Below are a few references meant to provide more background and some of the approaches researchers are applying in the fields relevant to these Crunches. This is not meant to be an exhaustive list and many important works are not listed here.

Single cell transcriptomic adipocytes datasets

Dissecting the impact of transcription factor dose on cell reprogramming heterogeneity using scTF-seq
A single-cell atlas of human and mouse white adipose tissue
Human subcutaneous and visceral adipocyte atlases uncover classical and nonclassical adipocytes and depot-specific patterns
Adipose tissue retains an epigenetic memory of obesity after weight loss.
Unveiling adipose populations linked to metabolic health in obesity
Spatial mapping reveals human adipocyte subpopulations with distinct sensitivities to insulin
snRNA-seq reveals a subpopulation of adipocytes that regulates thermogenesis
Wnt signaling preserves progenitor cell multipotency during adipose tissue development
Adipogenic and SWAT cells separate from a common progenitor in human brown and white adipose depots
Single-Nucleus Analysis of Human White Adipose Tissue Reveals Adipocyte Subsets with Distinct Metabolic Profiles
Mapping the transcriptional landscape of human white and brown adipogenesis using single-nuclei RNA-seq

CRISPR/Cas9 genetic perturbation screens

Modeling perturbations and causality

Machine learning for perturbational single-cell omics
Elements of Causal Inference: Foundations and Learning Algorithms
Causal Structure and Representation Learning with Biomedical Applications
scPerturb: Information Resource for Harmonized Single-Cell Perturbation Data
GeneDisco: A Benchmark for Experimental Design in Drug Discovery
Systema: a framework for evaluating genetic perturbation response prediction beyond systematic variation
Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines
MORPH Predicts the Single-Cell Outcome of Genetic Perturbations Across Conditions and Data Modalities
Learning Genetic Perturbation Effects with Variational Causal Inference
Squidiff: predicting cellular development and responses to perturbations using a diffusion model
Predicting cellular responses to perturbation across diverse contexts with State
TxPert: Leveraging Biochemical Relationships for Out-of-Distribution Transcriptomic Perturbation Prediction
GEARS: Predicting transcriptional outcomes of novel multi-gene perturbations
Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling
Predicting cellular responses to complex perturbations in high-throughput screens
PerturbNet predicts single-cell responses to unseen chemical and genetic perturbations
Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution
Active Learning for Optimal Intervention Design in Causal Models
Control of cell state transitions

Reading these articles is not necessary to complete the challenge, but we believe these can be a helpful resource.