Poster ‐ 14: Batch Effect Reduction in in‐vitro Raman Microscopic Radiosensitivity Study Using Ovarian Cancer Cells
Bibliographic record
Abstract
Purpose: To improve classification by reducing batch effect in samples from the ovarian carcinoma cell lines A2780s (parental wild type) and A2780cp (cisplatin cross‐radio‐resistant), before, right after, and 24 hours after irradiation to 10Gy. Methods: Spectra were acquired with a home built confocal Raman microscope in 3 distinct runs of six samples: unirradiated s&cp (control pair), then 0h and 24h after irradiation. The Raman spectra were noise reduced, then background subtracted with SMIRF algorithm. ∼35 cell spectra were collected from each sample in 1024 channels from 700cm‐1 to 1618cm‐1. The spectra were analyzed by regularized multiclass LDA. For feature reduction the spectra were grouped into 3 overlapping group pairs: s‐cp, 0Gy–10Gy0h and 0Gy10–Gy24h. The three features, the three differences of the mean spectra were mapped to the analysis sub‐space by the inverse regularized covariance matrix. The batch effect noticeably confounded the dose and time effect. Results: To remove the batch effect, the 2+2=4D subspace extended by the covariance matrix of the means of the 0Gy control groups was subtracted from the spectra of each sample. Repeating the analysis on the spectra with the control group variability removed, the batch effect was dramatically reduced in the dose and time directions enabling sharp linear discrimination. The cell type classification also improved. Conclusions: We identified a efficient batch effect removal technique crucial to the applicability of Raman microscopy to radiosensitivity studies both on cell cultures and potential clinical diagnostic applications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".