Cell subset prediction for blood genomic studies.
Cell subset prediction for blood genomic studies.
复制标题
血液基因组研究的细胞子集预测。
DOI：
10.1186/1471-2105-12-258
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发表时间：
2011-06-24
影响因子：
3
通讯作者：
Kleinstein SH
中科院分区：
文献类型：
--
作者：
Bolen CR;Uduman M;Kleinstein SH
患者血液样本的全基因组转录图谱为研究潜在的疾病机制和个性化的治疗决策提供了一个强大的工具。大多数研究是基于对混合人群外周血单个核细胞(PBMC)总数的分析。在这种情况下，准确性固有地受到限制，因为来自其他细胞的RNA将稀释特定于细胞亚群的基因特征的差异表达。虽然使用特定的外周血单核细胞亚群进行转录图谱分析将提高我们从这些数据中提取知识的能力，但很少有人明显地看到哪个细胞亚群(S)将是最具信息量的。我们开发了一种计算方法(根据浓缩相关进行子集预测，SPEC)，仅使用来自全部PBMC的数据来预测预定义的基因列表(即基因签名)的细胞来源。SPEC不依赖于签名中细胞亚集特定基因的出现，而是利用与一组样本中特定亚集基因的相关性。使用多个实验数据集的验证表明，SPEC可以准确地识别基因签名的来源是髓系还是淋巴系，以及区分B细胞、T细胞、NK细胞和单核细胞。使用SPEC，我们预测，髓系细胞是干扰素治疗反应基因签名的来源，与对标准治疗无反应的丙型肝炎患者相关。SPEC是一种强大的血液基因组研究技术。它可以帮助识别对了解疾病和治疗反应至关重要的特定细胞亚群。SPEC被广泛应用，因为只需要从全部PBMC中提取基因表达谱，因此它可以很容易地用于挖掘大量现有的微阵列或RNA-SEQ数据。
Genome-wide transcriptional profiling of patient blood samples offers a powerful tool to investigate underlying disease mechanisms and personalized treatment decisions. Most studies are based on analysis of total peripheral blood mononuclear cells (PBMCs), a mixed population. In this case, accuracy is inherently limited since cell subset-specific differential expression of gene signatures will be diluted by RNA from other cells. While using specific PBMC subsets for transcriptional profiling would improve our ability to extract knowledge from these data, it is rarely obvious which cell subset(s) will be the most informative. We have developed a computational method (Subset Prediction from Enrichment Correlation, SPEC) to predict the cellular source for a pre-defined list of genes (i.e. a gene signature) using only data from total PBMCs. SPEC does not rely on the occurrence of cell subset-specific genes in the signature, but rather takes advantage of correlations with subset-specific genes across a set of samples. Validation using multiple experimental datasets demonstrates that SPEC can accurately identify the source of a gene signature as myeloid or lymphoid, as well as differentiate between B cells, T cells, NK cells and monocytes. Using SPEC, we predict that myeloid cells are the source of the interferon-therapy response gene signature associated with HCV patients who are non-responsive to standard therapy. SPEC is a powerful technique for blood genomic studies. It can help identify specific cell subsets that are important for understanding disease and therapy response. SPEC is widely applicable since only gene expression profiles from total PBMCs are required, and thus it can easily be used to mine the massive amount of existing microarray or RNA-seq data.
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影响因子：
64.8
作者：
通讯作者：
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影响因子：
12.3
作者：
Gentleman RC;Carey VJ;Bates DM;Bolstad B;Dettling M;Dudoit S;Ellis B;Gautier L;Ge Y;Gentry J;Hornik K;Hothorn T;Huber W;Iacus S;Irizarry R;Leisch F;Li C;Maechler M;Rossini AJ;Sawitzki G;Smith C;Smyth G;Tierney L;Yang JY;Zhang J
通讯作者：
Zhang J
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20.3
作者：
Ramilo, Octavio;Allman, Windy;Chaussabel, Damien
通讯作者：
Chaussabel, Damien
影响因子：
3.6
作者：
Radom-Aizik S;Zaldivar F Jr;Leu SY;Cooper DM
通讯作者：
Cooper DM
影响因子：
4.4
作者：
Palmer, Chana;Diehn, Maximilian;Alizadeh, Ash A;Brown, Patrick O
通讯作者：
Brown, Patrick O