Unsupervised Identification of SARS-CoV-2 Target Cell Groups via Nonlinear Dimensionality Reduction on Single-cell RNA-Seq Data
Bibliographic record
Abstract
Recent emergence of a new coronavirus, SARS-CoV2, has caused the disease COVID-19 and has been declared a worldwide pandemic. Identification of relevant modules such as target cells is a significant step for characterizing diseases and consequently leads to better diagnosis, treatment and prognosis. High-throughput single-cell RNA-Seq (scRNA-seq) technologies have advanced in recent years, enabling researchers to investigate cells individually and understand their biological mechanisms. Computational techniques such as data clustering, which are categorized via unsupervised learning methods, are the more suitable for the pre-processing step in scRNA-seq data analysis. They can be used to identify a group of genes that belong to a specific cell type based on similar gene expression patterns. However, due to the sparsity and high-dimensional nature of this type of data, classical clustering methods are not efficient. Therefore, the use of nonlinear dimensionality reduction techniques to improve clustering results is crucial. In this work, we aim to find representative clusters of SARS-CoV-2 target cell lung by combining dimensionality reduction and clustering techniques. We first perform upstream analysis on data, including normalization and filtering using quality control metrics. We then assess the impact of different dimensionality reduction techniques on the clustering results. Our results show that modified Locally Linear Embedding combined with Independent Component Analysis have a very positive impact on clustering large-scale COVID19 scRNA-seq data. To validate our findings, we identified target cell types involved in immune system functionality and a list of overlapping marker genes among COVID-19, Influenza A and HSV-1 infection.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".