Integrative Analysis of scRNA-seq and ATAC-seq for Cell Fate Determination
Bibliographic record
Abstract
Single-cell RNA sequencing (scRNA-seq) and single-cell chromatin accessibility sequencing (scATAC-seq) are important technological breakthroughs in the field of life sciences in recent years, providing an unprecedented high-resolution perspective for studying the mechanism of cell fate determination. The gene expression profile of individual cells can be analyzed through scRNA-seq, revealing cellular heterogeneity and developmental trajectories. scATAC-seq can detect the chromatin open state at the single-cell level and identify potential regulatory elements and binding sites of transcription factors. The integration and analysis of scRNA-seq and scATAC-seq data can simultaneously characterize the cell state at both the transcriptional and epigenetic levels, thereby gaining an in-depth understanding of the synergistic role of transcriptional regulatory networks and chromatin dynamics in the process of cell fate determination. This study will review the principles and applications of single-cell omics technology, discuss the roles of transcription factors and chromatin accessibility in cell fate determination, and focus on introducing the key regulatory factors, cis-regulatory elements and gene regulatory networks revealed by the integrated analysis of scRNA-seq and scATAC-seq. We will also introduce methods for inferring cell fate trajectories and conducting pathway enrichment analysis using integrated data, and through cases of hematopoietic and nervous system development, illustrate how integrated analysis can reveal new insights into the process of cell differentiation. Finally, the potential clinical application value of single-cell multi-omics in areas such as tumor heterogeneity, immune cell fate, and regenerative medicine is prospected. The limitations of current technologies and analytical methods are analyzed, and the future development directions are prospected.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".