Large-scale labeling and assessment of sex bias in publicly available expression data.
Large-scale labeling and assessment of sex bias in publicly available expression data.
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DOI：
10.1186/s12859-021-04070-2
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发表时间：
2021-03-30
影响因子：
3
通讯作者：
Altman RB
中科院分区：
文献类型：
--
作者：
Flynn E;Chang A;Altman RB
女性发生临床相关药物不良事件的风险高出1.5倍以上。虽然这种较高的流行率部分是由于与性别有关的影响，但生物性别差异也可能影响药物反应。公开可用的基因表达数据库为在细胞水平上检查药物反应提供了独特的机会。然而，元数据的缺失和异质性阻碍了药物暴露研究的大规模识别，并限制了性别偏倚的评估。为了解决这个问题，我们训练了生物体特定的模型来从基因表达数据中推断样本性别，并使用实体归一化来将元数据细胞系和药物提及映射到现有的本体。使用这种方法，我们推断了来自refin.bio的450，371个人类和245，107个小鼠微阵列和RNA-seq样本的性别标签。总体而言，我们发现人类样本中存在轻微的雌性偏倚（52.1%），小鼠样本中存在轻微的雄性偏倚（62.5%）;这与大多数人类混合性别研究和小鼠单性别研究相对应，分为仅雌性和仅雄性（分别为25.8% vs. 18.9%和21.6% vs. 31.1%）。在药物研究中，我们发现总体上性别采样偏倚的证据有限;然而，特定类别的药物，包括人类癌症和小鼠神经系统药物，分别在仅限女性和仅限男性的研究中得到了丰富。我们利用基于表达的性别标签来进一步检查细胞系性别的复杂性，并评估元数据性别标签错误注释的频率（2-5%）。我们的研究结果表明，有限的整体性别偏见，同时突出了特定子领域的高偏见，并强调了包括性别标签，以更好地了解潜在的生物学的重要性。我们将推断和规范化的标签以及错误注释样本的标志沿着公开，以促进在未来的分析中常规使用性别作为研究变量。在线版本包含补充材料，可通过10.1186/s12859-021-04070-2获得。
Women are at more than 1.5-fold higher risk for clinically relevant adverse drug events. While this higher prevalence is partially due to gender-related effects, biological sex differences likely also impact drug response. Publicly available gene expression databases provide a unique opportunity for examining drug response at a cellular level. However, missingness and heterogeneity of metadata prevent large-scale identification of drug exposure studies and limit assessments of sex bias. To address this, we trained organism-specific models to infer sample sex from gene expression data, and used entity normalization to map metadata cell line and drug mentions to existing ontologies. Using this method, we inferred sex labels for 450,371 human and 245,107 mouse microarray and RNA-seq samples from refine.bio. Overall, we find slight female bias (52.1%) in human samples and (62.5%) male bias in mouse samples; this corresponds to a majority of mixed sex studies in humans and single sex studies in mice, split between female-only and male-only (25.8% vs. 18.9% in human and 21.6% vs. 31.1% in mouse, respectively). In drug studies, we find limited evidence for sex-sampling bias overall; however, specific categories of drugs, including human cancer and mouse nervous system drugs, are enriched in female-only and male-only studies, respectively. We leverage our expression-based sex labels to further examine the complexity of cell line sex and assess the frequency of metadata sex label misannotations (2–5%). Our results demonstrate limited overall sex bias, while highlighting high bias in specific subfields and underscoring the importance of including sex labels to better understand the underlying biology. We make our inferred and normalized labels, along with flags for misannotated samples, publicly available to catalyze the routine use of sex as a study variable in future analyses. The online version contains supplementary material available at 10.1186/s12859-021-04070-2.
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影响因子：
6.5
作者：
Kong, Betty Y.;Haugh, Isabel M.;Paller, Amy S.
通讯作者：
Paller, Amy S.
影响因子：
5.4
作者：
Gershoni M;Pietrokovski S
通讯作者：
Pietrokovski S
影响因子：
4.6
作者：
Fasterius E;Al-Khalili Szigyarto C
通讯作者：
Al-Khalili Szigyarto C
影响因子：
8.2
作者：
Beery AK;Zucker I
通讯作者：
Zucker I
GCTX格式和CMAP {PY，R，M，J}软件包：用于优化注释密集矩阵的优化存储和集成遍历的资源。
DOI：
10.1093/bioinformatics/bty784
发表时间：
2019-04-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Enache OM;Lahr DL;Natoli TE;Litichevskiy L;Wadden D;Flynn C;Gould J;Asiedu JK;Narayan R;Subramanian A
通讯作者：
Subramanian A