Feature extraction and normalization algorithms for high-density oligonucleotide gene expression array data

Abstract

Algorithms for performing feature extraction and normalization on high-density oligonucleotide gene expression arrays, have not been fully explored, and the impact these algorithms have on the downstream analysis is not well understood. Advances in such low-level analysis methods are essential to increase the sensitivity and speci®city of detecting whether genes are present and/or differentially expressed. We have developed and implemented a number of algorithms for the analysis of expression array data in a software application, the DNA-Chip Analyzer (dChip). In this report, we describe the algorithms for feature extraction and normalization, and present validation data and comparison results with some of the algorithms currently in use.

Functional validation of the anaplastic lymphoma kinase signature identifies CEBPB and Bcl2A1 as critical target genes

“…Unsupervised analyses were applied to a subset of genes whose average change in expression levels varied at least 2-fold from the mean across the whole panel. Hierarchical agglomerative clustering and dendrogram generation were used to search for natural groupings in the profiles using DNA-Chip Analyzer (dChip) software (25); in order to perform the clustering of the selected probe lists, Pearson's correlation coefficient and average linkage were used as distance and linkage methods, respectively (49,50). The supervised gene expression analyses were performed using the Gene@Work software platform as previously described (49).…”

“…As controls, we used untreated cells and transduced TS cells with a mutated ALK shRNA construct (A5M). To determine whether the GEP of ALCL cell lines could identify distinct groups based on NPM-ALK expression, we performed an unsupervised analysis (25). The 21 samples generated a dendrogram with 2 major branches: one contained all control samples expressing NPM-ALK (A5 shRNA uninduced and A5M shRNA induced for 84 hours); the second branch grouped only samples in which A5 shRNA was induced (Figure 2A).…”

Changes in chondrogenic phenotype and gene expression profiles associated with the in vitro expansion of human synovium‐derived cells

“…Briefly, 1 mg of RNA, oligo-dT hexamer primer, 5 Â reaction buffer, RNase inhibitor, and 10 mM dNTP mix were mixed with M-MuLV reverse transcriptase, and reverse transcription was performed at 258C for 15 min and at 378C for 60 min, followed by a 10 min incubation at 708C to inactivate the reverse transcriptase. All reactions were determined to be in the linear amplification range for cycle numbers [25][26][27][28][29][30][31][32][33][34][35]. Gene markers focused were type I collagen gene (forward primer is CTCGAGGTGGACACCA-CCCT and reverse primer is CAGCTGCATGGCCACATCGG), type II collagen gene (GAATTCGGTGTGGACATAGG and TACAGAGGTGTTTGACACAG), type X collagen gene (AAT-CCCTGGACCGGCTGGAATTC and TTGATGCCTGGCTGT-CCTGGAACC), Sry-type high-mobility-group box transcription factor-9 (Sox-9) gene (GAACGCACATCAAGACGGAG and TCTCGTTGATTTCGCTGCTC), and aggrecan gene (TGAGG-AGGGCTGGAACAAGTACC and GGAGGTGGTAATTGCA-GGGAACA).…”

Mitochondrial DNA deletions inhibit proteasomal activity and stimulate an autophagic transcript

“…The chips were then washed, stained with avidin-labelled phycoerytherin and scanned with an Affymetrix GeneChip scanner. Raw data were processed by Microarray Suite 4.0 (Affymetrix) and microarray data files analyzed using DNA-chip Analyzer (dChip) software [41].…”