XC-ID: De novo identification of the active X chromosome in single-cell RNA-seq
Bibliographic record
Abstract
Abstract Motivation X chromosome inactivation (XCI) is an epigenetic process that equalizes X-linked gene dosage between females (XX) and males (XY). During early development, one X chromosome in each cell is randomly silenced and clonally inherited, producing a stable mosaic of two epigenetically distinct cell lineages. This mosaicism provides a natural internal control for studying cell-intrinsic regulatory differences between X lineages. However, identifying the active X chromosome in single cells remains difficult due to sparse allelic coverage, dependence on pre-phased references, and biological variability from XCI escape and skew. Results We present XC-ID ( X C hromosome inactivation ID entifier), a scalable computational framework for de novo identification of the active X chromosome from single-cell RNA-seq data. XC-ID employs a simulated-annealing algorithm to infer X-linked haplotype structure directly from allelic counts, followed by bootstrap-based confidence estimation to filter uncertain cell assignments. Applied to single-nucleus RNA-seq data from a female Mus musculus hybrid with known genotype, XC-ID achieved >99% accuracy in predicting the active X chromosome. The method remains robust to allelic noise, sequencing errors, and sparsity, and differential expression between inferred X lineages reveals biologically coherent dosage-compensation patterns. Availability XC-ID is available as a Python package with both API and command-line support at https://github.com/jlhjiang/XC-ID .
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".