scATAC seq (Single Cell- Assay for Transposase-Accessible Chromatin) Analysis

This project focused on analyzing single-cell chromatin accessibility data from human brain cortex cells, including progenitors and differentiated cells, using the ArchR package in R.

Key Tasks:

1. Preprocessing and Quality Control:

   • The data was downloaded and filtered based on the number of fragments and TSS enrichment. Doublets were identified and removed.
   • Various quality control measures were applied, including fragment length distribution, TSS enrichment, and the number of fragments per sample.

2. Dimensionality Reduction:

   • Dimensionality reduction was performed using ArchR’s iterative latent semantic indexing (LSI) method.
   • UMAP plots were generated to visualize the data, with cells colored by sample, TSS enrichment, and fragment number.
   • Batch effects were identified and corrected, and the impact of batch correction was visualized.

3. Clustering:

   • Louvain clustering was applied to all cells, and the resulting clusters were visualized using UMAP. The sample proportions within each cluster were also analyzed.

4. Peaks:

   • A joint peak set was computed for the dataset, and cluster-specific marker peaks were identified.
   • Heatmaps were created to show accessibility in these marker peaks, and specific genes such as TOP2A, MKI67, AURKA, SATB2, and VGLUT1 were analyzed.

5. Gene Activity:

   • Gene activity scores were computed using chromatin accessibility, and cluster marker genes were identified based on specific parameters.
   • MAGIC was applied to visualize the first five marker genes with and without smoothing.

6. Transcription Factor (TF) Motif Activity:

   • TF motif activity was computed using an appropriate annotation database.
   • UMAP embeddings were generated for the top TF motifs, and their activity across different clusters was visualized.

7. Integration with Gene Expression:

   • The scRNA-seq data was integrated with scATAC-seq data to link gene expression with chromatin accessibility. UMAP plots were generated to visualize this integration for specific marker genes.
   • Correlation coefficients between gene expression and activity were computed.

8. Peak-Gene Linkage:

   • Peak-gene linkage was computed using both gene expression and chromatin accessibility data.
   • Heatmaps were created to visualize the linkage across all cell types.

9. Differential Accessibility:

   • Differential peak accessibility between GluN5 and Cyc. Prog. cells was computed, and MA and volcano plots were created.
   • TF motif enrichment was analyzed for differentially accessible peaks.

10. TF Footprinting:

• TF footprints were obtained for the top three motifs from the GluN5 and Cyclic progenitor cells.
• Normalization for Tn5 bias was performed, and aggregate footprints were visualized and interpreted.

11. Co-accessibility:

• Co-accessibility of peaks was computed and visualized using genome track plots.
• Potential enhancers for marker genes were identified by analyzing peak linkage to gene TSS.

R Script:

The R script used for analysis is named scATAC_seq_script.

Results:

The resulting plots and images can be found in the plots directory , and the Quality Control plot generated while creating the Arrow file can be found the QualityControl directory of this repository.

ArchR Documentation:

For further information on the ArchR package, please refer to the official ArchR documentation.