Integration of single-cell RNA-Seq and CyTOF data characterises heterogeneity of rare cell subpopulations
Bibliographic record
Abstract
Background : The simultaneous measurement of cellular proteins and transcriptomes of single cell data has become an exciting new possibility with the advent of highly multiplexed multi-omics methodologies. However, mass cytometry (CyTOF) is a well-established, affordable technique for the analysis of proteomic data, which is well suited for the discovery and characterisation of very rare subpopulations of cells with a wealth of publicly available datasets. Methods : We present and evaluate the multimodal integration of single cell RNA-Seq and CyTOF datasets coming from both matched and unmatched samples, using two publicly available datasets. Results : We demonstrate that the integration of well annotated CyTOF data with single cell RNA sequencing can aid in the identification and annotation of cell populations with high accuracy. Furthermore, we show that the integration can provide imputed measurements of protein markers which are comparable to the current gold standard of antibody derived tags (ADT) from CITE-Seq for both matched and unmatched datasets. Using this methodology, we identify and transcriptionally characterise a rare subpopulation of CD11c positive B cells in high resolution using publicly available data and we unravel its heterogeneity in a single cell setting without the need to sort the cells in advance, in a manner which had not been previously possible. Conclusions : This approach provides the framework for using available proteomic and transcriptomic datasets in a unified and unbiased fashion to assist ongoing and future studies of cellular characterisation and biomarker identification.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".