For relative risk studies, data analyzed were limited to untreated cohorts and did not address toxicity, viral resistance and cost associated with ART. We nevertheless believe that the findings are important for clinical decision making, because the prior question that physicians face is their patients�� prognosis if treatment is not initiated. Furthermore, despite our attempts to include only high-quality studies and to focus on standardized outcomes with known covariates, our pooled analyses are not meta-analyses in the strict sense. Notably, included studies varied in rates of loss to follow-up, extent of exposure to antiretroviral mono- or bi-therapy, OI prophylaxis and treatment, and patient inclusion criteria and age ranges; however, available data and statistical power precluded optimal assessment of these possible determinants. The capability of gene expression microarrays to simultaneously measure essentially all human genes has made possible a variety of approaches to analyzing biological samples. A simple approach is to measure the statistical significance of differentially Chlorhexidine hydrochloride expressed genes between two groups of samples studied. This supervised analysis presumes that any meaningful differences are between the predetermined groups of samples. An unsupervised analysis uses no prior knowledge about how the samples are related. As an example, global hierarchical clustering was used to discover the interferon signature in the blood of some but not all SLE patients. Closer integration of biological knowledge of genes with the analysis of expression data can enable more detailed examination of the patient samples. Gene Set Enrichment Analysis is a knowledge-based method to identify genes differentially expressed that share common biological functions or are in the same biochemical pathways. This type of analysis with sets of genes that are specifically expressed indifferent immune cell subsets can be used to identify the presence of these subsets in disease blood or tissue. However, the results are only qualitative, and systematic analysis of relative proportions or activation states of these subsets is not possible by this method. The deconvolution based on synchronized populations of yeast cells at specific points of the cell cycle predicted the phases occupied by different cell cycle mutants. In another application, Wang et al. analyzed mouse mammary tissue and used the residuals of their fit to Atropine sulfate separate the differential expression due to changes in tissue composition from those due to intrinsic gene regulation. In both these studies expression signatures of homogeneous samples of cells enabled the interpretation of the cellular composition of a complex tissue. A biological sample from a patient with an autoimmune disease typically contains various different immune cell subsets, and the process of microarray deconvolution can quantify their relative proportions. Essentially, the expression of each gene in the sample is modeled as a linear combination of the expression of that gene in each of the cells comprising that sample. If the expression signature of each immune cell subset is known, then the fractions of each subset in the sample can be determined by solving a linear equation to best fit the fractions of cell subsets to the whole sample��s expression signature. This first step of experimentally determining the signatures of the constituent parts is critical because it defines the framework of the results of deconvolution. The different cell types present in blood can be purified in order to construct expression signatures.