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Detailed Overview of the Notebooks

Notebook 01_scRNA_analysis_preprocessing.ipynb: Basic Demo Workflow

By the completion of this notebook, a user will be able to do the following:

  • Load and Preprocess the data

    • Load a sparse matrix in h5ad format using Scanpy
    • Preprocess the data, implementing standard QC metrics to assess cell and gene quality per cell, as well as per gen

  • QC cells visually to understand the data

    • Users will learn how to visually inspect 5 different plots that help reflect quality control metrics for single cell data to:
      • Identify stressed or dying cells undergoing apoptosis
      • Empty droplets or dead cells
      • Cells with abnormal gene counts
      • Low quality or overly dominant cells

  • Filter unusual cells

    • Users will learn how to remove cells with an extreme number of genes expressed
    • Users will filter out cells with an unusual amount of mitochondrial content

  • Remove unwanted sources of variation

    • Select most variable genes to better inform analysis and increase computational efficiency
    • Regress out additional technical variation that we observed in the visual plots (Note, this can actually remove biologically relevant information, and would need to be carefully considered with a more complex data set)
    • Standardize by using a z-score transformation

  • Cluster and visualize data

    • Implement PCA to reduce computational complexity. We use the GPU-accelerated PCA implementation from cuML, which significantly speeds up computation compared to CPU-based methods.
    • Identify batch effects visually by generating a UMAP plot with graph-based clustering

  • Batch Correction and analysis

    • Remove assay-specific batch effects using Harmony
    • Re-compute the k-nearest neighbors graph and visualize using the UMAP.
    • Perform graph-based clustering
    • Visualize using other methods (tSNE)

  • Explore the biological information from the data

    • Differential expression analysis: Identifying marker genes for cell types
      • Implement logistic regression
      • Rank genes that distinguish cell types
    • Trajectory analysis
      • Implement a diffusion map to understand the progress of cell types

Notebook 02_scRNA_analysis_extended.ipynb - Decoupler-GPU: Accelerated Transcriptional Regulatory Analysis

This is an overview of methods that can be used to investigate transcriptional regulation. Although the tutorial does not dive deeply into these models, the notebook is used to reflect a difference in speed that is benefitted by GPU.

Notebook 03_scRNA_analysis_with_pearson_residuals.ipynb - A different way to normalize with Pearson Residuals

By the completion of this notebook, a user will be able to remove unwanted sources of variation using Pearson Residuals to normalize. This is a different approach to analysis from Notebook 01. After filtering, we introduce a normalization step toa address the potential issues in how we previously removed unwanted sources of variation.

Notebook 04_scRNA_analysis_dask_out_of_core.ipynb - Scale analysis to 11M cells easily and quickly leveraging Dask

By the completion of this notebook, a user will be able to do the following:

  • Create a local DASK cluster
  • Load data directly into an AnnData object using a Dask array
  • Filter cells and genes without additional computation
  • Log normalize the data and identify highly variable genes
  • Scale gene expression
  • Compute PCA using GPU acceleration

Notebook 05_spatial_demo.ipynb - Spatial Analysis with rapids-singlecell and Squidpy

By the completion of this notebook, a user will be able to do the following:

  • Compute spatial autocorrelation using Moran's I (global spatial structure) and Geary's C (local expression differences) to identify niche-specific expression and spatial gradients
  • Perform co-occurrence analysis to measure the probability of cell-type pairs co-localizing across varying spatial distances, revealing tissue organization and cell-cell interactions
  • Leverage GPU-accelerated spatial implementations in rapids-singlecell for significant speedups over CPU-based methods

Notebook 06_scRNA_analysis_1.0M_brain_example.ipynb - End to end example on 1M Brain Cells

In this notebook, a user will perform similar steps as the Notebook 01 (end to end demo), but on 1M brain cells.

Notebook 07_perturbation_analysis_invivo_brain_example.ipynb - Perturbation Analysis: In Vivo Brain CRISPR Atlas Example

This notebook demonstrates GPU-accelerated perturbation analysis on a whole-brain single-nucleus CRISPR atlas (~3.5M cells, targeting ~2,000 genes across hundreds of neuronal types) using RAPIDS-singlecell. By the completion of this notebook, a user will be able to do the following:

  • Load a preprocessed Zarr dataset from HuggingFace into a GPU-ready AnnData object using Dask for out-of-core processing
  • Configure a local CUDA cluster and tune GPU memory management with RAPIDS Memory Manager (RMM)
  • Build GPU-accelerated PCA embeddings on the full dataset and visualize cell populations with UMAP
  • Compute pairwise E-distances between perturbation groups and non-targeting controls across all cell types to build a global perturbation-response map
  • Overlay external essential gene lists (Blomen et al., Science 2015) in select cell types and use Mann-Whitney U tests to show that essential gene perturbations produce significantly higher E-distances