PLSKO:仿,omics

Guannan Yang1, Ellen Menkhorst2,3, Evdokia Dimitriadis2,3

  • 1Melbourne Integrative Genomics, School of Mathematics and Statistics, The University of Melbourne, Parkville, Victoria 3010, Australia.

PubMed
概括

部分最小方程复制 (PLSKO) 提供了强大的错误发现率 (FDR) 控制用于奥米克数据分析. 这种无假设的方法在复杂的设置中保持FDR控制和功率,优于现有的仿制发电机.

相关概念视频

Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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Randomized Experiments01:13

Randomized Experiments

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Simple randomization
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