Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, New York 10016, United States.
Division of Translational Medicine, Department of Medicine, New York University Grossman School of Medicine, New York, New York 10016, United States.
J Proteome Res. 2021 Jul 2;20(7):3767-3773. doi: 10.1021/acs.jproteome.1c00190. Epub 2021 Jun 24.
Unbiased assays such as shotgun proteomics and RNA-seq provide high-resolution molecular characterization of tumors. These assays measure molecules with highly varied distributions, making interpretation and hypothesis testing challenging. Samples with the most extreme measurements for a molecule can reveal the most interesting biological insights yet are often excluded from analysis. Furthermore, rare disease subtypes are, by definition, underrepresented in cancer cohorts. To provide a strategy for identifying molecules aberrantly enriched in small sample cohorts, we present BlackSheep, a package for nonparametric description and differential analysis of genome-wide data, available from Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/blacksheepr.html) and Bioconda (https://bioconda.github.io/recipes/blksheep/README.html). BlackSheep is a complementary tool to other differential expression analysis methods, which is particularly useful when analyzing small subgroups in a larger cohort.
无偏分析方法，如鸟枪法蛋白质组学和 RNA 测序，可对肿瘤进行高分辨率的分子特征分析。这些分析方法可以测量具有高度变化分布的分子，这使得解释和假设检验具有挑战性。对于一个分子来说，具有最极端测量值的样本可以揭示最有趣的生物学见解，但通常被排除在分析之外。此外，罕见疾病亚型的定义是癌症队列中代表性不足的。为了提供一种方法来识别在小样本队列中异常富集的分子，我们提出了 BlackSheep，这是一个用于全基因组数据的非参数描述和差异分析的软件包，可从 Bioconductor（https://www.bioconductor.org/packages/release/bioc/html/blacksheepr.html）和 Bioconda（https://bioconda.github.io/recipes/blksheep/README.html）获得。BlackSheep 是其他差异表达分析方法的补充工具，当在较大的队列中分析较小的亚组时，它特别有用。
