Determination of the minimum sample size in microarray experiments to cluster genes using k-means clustering
Bibliographic record
Abstract
Gene expression profiles obtained from time-series microarray experiments can reveal important information about biological processes. However, conducting such experiments is costly and time consuming. The cost and time required are linearly proportional to sample size. Therefore, it is worthwhile to provide a way to determine the minimal number of samples or trials required in a microarray experiment. One of the uses of microarray hybridization experiments is to group together genes with similar patterns of the expression using clustering techniques. In this paper, the k-means clustering technique is used. The basic idea of our approach is an incremental process in which testing, analysis and evaluation are integrated and iterated. The process is terminated when the evaluation of the results of two consecutive experiments shows they are sufficiently close. Two measures of "closeness" are proposed and two real microarray datasets are used to validate our approach. The results show that the sample size required to cluster genes in these two datasets can be reduced; i.e. the same results can be achieved with less cost. The approach can be used with other clustering techniques as well.
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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 | 0.015 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".