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ZINB-Based Graph Embedding Autoencoder for Single-Cell RNA-Seq Interpretations
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use scTAG rather than the strongest NMI baseline | cell clustering on the 16 reported datasets · 16 scRNA-seq datasets | NMI | Mixed |
| Use TAG and the ZINB decoder instead of general GCN/decoder components (ablation) | scTAG · 16 scRNA-seq datasets | NMI: 0.7827 -> 0.8252 | Helped |
| Use TAG and the ZINB decoder instead of general GCN/decoder components (ablation) | scTAG · 16 scRNA-seq datasets | ARI: 0.7372 -> 0.8138 | Helped |
| Use topology-adaptive graph convolution rather than general graph convolution (ablation) | scTAG · 16 scRNA-seq datasets | NMI: 0.8013 -> 0.8252 | Helped |
| Use topology-adaptive graph convolution rather than general graph convolution (ablation) | scTAG · 16 scRNA-seq datasets | ARI: 0.7758 -> 0.8138 | Helped |
| Use a ZINB-based decoder rather than removing the ZINB decoder (ablation) | scTAG · 16 scRNA-seq datasets | NMI: 0.8077 -> 0.8252 | Helped |
| Use a ZINB-based decoder rather than removing the ZINB decoder (ablation) | scTAG · 16 scRNA-seq datasets | ARI: 0.7993 -> 0.8138 | Helped |
| Set the KNN neighbor parameter to 15 rather than the other tested values (ablation) | scTAG cell graph construction · 16 scRNA-seq datasets | NMI and ARI | Helped |
| Select 500 highly variable genes rather than the other tested gene counts (ablation) | scTAG preprocessing · 16 scRNA-seq datasets | NMI and ARI | Helped |
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