Trajectory Inference Preprocessing

trajectory-inference-preprocessing

Summary

Prepare clustered single-cell gene expression data for trajectory inference by constructing a k-nearest neighbor graph and applying Leiden clustering. This preprocessing step abstracts the high-dimensional cell-to-cell neighborhood structure into a coarse-grained partition graph suitable for downstream trajectory algorithms like PAGA.

When to use

Apply this skill when you have raw or normalized single-cell RNA-seq expression data stored in an AnnData object (.h5ad format) and your analysis goal is to infer developmental or differentiation trajectories. Use it specifically when you need to move from individual cell resolution to cluster-level abstraction before running partition-based graph abstraction (PAGA) or other trajectory methods that operate on clustered data.

When NOT to use

• The input data has not been normalized or contains confounding batch effects that have not been corrected; trajectory inference requires comparable gene expression levels across cells.
• You already have manual cell type annotations and do not need unsupervised clustering; Leiden clustering may contradict existing biological labels.
• Your single-cell dataset has extremely low depth (< 500 UMI per cell on average) or sparse sampling of the developmental process, as the kNN graph and clustering will not reliably capture continuous transitions.

Inputs

• AnnData object (.h5ad) with normalized or log-transformed gene expression matrix in .X
• PCA representation (typically pre-computed in .obsm['X_pca'])
• Raw or preprocessed expression counts

Outputs

• AnnData object with k-nearest neighbor graph (connectivities and distances matrices in .obsp)
• AnnData object with Leiden cluster assignments in .obs['leiden']
• Neighborhood graph ready for partition-based abstraction

How to apply

Load your expression matrix into an AnnData object and compute the principal component analysis (PCA) representation using default scanpy preprocessing. Next, construct a k-nearest neighbor (kNN) graph using sc.pp.neighbors() with default parameters (n_neighbors=15, use_rep='X_pca'), which captures local cell-to-cell connectivity in reduced dimensionality space. Apply sc.tl.leiden() to partition cells into discrete clusters, which balances community detection sensitivity with computational tractability. The resulting clusters become nodes in the downstream trajectory graph. Verify that the neighbors graph (stored in adata.obsp['distances'] and adata.obsp['connectivities']) has been computed and that cluster assignments exist in adata.obs['leiden'] before proceeding to trajectory inference methods.

Related tools

• Scanpy (Core toolkit providing sc.pp.neighbors(), sc.tl.leiden(), and trajectory inference functions including sc.tl.paga()) — https://github.com/scverse/scanpy
• anndata (Data container for single-cell gene expression data; stores expression matrix, neighborhood graph, and cluster annotations) — https://github.com/scverse/anndata
• Python (Programming environment for executing scanpy workflows and data manipulation)
• PAGA (Downstream trajectory inference method that consumes the kNN-clustered data from this preprocessing step to compute partition-based graph abstraction) — https://github.com/theislab/paga

Examples

import scanpy as sc; adata = sc.datasets.paul15(); sc.pp.neighbors(adata, n_neighbors=15, use_rep='X_pca'); sc.tl.leiden(adata); print(adata.obsp['connectivities'].shape, adata.obs['leiden'].nunique())

Evaluation signals

• adata.obsp['connectivities'] and adata.obsp['distances'] exist and have shape (n_cells, n_cells); confirm non-zero entries correspond to k=15 nearest neighbors per cell.
• adata.obs['leiden'] contains discrete cluster labels with no missing values; verify number of clusters is biologically reasonable (typically 5–20 for hematopoiesis datasets like paul15).
• adata.obsm['X_pca'] is present and has dimensionality matching the number of PCs retained (default ~50); confirm explained variance ratio shows expected drop-off.
• No cells have zero degree in the kNN graph (i.e., all cells have at least 15 neighbors); check that k=15 is not larger than the total number of cells minus 1.
• Leiden clustering is reproducible across runs with the same random seed; verify cluster stability by inspecting silhouette scores or comparing cluster composition to known cell type markers.

Limitations

• The choice of k=15 neighbors is a default that may not suit all datasets; small cell populations or datasets with extreme local density gradients may require manual tuning of n_neighbors.
• Leiden clustering is stochastic; results depend on the random seed. Different seeds may yield slightly different cluster memberships, affecting downstream trajectory inference.
• PCA-based neighborhood computation assumes that the first ~50 principal components capture biologically meaningful structure; datasets with strong technical confounders or batch effects may require batch correction before this step.
• The method assumes cells are sampled densely enough along the trajectory to form a continuous kNN manifold; very sparse or discontinuous cell sampling may fragment the neighborhood graph into disconnected components.

Evidence

• [other] Scanpy is a scalable toolkit for analyzing single-cell gene expression data that includes trajectory inference capabilities, which encompasses methods like PAGA for constructing partition-based graph abstractions from clustered data.: "Scanpy is a scalable toolkit for analyzing single-cell gene expression data that includes trajectory inference capabilities"
• [other] Compute k-nearest neighbors graph using sc.pp.neighbors with default parameters (n_neighbors=15, use_rep='X_pca').: "Compute k-nearest neighbors graph using sc.pp.neighbors with default parameters (n_neighbors=15, use_rep='X_pca')"
• [other] Run Leiden clustering via sc.tl.leiden to partition cells into clusters.: "Run Leiden clustering via sc.tl.leiden to partition cells into clusters"
• [intro] It includes preprocessing, visualization, clustering, trajectory inference and differential expression testing: "It includes preprocessing, visualization, clustering, trajectory inference and differential expression testing"
• [other] Verify that adata.uns['paga'] exists and contains a 'connectivities' matrix with dimensions matching n_clusters × n_clusters.: "Verify that adata.uns['paga'] exists and contains a 'connectivities' matrix with dimensions matching n_clusters × n_clusters"