DIC: Deep Imputing and Clustering Single Cell RNA Sequencing Data
Bibliographic record
Abstract
It is notorious that single-cell RNA sequencing (scRNA-seq) data contain a significant number of missing values due to technical variability. The issue of missing values presents a major challenge in scRNA-seq analysis, especially, complicating the identification of cell types via clustering. To address this issue, various methods have been developed to impute the missing data in scRNA-seq clustering. Most methods first impute missing expression values and then cluster scRNA-seq data. However, these approaches often fail to fully exploit the biologically meaningful cluster structures while imputing missing values. In this study, we propose DIC, a deep neural network with the Y-structure that collaboratively imputes and clusters scRNA-seq data. The Y-structure of DIC is formed by an autoencoder with an extra branch attached to its code layer. Therefore, DIC is divided into three modules: a base module (encoder), an imputation module (decoder) and a clustering module (extra branch). The imputation module and the clustering module work together to perform missing data imputation and cell clustering using deeply learned features from the base module. During the model training process, the cluster structure information is used for missing data imputation while the imputation module enhances the clustering performance by generating more accurately recovered missing data. Our experimental results illustrate that DIC is effective in both imputing missing data and identifying cell types.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".