Whole blood gene expression analysis of stable and acute rejection pediatric kidney transplant patients. Homo sapiens

Full title: Expression data from whole blood gene expression analysis of stable and acute rejection pediatric kidney transplant patients Tissues are often made up of multiple cell-types. Blood, for example, contains many different cell-types, each with its own functional attributes and molecular signature. In humans, because of its accessibility and immune functionality, blood cells have been used as a source for RNA-based biomarkers for many diseases. Yet, the proportions of any given cell-type in the blood can vary markedly, even between normal individuals. This results in a significant loss of sensitivity in gene expression studies of blood cells and great difficulty in identifying the cellular source of any perturbations. Ideally, one would like to perform differential expression analysis between patient groups for each of the cell-types within a tissue but this is impractical and prohibitively expensive. This dataset is the validation dataset used to test the csSAM gene expression deconvolution algorithm as reported in the accompanying paper. Overall design: Whole blood gene expression measurements for 24 pediatric renal transplant patients were analyzed on human specific HGU133V2.0 (+) whole genome expression arrays. Blood drawn using PaxGene Blood RNA Tubes (PreAnalytiX, Qiagen).

完整标题：稳定期与急性排斥反应儿科肾移植患者全血基因表达分析所得的表达谱数据
组织通常由多种细胞类型构成。以血液为例，其包含诸多不同细胞类型，各类细胞均具备独特的功能属性与分子特征。
对于人类而言，血液细胞因其易于获取且具备免疫功能，已被广泛用作多种疾病的RNA类生物标志物来源。
然而，即便在健康个体之间，血液中任意特定细胞类型的占比也可能存在显著差异。这会导致血细胞基因表达研究的灵敏度大幅下降，同时极大增加了识别基因表达扰动细胞来源的难度。
理想情况下，我们希望针对组织内每种细胞类型分别在患者组间开展差异表达分析，但该操作既不切实际，成本也高得令人望而却步。
本数据集即为用于验证csSAM基因表达反卷积算法的验证集，正如伴随发表的论文中所述。
实验整体设计：对24名儿科肾移植患者的全血基因表达量进行了检测，检测平台为人类专属HGU133V2.0（+）全基因组表达芯片。血液样本采用PaxGene血液RNA采血管（PreAnalytiX, Qiagen）采集。