Abstract：
:Microarray experiments are being increasingly used in molecular biology. A common task is to detect genes with differential expression across two experimental conditions, such as two different tissues or the same tissue at two time points of biological development. To take proper account of statistical variability, some statistical approaches based on the t-statistic have been proposed. In constructing the t-statistic, one needs to estimate the variance of gene expression levels. With a small number of replicated array experiments, the variance estimation can be challenging. For instance, although the sample variance is unbiased, it may have large variability, leading to a large mean squared error. For duplicated array experiments, a new approach based on simple averaging has recently been proposed in the literature. Here we consider two more general approaches based on nonparametric smoothing. Our goal is to assess the performance of each method empirically. The three methods are applied to a colon cancer data set containing 2,000 genes. Using two arrays, we compare the variance estimates obtained from the three methods. We also consider their impact on the t-statistics. Our results indicate that the three methods give variance estimates close to each other. Due to its simplicity and generality, we recommend the use of the smoothed sample variance for data with a small number of replicates.
journal_name
Funct Integr Genomicsjournal_title
Functional & integrative genomicsauthors
Huang X,Pan Wdoi
10.1007/s10142-002-0066-2keywords:
subject
Has Abstractpub_date
2002-08-01 00:00:00pages
126-33issue
3eissn
1438-793Xissn
1438-7948journal_volume
2pub_type
杂志文章abstract：:Aldo/keto reductases (AKRs) constitute a multitasking protein family that catalyzes diverse metabolic transformations including detoxification of stress generated reactive aldehydes. Yet this important protein family is poorly understood particularly in cyanobacteria, the ecologically most diverse and significant grou...
journal_title：Functional & integrative genomics
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journal_title：Functional & integrative genomics
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journal_title：Functional & integrative genomics
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journal_title：Functional & integrative genomics
pub_type： 杂志文章,评审
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journal_title：Functional & integrative genomics
pub_type： 杂志文章
doi：10.1007/s10142-013-0327-2
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pub_type： 杂志文章
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journal_title：Functional & integrative genomics
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journal_title：Functional & integrative genomics
pub_type： 杂志文章
doi：10.1007/s10142-014-0379-y
更新日期：2014-09-01 00:00:00
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journal_title：Functional & integrative genomics
pub_type： 杂志文章
doi：10.1007/s10142-011-0252-1
更新日期：2012-03-01 00:00:00
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journal_title：Functional & integrative genomics
pub_type： 杂志文章
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journal_title：Functional & integrative genomics
pub_type： 杂志文章,评审
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更新日期：2019-09-01 00:00:00
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journal_title：Functional & integrative genomics
pub_type： 杂志文章,评审
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journal_title：Functional & integrative genomics
pub_type： 杂志文章
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journal_title：Functional & integrative genomics
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pub_type： 杂志文章,评审
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journal_title：Functional & integrative genomics
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journal_title：Functional & integrative genomics
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journal_title：Functional & integrative genomics
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journal_title：Functional & integrative genomics
pub_type： 杂志文章
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更新日期：2017-07-01 00:00:00
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journal_title：Functional & integrative genomics
pub_type： 杂志文章,评审
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更新日期：2000-09-01 00:00:00
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