Integrative Analysis of scRNA-seq and ATAC-seq for Cell Fate Determination
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.000 | 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 it