications

Supporting

Mentioning

512

Contrasting

Unclassified

Order By: Relevance

“…S2. The partial leastsquares regression approach is beneficial because it uses the continuous spectrum as a single measurement rather than in a band-by-band type of analysis (70,71). Across 79 1-ha field plots in Peru, Asner et al (16,17) showed that LMA could be retrieved with an uncertainty (root mean squared error or RMSE) of 11.8 g m −2 across a LMA range of 76-180 mg g −1 .…”

Section: Methodsmentioning

confidence: 99%

Large-scale climatic and geophysical controls on the leaf economics spectrum

Asner

Knapp

Anderson

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

108

View full text Add to dashboard Cite

Leaf economics spectrum (LES) theory suggests a universal tradeoff between resource acquisition and storage strategies in plants, expressed in relationships between foliar nitrogen (N) and phosphorus (P), leaf mass per area (LMA), and photosynthesis. However, how environmental conditions mediate LES trait interrelationships, particularly at large biospheric scales, remains unknown because of a lack of spatially explicit data, which ultimately limits our understanding of ecosystem processes, such as primary productivity and biogeochemical cycles. We used airborne imaging spectroscopy and geospatial modeling to generate, to our knowledge, the first biospheric maps of LES traits, here centered on 76 million ha of Andean and Amazonian forest, to assess climatic and geophysical determinants of LES traits and their interrelationships. Elevation and substrate were codominant drivers of leaf trait distributions. Multiple additional climatic and geophysical factors were secondary determinants of plant traits. Anticorrelations between N and LMA followed general LES theory, but topo-edaphic conditions strongly mediated and, at times, eliminated this classic relationship. We found no evidence for simple P-LMA or N-P trade-offs in forest canopies; rather, we mapped a continuum of N-P-LMA interactions that are sensitive to elevation and temperature. Our results reveal nested climatic and geophysical filtering of LES traits and their interrelationships, with important implications for predictions of forest productivity and acclimation to rapid climate change.Amazon basin | functional biogeography | leaf traits | plant traits | tropical forests

show abstract

Section: Methodsmentioning

confidence: 99%

Large-scale climatic and geophysical controls on the leaf economics spectrum

Asner

Knapp

Anderson

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

108

View full text Add to dashboard Cite

show abstract

“…Weights, however, are a nonlinear combination of predictor and response variables (Nguyen & Rockeb, 2004). There are approaches (Liu & Rayens, 2007;Boulesteix & Strimmer, 2006;Fort & Lambert-Lacroix, 2005;Nguyen & Rockeb, 2004;Man et al, 2004;Huang et al, 2005) applying the original PLS to categorical, binary responses. However, research confirms that it is better to use PLS procedures adjusted to binary responses (Nguyen & Rockeb, 2002).…”

Section: Dealing With High Dimensionalitymentioning

confidence: 99%

“…Also, weights are dependent on sample predictor variances and the partial correlation coefficient (Garthwaite, 1994;Nguyen & Rockeb, 2004). PLS is also used in conjunction with Linear Discriminant Analysis (LDA) (Boulesteix & Strimmer, 2006;Boulesteix, 2004;Liu & Rayens, 2007). Fort and Lambert-Lacroix (Fort & Lambert-Lacroix, 2005) proposed a combination of the PLS and Ridge penalty.…”

Section: Dealing With High Dimensionalitymentioning

confidence: 99%

A Comprehensive Analysis of MALDI-TOF Spectrometry Data

Plechawska–Wójcik¹

2012

Medical Informatics

View full text Add to dashboard Cite

“…The PLSGLR method chooses latent components and considers the response variable in regression, which is different from similar methods, such as principal component regression [44].…”

Section: Feature Rankingmentioning

confidence: 99%

Feature Selection Solution with High Dimensionality and Low-Sample Size for Land Cover Classification in Object-Based Image Analysis

et al. 2017

View full text Add to dashboard Cite

Land cover information extraction through object-based image analysis (OBIA) has become an important trend in remote sensing, thanks to the increasing availability of high-resolution imagery. Segmented objects have a large number of features that cause high-dimension and low-sample size problems in the classification process. In this study, on the basis of a partial least squares generalized linear regression (PLSGLR), we propose a group corrected PLSGLR, known as G-PLSGLR, that aims to reduce the redundancy of object features for land cover identifications. Using Gaofen-2 images, the area of interest was segmented and sampled to generate small sample-size training datasets with 51 object features. The features selected by G-PLSGLR were compared against a guided regularized random forest (GRRF) in metrics of reduction rate, feature redundancy, and accuracy assessment of classification. Three indicators of overall accuracy (OA), user's accuracy (UA), and producer's accuracy (PA) were applied for accuracy assessment in this paper. The result shows that the G-PLSGLR achieved a reduction rate of 9.27 with a feature redundancy of 0.29, and a value of OA 90.63%. The GRRF achieved a reduction rate of 1.61 with a feature redundancy of 0.42, and a value of OA 85.56%. The PA of each land cover category was more than 95% using features selected by G-PLSGLR, while the PA ranged from 77 to 96% using features selected by GRRF. The UA of G-PLSGLR-selected features ranged from 70 to 80% except for grass land and bare land, which achieved 10% higher UA than GRRF-selected features. The G-PLSGLR method we proposed has the advantages of a large reduction rate, low feature redundancy, and high classification performance, which can be applied in OBIA-based land cover classification.

show abstract

Partial least squares: a versatile tool for the analysis of high-dimensional genomic data

Cited by 675 publications

References 79 publications

Large-scale climatic and geophysical controls on the leaf economics spectrum

Large-scale climatic and geophysical controls on the leaf economics spectrum

A Comprehensive Analysis of MALDI-TOF Spectrometry Data

Feature Selection Solution with High Dimensionality and Low-Sample Size for Land Cover Classification in Object-Based Image Analysis

Contact Info

Product

Resources

About