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Downstream Analysis Roadmap

Downstream analysis should turn a trajectory result into biological interpretation: which genes drive a transition, which regulators control them, and how perturbations may redirect fate.

Scope

flowchart TD
    A["Cafe trajectory"] --> B["Gene trends"]
    A --> C["Driver gene ranking"]
    A --> D["GRN inference"]
    C --> E["Perturbation prediction"]
    D --> E
    B --> F["Interactive report"]
    C --> F
    D --> F
    E --> F

Priority Modules

Priority Module Purpose
P0 branch-specific gene trends explain expression dynamics along pseudotime
P0 lineage driver genes identify genes associated with terminal fate choice
P1 GRN wrapper connect regulators to target genes
P1 cellxgene-cafe display inspect trajectory and downstream results interactively
P2 perturbation prediction predict fate shift after gene perturbation
P2 trajectory alignment compare dynamic programs across datasets

Gene Trend Analysis

For each gene \(g\) and lineage \(k\):

\[ y_{ig}^{(k)} = s_{g,k}(\tau_i) + \epsilon_i \]

The first implementation can use simple smoothers or generalized additive models. The important output is a transition-aware table:

gene lineage score trend_type peak_time

Driver Gene Analysis

Driver genes should be ranked per lineage or edge:

\[ D(g, e) = \left|\operatorname{cor}(x_g, \alpha_e)\right| \times \operatorname{specificity}(g, e) \]

Candidate wrappers:

  • CellRank lineage drivers
  • Dynamo vector-field regulators
  • random forest feature importance from position prediction
  • branch-specific differential expression

GRN Inference

Candidate wrappers:

  • SCENIC / pySCENIC
  • GRNBoost2
  • CellOracle
  • Dynamo regulatory analysis

Cafe should normalize GRN outputs to:

regulator target lineage_or_edge weight sign evidence

Perturbation Prediction

Perturbation output should be expressed as a fate or transition shift:

\[ \Delta P_{k}^{(g)} = P(y = k \mid \operatorname{perturb}(g)) - P(y = k) \]

Candidate tools:

  • CellOracle perturbation simulation
  • scGen / CPA-style latent perturbation models
  • GEARS-like perturbation models
  • method-specific vector field perturbation

cellxgene-cafe Connection

The downstream module should export results through the Cafe Result Schema, so cellxgene-cafe can display:

  • pseudotime and lineage coloring,
  • velocity arrows,
  • milestone network overlay,
  • driver gene tables,
  • gene trends,
  • GRN edge tables,
  • perturbation effect tables.

Student Deliverables

Milestone 1:

  • run one gene-trend analysis on a standard dataset
  • run one CellRank-style driver gene analysis
  • write a short method survey for GRN and perturbation tools
  • export one result into the Cafe result schema

Milestone 2:

  • implement DriverGeneWrapper design draft
  • implement GRNWrapper design draft
  • create a downstream demo notebook
  • add interactive display requirements for cellxgene-cafe

Milestone 3:

  • complete one biological case study
  • compare driver genes with known markers
  • create publication-quality downstream figures