5/18/2023 0 Comments Microarray probe to gene r taply![]() MM probes were designed to capture array background noise (rawQ) and non-specific binding (NSB) of off-target transcripts. The original Sscore relies on the difference between perfect match (PM) and cognate mismatch (MM) probes to correct for non-specific hybridization while calculating a measure of expression difference between two samples. This algorithm has been utilized in publications across multiple laboratories for studies based on 3′ IVT array technology. This advantage prompted development of a Bioconductor R package, sscore, for application of the original Sscore method on Affymetrix microarrays. Prior work demonstrated the advantage of the Sscore method over probe summarization techniques such as RMA for Affymetrix microarray analysis, particularly for experiments having smaller numbers of replicates. The Sscore method provided an easily interpretable standard normal distribution of expression differences between two samples for a given probeset, akin to a z-score transformation. This entailed comparing individual oligonucleotide probes within each probeset between two samples, after applying a heteroscedastic error correction model. ![]() Since expression “differences” rather than absolute expression levels are generally the goal in microarray studies, our laboratory previously developed the Sscore algorithm for analysis of Affymetrix oligonucleotide microarrays for detecting significant expression changes between paired samples. Popular analysis methods for oligonucleotide arrays, such as the Robust Multiarray Analysis (RMA) method, produce expression values for given genes/transcripts/exons by summarizing hybridization intensities across all corresponding oligonucleotides. A major commercial platform for microarray analysis, produced originally by Affymetrix and now by Thermo-Fisher, utilizes collections of oligonucleotides distributed across cognate genes to probe RNA expression by hybridization. The GCSscore package represents a powerful new application for analysis of the newest generation of oligonucleotide microarrays such as the ClariomS and ClariomD/XTA arrays produced by Affymetrix/Fisher.ĭespite the advent of RNAseq for transcriptomics analysis, microarrays continue to be widely used with an average of over 7000 PubMed listings per year in 2015–2019 for this technology. This was particularly striking for analysis of the highly complex ClariomD/XTA based arrays. However, GCSscore functioned well for both the ClariomS and ClariomD/XTA newer microarrays and outperformed existing analysis approaches insofar as the number of DEGs and cognate biological functions identified. We demonstrate that for older 3′-IVT arrays, GCSscore produced very similar differential gene expression analysis results compared to the original Sscore method. By utilizing the direct probe-level intensities, the GCSscore algorithm was able to detect DEGs under stringent statistical criteria for all Clariom-based arrays. G CSscore has multiple methods for grouping individual probes on the ClariomD/XTA chips, providing the user with differential expression analysis at the gene-level and the exon-level. We implemented this algorithm in an improved GCSscore R package for analysis of modern oligonucleotide microarrays. Here we describe the GCSscore algorithm, which utilizes the GC-content of a given oligonucleotide probe to estimate non-specific binding using antigenomic background probes found on new generations of arrays. However, the prior version of the Sscore algorithm and a R-based implementation software, sscore, do not function with the latest generations of Affymetrix/Fisher microarrays due to changes in microarray design that eliminated probes previously used for estimation of non-specific binding. The Sscore algorithm was validated for sensitive detection of DEGs by comparison with existing methods. We previously developed a methodology, the Sscore algorithm, for probe-level identification of differentially expressed genes (DEGs) between treatment and control samples with oligonucleotide microarrays. However, existing methods of analysis for these high-density arrays do not leverage the statistical power contained in having multiple oligonucleotides representing each gene/exon, but rather summarize probes into a single expression value. The latest generation of Affymetrix/Thermo-Fisher microarrays, the ClariomD/XTA and ClariomS array, provide a sensitive and facile method for complex transcriptome expression analysis. Despite the increasing use of RNAseq for transcriptome analysis, microarrays remain a widely-used methodology for genomic studies.
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