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Kidney transplantation (Affymetrix set)

ABSTRACT: In this study, we examined transcriptional profiles from 3 different microarray platforms, across 103 peripheral blood samples with and without acute rejection, to find a critical gene-set for the diagnosis of acute renal rejection that matched biopsy diagnosis, irrespective of patient demographics, clinical confounders, concomitant infection, immunosuppression usage or sample processing methods. We hypothesized that changes in peripheral blood expression profiles correlate with biopsy-proven rejection, and that these changes could be used as biomarkers for the diagnosis and prediction of acute rejection. We performed cross-sectional microarray analysis of 103 matched peripheral blood samples collected at a single time point, timed with a biopsy where acute rejection was either confirmed as present (60 AR samples) or absent (62 STA samples). The samples were hybridized to one of 3 microarray platforms: Affymetrix (n=75 the 54K HG-U133_Plus2 Array), Agilent (n=26, 44K oligo Array) and cDNA array (n=21, ~30K cDNA). Of 103 samples, 14 were used on both Affy and Agilent arrays; 1 was used in cDNA and Agilent array and 2 were used across 3 array platforms. Microarray data generated from 3 array platforms were cross-compared, by mapping common and overlapping transcripts to Human Gene Organization (HUGO) gene names. Significant gene lists on each platform were identified using Significant Analysis of Microarray (SAM, ref) with a common significance threshold of a false discovery rate (FDR) of <10%.This set contains the data for samples hybridized to Affymetrix arrays. Disease State: Acute Rejection (AR#sampleid) or Stable (S#sampleid) samples

ORGANISM(S): Homo sapiens

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PROVIDER: S-DIXA-D-1021 | biostudies-other |

REPOSITORIES: biostudies-other

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Project description:BackgroundAffymetrix GeneChip microarrays are popular platforms for expression profiling in two types of studies: detection of differential expression computed by p-values of t-test and estimation of fold change between analyzed groups. There are many different preprocessing algorithms for summarizing Affymetrix data. The main goal of these methods is to remove effects of non-specific hybridization, and to optimally combine information from multiple probes annotated to the same transcript. The methods are benchmarked by comparison with reference methods, such as quantitative reverse-transcription PCR (qRT-PCR).ResultsWe present a comprehensive analysis of agreement between Affymetrix GeneChip and qRT-PCR results. We analyzed the influence of filtering by fraction Present calls introduced by J.N. McClintick and H.J. Edenberg (2006) and 2 mapping procedures: updated probe sets definitions proposed by Dai et al. (2005) and our "naive mapping" method. Because of evolution of genome sequence annotations since the time when microarrays were designed, we also studied the effect of the annotation release date. These comparisons were prepared for 6 popular preprocessing algorithms (MAS5, PLIER, RMA, GC-RMA, MBEI, and MBEImm) in the 2 above-mentioned types of studies. We used data sets from 6 independent biological experiments. As a measure of reproducibility of microarray and qRT-PCR values, we used linear and rank correlation coefficients.ConclusionsWe show that filtering by fraction Present calls increased correlations for all 6 preprocessing algorithms. We observed the difference in performance of PM-MM and PM-only methods: using MM probes increased correlations in fold change studies, but PM-only methods proved to perform better in detection of differential expression. We recommend using GC-RMA for detection of differential expression and PLIER for estimation of fold change. The use of the more recent annotation improves the results in both types of studies, encouraging re-analysis of old data.

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Kidney transplantation (Affymetrix set)

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