Normalization is the first critical step in microbiome sequencing data analysis used to account for variable library sizes. Current RNA-Seq based normalization methods that have been adapted for microbiome data fail to consider the unique characteristics of microbiome data, which contain a vast number of zeros due to the physical absence or under-sampling of the microbes. Normalization methods ...

We study statistical inferences for a class of modulated stationary processes with time-dependent variances. Due to non-stationarity and the large number of unknown parameters, existing methods for stationary or locally stationary time series are not applicable. Based on a self-normalization technique, we address several inference problems, including self-normalized central limit theorem, self-...

This paper presents some experiments with feature and score normalization for text-independent speaker verification of cellular data. The speaker verification system is based on cepstral features and Gaussian mixture models with 1024 components. The following methods, which have been proposed for feature and score normalization, are reviewed and evaluated on cellular data: cepstral mean subtrac...

SARTools is a R package associated with two R script templates which allow one to perform differential analysis with either DESeq2 [1] or edgeR [2], the normalization method employed being the one associated with the package used. The purpose of this additional file is to show that normalization methods are not interchangeable between statistical models without adequate transformation. Despite ...

A new front-end processing scheme for robust speech recognition is proposed and evaluated on the multi-lingual Aurora 3 database. The front-end processing scheme consists of Mel-scaled spectral subtraction, speech segmentation, cepstral coefficient extraction, utterance-level frame dropping and eigenspace feature normalization. We also investigated performance on all language databases by post-...

Preclinical PET studies of β-amyloid (Aβ) accumulation are of growing importance, but comparisons between research sites require standardized and optimized methods for quantitation. Therefore, we aimed to evaluate systematically the (1) impact of an automated algorithm for spatial brain normalization, and (2) intensity scaling methods of different reference regions for Aβ-PET in a large dataset...

Although spectral mixture analysis has been widely used for mapping the abundances of physical components of urban surface with moderate spatial resolution satellite imagery recently, the spectral heterogeneity of urban land surface has still posed a great challenge to accurately estimate fractions of surface materials within a pixel. How to dealing with the highly spectral heterogeneous nature...

URL normalization is a process of transforming URL strings into canonical form. Through this process, duplicate URL representations for web pages can be reduced significantly. There are a number of normalization methods. In this paper, we describe four metrics for evaluating normalization methods. The reliability and consistency of a URL is also considered in our evaluation. With the metrics pr...

This paper addresses the problem of channel effect in the line spectrum frequency (LSF) domain. The channel effect can be expressed in terms of the channel phase. The speech signal is represented by its inverse filter derived from LP analysis. Then the mean normalization on the inverse filters is introduced for removing the channel distortion. Further study indicates that the mean normalization...

Affymetrix gene expression microarray is a popularly used platform for differential analysis. The analysis pipeline includes five steps: background correction, normalization, PM-only correction, and summarization, and differential analysis. Using publicly available microarray data, we compared the performance of five summarization methods: Median, Mean, Median Polish, Robust Linear Model, Li-Wo...