Comparative Study of Transcriptomic profiling and Functional enrichment in Ovarian Cancer Cell lines
Bibliographic record
Abstract
High-throughput cDNA sequencing (RNA-seq) has emerged as a sophisticated tool for transcriptomic studies, especially for identifying differentially expressed genes (DEGs) and measuring the transcripts between different sample groups or conditions. There are several pipelines and tools available for performing the task, but still there is no general consent for the protocol to be used for the analysis. In this comparative study, transcriptomic profiling of Ovarian cancer cell lines data sets were carried out by using two different pipelines- ‘Tuxedo’ protocol (Tophat, Cuflinks-Cuffdiff, CummerBund) and ‘new Tuxedo’ protocol (HISAT, StringTie, Desq2) were used for estimating the transcript abundancies and for analysing differential expression. ‘New Tuxedo’ protocol was found to be fast and efficient than ‘Tuxedo’ protocol and the run time on an 8 GB RAM PC was ~ 2 hr and ~ 6 days, respectively. A total of 613 and 371 DEGs were obtained by using ‘Tuxedo’ and ‘New Tuxedo’ pipeline, respectively. Functional profiling was performed, by a comparative study of high throughput functional enrichment tools (clueGO, DAVID, EnRichr, FunRich, gProfiler, GSEA, PANTHER and webGestalt) to get the functions and pathways of most enriched genes involved in ovarian cancer cell lines. The common biological pathways and Gene Ontology (GO) terms were extracted with common genes from all the tools to get most enriched genes with the GO functional terms. Thus, the characterization of biological pathway and GO processes (Biological processes and Molecular Function) of most enriched gene sets involved in ovarian cancer cell lines were obtained.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".