Smooth functions and local extreme values

Kovac, Arne

doi:10.1016/j.csda.2006.08.018

Cited by 12 publications

(13 citation statements)

References 24 publications

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“…With cross‐validation we obtained a bandwidth of 4.092 and the peak preserving procedure is able to filter out the noise, retaining the relevant peaks, in agreement with the results reported by Kovac (2007). See Figure 12, where for comparison we also included a conventional local linear estimate (with a bandwidth chosen via cross‐validation).…”

Section: Real Data Examplessupporting

confidence: 88%

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Local linear fitting and improved estimation near peaks

Desmet

Gijbels

2009

Can J Statistics

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The authors look into the problem of estimating regression functions that exhibit jump irregularities in the first derivative. They investigate the behaviour of the bias in the local linear fit and show the superior performance of appropriate one‐sided versions of the local linear fit near such irregularities. They then propose an improved estimation procedure based on data‐driven selection of a conventional or one‐sided local linear fit according to a residual sum of squares type of criterion. The authors provide theoretical results and illustrate the method both on simulated and real‐life data examples. The Canadian Journal of Statistics 37: 453–475; 2009 © 2009 Statistical Society of Canada

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Section: Real Data Examplessupporting

confidence: 88%

“…As mentioned in the introduction, the aim here is to remove noise so that only peaks relevant from a chemical point of view remain. This data set was analyzed in Kovac (2007) where the aim was to reveal the true modality.…”

Section: Real Data Examplesmentioning

confidence: 99%

Local linear fitting and improved estimation near peaks

Desmet

Gijbels

2009

Can J Statistics

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“…The main goal of this study is to develop an efficient and effective denoising algorithm to remove biases and noise from readcount data for better identification of CNVs using NGS data. In this work, we introduced an efficient and accurate denoising technique based on a signal processing approach, Taut String [ 53 – 55 ]. This approach efficiently removes noise while preserves breakpoints and prepares error free readcount data for the segmentation.…”

Section: Introductionmentioning

confidence: 99%

Noise cancellation using total variation for copy number variation detection

2018

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BackgroundDue to recent advances in sequencing technologies, sequence-based analysis has been widely applied to detecting copy number variations (CNVs). There are several techniques for identifying CNVs using next generation sequencing (NGS) data, however methods employing depth of coverage or read depth (RD) have recently become a main technique to identify CNVs. The main assumption of the RD-based CNV detection methods is that the readcount value at a specific genomic location is correlated with the copy number at that location. However, readcount data’s noise and biases distort the association between the readcounts and copy numbers. For more accurate CNV identification, these biases and noise need to be mitigated. In this work, to detect CNVs more precisely and efficiently we propose a novel denoising method based on the total variation approach and the Taut String algorithm.ResultsTo investigate the performance of the proposed denoising method, we computed sensitivities, false discovery rates and specificities of CNV detection when employing denoising, using both simulated and real data. We also compared the performance of the proposed denoising method, Taut String, with that of the commonly used approaches such as moving average (MA) and discrete wavelet transforms (DWT) in terms of sensitivity of detecting true CNVs and time complexity. The results show that Taut String works better than DWT and MA and has a better power to identify very narrow CNVs. The ability of Taut String denoising in preserving CNV segments’ breakpoints and narrow CNVs increases the detection accuracy of segmentation algorithms, resulting in higher sensitivities and lower false discovery rates.ConclusionsIn this study, we proposed a new denoising method for sequence-based CNV detection based on a signal processing technique. Existing CNV detection algorithms identify many false CNV segments and fail in detecting short CNV segments due to noise and biases. Employing an effective and efficient denoising method can significantly enhance the detection accuracy of the CNV segmentation algorithms. Advanced denoising methods from the signal processing field can be employed to implement such algorithms. We showed that non-linear denoising methods that consider sparsity and piecewise constant characteristics of CNV data result in better performance in CNV detection.

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“…The success of noise reduction is reflected using an increase in the signal-to-noise ratio (SNR) of the processed measurement relative to the original, and a number of automated 82,83 and fully automated algorithms [76][77][78] exist that could be employed to this end. Figure 5 shows a typical result from the baselineflattened and despiked demonstration dataset of live human embryonic stem cells before (Fig.…”

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confidence: 99%

Development and Integration of Block Operations for Data Invariant Automation of Digital Preprocessing and Analysis of Biological and Biomedical Raman Spectra

Schulze

Turner

2015

Appl Spectrosc

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High-throughput information extraction from large numbers of Raman spectra is becoming an increasingly taxing problem due to the proliferation of new applications enabled using advances in instrumentation. Fortunately, in many of these applications, the entire process can be automated, yielding reproducibly good results with significant time and cost savings. Information extraction consists of two stages, preprocessing and analysis. We focus here on the preprocessing stage, which typically involves several steps, such as calibration, background subtraction, baseline flattening, artifact removal, smoothing, and so on, before the resulting spectra can be further analyzed. Because the results of some of these steps can affect the performance of subsequent ones, attention must be given to the sequencing of steps, the compatibility of these sequences, and the propensity of each step to generate spectral distortions. We outline here important considerations to effect full automation of Raman spectral preprocessing: what is considered full automation; putative general principles to effect full automation; the proper sequencing of processing and analysis steps; conflicts and circularities arising from sequencing; and the need for, and approaches to, preprocessing quality control. These considerations are discussed and illustrated with biological and biomedical examples reflecting both successful and faulty preprocessing.

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Smooth functions and local extreme values

Cited by 12 publications

References 24 publications

Local linear fitting and improved estimation near peaks

Local linear fitting and improved estimation near peaks

Noise cancellation using total variation for copy number variation detection

Development and Integration of Block Operations for Data Invariant Automation of Digital Preprocessing and Analysis of Biological and Biomedical Raman Spectra

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