Faster cyclic loess: normalizing RNA arrays via linear models
نویسندگان
چکیده
منابع مشابه
Faster cyclic loess: normalizing RNA arrays via linear models
MOTIVATION Our goal was to develop a normalization technique that yields results similar to cyclic loess normalization and with speed comparable to quantile normalization. RESULTS Fastlo yields normalized values similar to cyclic loess and quantile normalization and is fast; it is at least an order of magnitude faster than cyclic loess and approaches the speed of quantile normalization. Furth...
متن کاملNormalizing Empirically Underidentified Linear State-Space Models
Normalizing latent-variable models in empirical work has sometimes more influence on statistical inference than commonly appreciated. In this paper, I show how relating non-identification to an invariance property of the likelihood function under certain groups of parameter transformations helps understanding the influence of normalization on inference and can guide the choice of identifying re...
متن کاملWhen and why are log-linear models self-normalizing?
Several techniques have recently been proposed for training “self-normalized” discriminative models. These attempt to find parameter settings for which unnormalized model scores approximate the true label probability. However, the theoretical properties of such techniques (and of self-normalization generally) have not been investigated. This paper examines the conditions under which we can expe...
متن کاملPeriodicity and Cyclic Shifts via Linear Sketches
We consider the problem of identifying periodic trends in data streams. We say a signal a ∈ R is p-periodic if ai = ai+p for all i ∈ [n− p]. Recently, Ergün et al. [4] presented a one-pass, O(polylogn)space algorithm for identifying the smallest period of a signal. Their algorithm required a to be presented in the time-series model, i.e., ai is the ith element in the stream. We present a more g...
متن کاملLarge Scale Constrained Linear Regression Revisited: Faster Algorithms via Preconditioning
In this paper, we revisit the large-scale constrained linear regression problem and propose faster methods based on some recent developments in sketching and optimization. Our algorithms combine (accelerated) mini-batch SGD with a new method called two-step preconditioning to achieve an approximate solution with a time complexity lower than that of the state-of-the-art techniques for the low pr...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Bioinformatics
سال: 2004
ISSN: 1367-4803,1460-2059
DOI: 10.1093/bioinformatics/bth327