Perhaps their relative levels define clinically important subtypes of SLE, and if so they might be useful diagnostic markers for this disease characteristic. Several of the cell types that are hypothesized to be Cinoxacin particularly important in SLE are correlated with patients�� clinical measurement of disease activity. Activated dendritic cells are efficient antigen presentation machinery, and this cell type is found here to be more abundant in patients with relatively active disease, consistent with correlations between autoantibody blood titer and SLE disease activity. Other immune diseases may be amenable to the methods presented here. One challenge will be the interpretation of predictions in a disease that affects specific organs such as rheumatoid arthritis where there is less evidence of a systemic effect. Deconvolution of biopsies from these patients would have to be interpreted with caution as individual cell types are difficult to isolate from solid tissues for any validation. Moreover, biopsies would contain significant quantities of cells of unknown type. Therefore their signature genes would not be included in the basis matrix for deconvolution. Of course, expanding the arsenal of data on basis cell types would enable 4-(Benzyloxy)phenol thorough analysis of such diverse kinds of samples. Expression deconvolution has several advantages over cell sorting or other traditional methods for quantification of cell species. First, conventional methods deal with at most a handful of different cell types at one time, while expression analysis can simultaneously quantify a much greater number of cell types. Second, deconvolution integrates the partially redundant information of a large number of genes to yield its results; this redundancy 
is important because it mitigates the contribution of noise inherent in biological measurements and thus boosts robustness. Third, post-hoc analysis of expression datasets is relatively rapid, and there are a large and growing number of datasets publicly available. Novel methods of expression analysis like deconvolution have the potential to further tap this important resource for the scientific community. One concern with the application of a system of linear equations such as used here is the mathematical structure of the problem: is it well posed, what is the dimensionality and conditioning of the matrix. The dimensions of the matrix are important because a system of linear equations must be overdetermined- it must have more equations than unknowns in order to not have multiple perfect solutions, and in this case there are 360 equations and only 18 unknowns. The equations must be linearly independent- that is, each sample��s expression signature must not be the equal to the sum of other signatures when they are each multiplied by any constant- this requirement is also satisfied. Finally, the matrix must be well conditioned. That is, the solutions yielded must be relatively stable and not overly sensitive to small fluctuations in input data. This characteristic represents a satisfactory bound on the error of the solution for a given mixture, and can be thought of as the result of a basis matrix that contains genes that together form a complete but parsimonious set of robust markers for the cell types of interest. The property of conditioning is quantified as the condition number, a positive scalar value that is low when the matrix is stable. As expected, the condition number was found to be high for small or large numbers of genes and to have a minimum for moderate numbers of genes.