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We purified naïve, effector memory and central memory T cells from peripheral blood mononuclear cells (PBMC) and compared the quantification of the T cell subsets by expression microarray deconvolution to their levels determined by cell sorting (FACS).
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To this end, cytology together with expression microarray data deconvolution seem more likely to succeed [ 54, 55].
These results varied from the FACS data by a mean of 1.3%, illustrating that deconvolution of expression microarray data accurately determines the levels of memory and naïve subsets of T lymphocytes in blood samples.
All chip results were analyzed by SBC analysis system (SAS), an expression microarray data analysis software of this corporation.
Gene expression microarray profiling was performed by UCL Genomics.
SERPINE1, identified from expression microarray of primary tumours as a potential biomarker for ECS, was validated through mRNA expression, and by immunohistochemistry (IHC) on a tissue microarray from the same cohort.
(a) Genome-wide expression microarray analysis of PSC27.
In Statistical analysis of gene expression microarray data.
Recursive partitioning for tumor classification with gene expression microarray data.
There are several limitations of microarray deconvolution that bear discussion.
These data include expression of genes in different activation and differentiation states that represent a spectrum of cell species present in blood, providing a basis set for microarray deconvolution of blood samples.
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