Performance Comparison of Affymetrix SNP6.0 and Cytogenetic 2.7M Whole-Genome Microarrays in Complex Cancer Samples
Bibliographic record
Abstract
The Affymetrix cytogenetic 2.7M whole-genome microarray (Cyto2.7M) detects genomic aberrations. The Cyto2.7M array has increased coverage in regions with cancer-related genes, ~4-fold reduced processing time, and 5-fold reduced input requirements (100 ng) compared to the commonly used Affymetrix SNP6.0 genome-wide microarray (SNP6.0). We set out to compare the performance of these microarrays on cancer samples containing complex genomic changes. We analyzed genomic DNA from 8 lymphoma samples and 1 blood sample using both SNP6.0 and Cyto2.7M microarrays. We compared the arrays with respect to 4 parameters, including detection of copy number variations (CNV), CNV boundaries, the actual copy number (CN) assigned to the aberrations, and loss of heterozygosity. The CN state of selected regions was validated by quantitative PCR. Very high consistency between arrays on all parameters tested was observed, hence only 30 of 224 aberrations disagreed on the CN state, corresponding to a total of ~12 Mb or 0.06% of the analyzed base pairs. Thus, the SNP6.0 and Cyto2.7M arrays are equally well suited to detect genomic aberrations in complex samples such as cancer samples. With reduced processing time and lower input requirements, the Cyto2.7M array enables genomic analysis of samples where only limited DNA is available.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".