Sampling, Amplification, and Resampling

نویسنده

  • Tianjiao Chu
چکیده

Many biological experiments for measuring the concentration levels of the gene transcripts or protein molecules involve the application of the Polymerase Chain Reaction (PCR) procedure to the gene or protein samples. To better model the results of the these experiments, we propose a new sampling scheme—sampling, amplification, and resampling (SAR)—for generating discrete data, and derive the asymptotic distribution of the SAR sample. We suggest new statistics for the test of association based on the new model, and give their asymptotic distributions. We also compare the new model with the traditional multinomial model, and show that the new model predicts a significantly larger variance for the SAR sample. This implies that, when applied to the SAR sample, the tests based on the traditional model will have a much higher type I error than expected.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Accurate sampling and deep sequencing of the HIV-1 protease gene using a Primer ID.

Viruses can create complex genetic populations within a host, and deep sequencing technologies allow extensive sampling of these populations. Limitations of these technologies, however, potentially bias this sampling, particularly when a PCR step precedes the sequencing protocol. Typically, an unknown number of templates are used in initiating the PCR amplification, and this can lead to unrecog...

متن کامل

Jackknife and Bootstrap Methods for Variance Estimation from Sample Survey Data

Re-sampling methods have long been used in survey sampling, dating back to Mahalanobis (1946). More recently, jackknife and bootstrap resampling methods have also been proposed for small area estimation; in particular for mean squared error (MSE) estimation and for constructing confidence intervals. We present a brief overview of early uses of resampling methods in survey sampling, and then pro...

متن کامل

A Resampling Technique for Relational Data Graphs

Resampling (a.k.a. bootstrapping) is a computationallyintensive statistical technique for estimating the sampling distribution of an estimator. Resampling is used in many machine learning algorithms, including ensemble methods, active learning, and feature selection. Resampling techniques generate pseudosamples from an underlying population by sampling with replacement from a single sample data...

متن کامل

Resampling Markov Chain Monte Carlo Algorithms: Basic Analysis and Empirical Comparisons

Sampling from complex distributions is an important but challenging topic in scientific and statistical computation. We synthesize three ideas, tempering, resampling, and Markov moving, and propose a general framework of resampling Markov chain Monte Carlo (MCMC). This framework not only accommodates various existing algorithms, including resample-move, importance resampling MCMC, and equi-ener...

متن کامل

Importance Sampling: A Review

We provide a short overview of Importance Sampling – a popular sampling tool used for Monte Carlo computing. We discuss its mathematical foundation and properties that determine its accuracy in Monte Carlo approximations. We review the fundamental developments in designing efficient IS for practical use. This includes parametric approximation with optimization based adaptation, sequential sampl...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2002