Rank-in: enabling integrative analysis across microarray and RNA-seq for cancer
Though transcriptomics technologies evolve rapidly in the past decades, integrative analysis of mixed data between microarray and RNA-seq remains challenging due to the inherent variability difference between them. Here, Rank-In was proposed to correct the nonbiological effects across the two technologies, enabling freely blended data for consolidated analysis. Rank-In was rigorously validated via the public cell and tissue samples tested by both technologies. On the two reference samples of the SEQC project, Rank-In not only perfectly classified the 44 profiles but also achieved the best accuracy of 0.9 on predicting TaqMan-validated DEGs. More importantly, on 327 Glioblastoma (GBM) profiles and 248, 523 heterogeneous colon cancer profiles respectively, only Rank-In can successfully discriminate every single cancer profile from normal controls, while the others cannot. Further on different sizes of mixed seq-array GBM profiles, Rank-In can robustly reproduce a median range of DEG overlapping from 0.74 to 0.83 among top genes, whereas the others never exceed 0.72. Being the first effective method enabling mixed data of cross-technology analysis, Rank-In welcomes hybrid of array and seq profiles for integrative study on large/small, paired/unpaired and balanced/imbalanced samples, opening possibility to reduce sampling space of clinical cancer patients. Rank-In can be accessed at http://www.badd-cao.net/rank-in/index.html.
细胞类型和基因表达去卷积 BayesPrism 使贝叶斯整体分析和单细胞 RNA 测序在肿瘤学中得以实现
Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology | Nature Cancer
利用大规模 RNA 测序(RNA-seq)数据集推断单细胞组成及其对全局基因表达变化的贡献是肿瘤学研究的一个重要课题。在这里，我们发展推断贝叶斯细胞比例重建使用统计边缘化(BayesPrism) ，一种贝叶斯方法来预测细胞组成和基因表达的个别细胞类型的批量 RNA-seq，使用患者衍生，scRNA-seq 作为先验信息。我们对原发性胶质母细胞瘤、头颈部鳞状细胞癌和皮肤黑色素瘤进行综合分析，探讨细胞类型组成与各肿瘤类型的临床结果之间的关系，并探讨恶性和非恶性细胞状态的空间异质性。我们在排除了混杂的非恶性细胞之后，使用基因表达注释细化了当前的癌症亚型。最后，我们确定了恶性细胞中表达与巨噬细胞浸润、 t 细胞、成纤维细胞和内皮细胞跨多种肿瘤类型相关的基因。我们的工作介绍了一种新的透镜，以准确推断细胞的组成和表达在大批量的 RNA-seq 数据。
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