ArrayPipe: a flexible processing pipeline for microarray data
Bibliographic record
Abstract
A number of microarray analysis software packages exist already; however, none combines the user-friendly features of a web-based interface with potential ability to analyse multiple arrays at once using flexible analysis steps. The ArrayPipe web server (freely available at www.pathogenomics.ca/arraypipe) allows the automated application of complex analyses to microarray data which can range from single slides to large data sets including replicates and dye-swaps. It handles output from most commonly used quantification software packages for dual-labelled arrays. Application features range from quality assessment of slides through various data visualizations to multi-step analyses including normalization, detection of differentially expressed genes, andcomparison and highlighting of gene lists. A highly customizable action set-up facilitates unrestricted arrangement of functions, which can be stored as action profiles. A unique combination of web-based and command-line functionality enables comfortable configuration of processes that can be repeatedly applied to large data sets in high throughput. The output consists of reports formatted as standard web pages and tab-delimited lists of calculated values that can be inserted into other analysis programs. Additional features, such as web-based spreadsheet functionality, auto-parallelization and password protection make this a powerful tool in microarray research for individuals and large groups alike.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".