Imputing single-cell RNA-seq data by graph autoencoder with multi-kernel
Bibliographic record
Abstract
Single-cell RNA-sequencing (scRNA-seq) technology has revolutionized the field by enabling the profiling of transcriptomes in cell resolution. However, it is flawed by the sparsity caused by low mRNA capture efficiency during sequencing. This results in "dropout" events where genes are expressed but not detected. Dropout can hinder downstream analyses like differential expression and clustering. To tackle this issue, we present a novel imputation approach called MKGAE, which utilizes graph convolution and autoencoder techniques to construct a generative model for imputing missing values within scRNA-seq data. Meanwhile, considering the intricate relationships between genes, merging them into a single graph might lead to the loss of important insights. To address this, we utilize two gene-to-gene graph kernels for graph convolution. Experiments across both simulated and real scRNA-seq datasets illustrate MKGAE’s superiority over other state-of-the-art methods in terms of clustering analysis and differentially expressed gene 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".