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Integrated Multi-Omics Analysis

This repository contains notebooks for integrating and analyzing multi-omics data, specifically focusing on RNA-seq, ATAC-seq, Perturb-CITE-Seq, and spatial transcriptomics datasets. The integration is performed using advanced computational methods to provide a comprehensive view of gene expression, chromatin accessibility, and spatial localization. The key steps and methods used across the notebooks are:

  • Melanoma Study using Perturb-CITE-Seq

    • A combined therapeutic approach targeting COL1A1 and EIF4A for melanoma treatment.
    • A multi-targeted strategy for aggressive melanoma, combining PRAME-targeted immunotherapy with PARP inhibitors and EMT/stemness pathway inhibitors.
    • The complex interplay between CD274 (PD-L1), CD58, and CD9 in different immune microenvironments.
    • The potential role of LY96 in immune evasion across multiple cancer types, including melanoma.
  • CRISPR Pooled imaging Screen Analysis

    • Over 40 custom geometrical features

    • Vision transformers (DINO) for image feature extraction

    • CellProfiler features

      Used these methods to map and cluster genes based on their roles in:

      • Protein Synthesis and Quality Control
      • Cytoskeleton and Cell Structure
      • Intracellular Transport and Organelle Function
  • RNA-ATAC Integration with scVI

    • Integrated RNA-seq and ATAC-seq datasets using scVI to analyze gene expression and chromatin accessibility data.
    • Leveraged publicly available datasets for a comprehensive analysis.
  • RNA-ATAC Integration with FGW and Random Forest

    • Utilized the Fused Gromov-Wasserstein (FGW) algorithm to align RNA-seq and ATAC-seq data.
    • Quantified the integration using FOSCTTM, Jensen-Shannon Divergence, and cross-entropy metrics.
    • Used a Random Forest model to transfer labels between datasets.
  • in silico gene perturbation analysis using the CellOracle package

    • Performed pseudotime analysis to order cells along a developmental trajectory
    • Analyzed the effects of gene knockouts on downstream gene expression using gene regulatory networks
  • Spatial Mapping using Tangram Package

    • Mapped RNA-seq data onto spatial coordinates using a stereo-seq dataset with the Tangram package.
    • Analyzed cell-cell interactions at different developmental stages, enhancing the understanding of spatial gene expression patterns.
    • Integration of RNA-seq with Spatial Transcriptomics
    • Integrated RNA-seq data with spatial transcriptomics (stereo-seq) to map gene expression onto spatial coordinates.
    • Facilitated label transfer, analysis of cell-cell communication through ligand-receptor interactions, and investigation of cell-cell transitions using manually curated zebrafish genes for proliferation and apoptosis.
  • Differential Expression Analysis of Immune-Related Genes

    • Conducted differential expression analysis of immune-related genes in zebrafish across developmental stages.
    • Examined genes related to viral diseases, bacterial infections, and stress responses.

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