v 0.0.1 development updated content

[Starlitnightly]

For scATAC-seq/snATAC-seq:

• 1. Data Preprocess:

  • fragment.tsv convert to matrix
  • matrix to fragment.tsv (However, converting counts to fragments will result in the loss of much detailed peak information.)

• 2. Doublet Inference

• 3. Plotting Sample Statistics

  • Make a ridge plot for each sample for the TSS enrichment scores
  • Make a violin plot for each sample for the TSS enrichment scores.
  • Make a violin plot for each sample for the log10(unique nuclear fragments).
  • Fragment size distributions
  • TSS enrichment profiles

• 4. Dimensionality Reduction

  • LSI Implementation
  • Batch Effect Correction wtih Harmony

• 5. Clustering

  • Seurat’s FindClusters
  • scran

• 6. Single-cell Embeddings

  • Uniform Manifold Approximation and Projection (UMAP)
  • t-Stocastic Neighbor Embedding (t-SNE)
  • Dimensionality Reduction After Harmony
  • Highlighting specific cells on an embedding

• 7. Gene Scores and Marker Genes

  • Calculating Gene Scores
  • Identification of Marker Features
  • Identifying Marker Genes
  • Visualizing Marker Genes on an Embedding
  • Marker Genes Imputation with MAGIC
  • Module Scores
  • Track Plotting

• 8. Defining Cluster Identity with scRNA-seq

  • Cross-platform linkage of scATAC-seq cells with scRNA-seq cells
  • Adding Pseudo-scRNA-seq profiles for each scATAC-seq cell
  • Labeling scATAC-seq clusters with scRNA-seq information

• 9. Pseudo-bulk Replicates

• 10. Calling Peaks

• 11. Identifying Marker Peaks

• 12. Motif and Feature Enrichment

• 13. Motif Deviations

• 14. Footprinting

• 15. Co-accessibility

• 16. Peak2GeneLinkage

• 17. Identification of Positive TF-Regulators

• 18. Trajectory Analysis

• 19. Handling ArchR Output

• 20. Integration with bulk ATAC-seq

• 21. Multiomic data analysis

• 22. scRNA-seq generate scATAC-seq

• 23. MultiVelo