Condiments

condiments

Workflows

Standard Workflow

Perform differential topology, progression, and fate selection analysis on single-cell data across multiple conditions.

library(condiments)
library(slingshot)
library(dplyr)

# Load toy dataset
data("toy_dataset", package = "condiments")
df <- toy_dataset$sd

# 1. Compute imbalance scores
scores <- imbalance_score(
  Object = df %>% select(Dim1, Dim2) %>% as.matrix(),
  conditions = df$conditions
)
df$scores <- scores$scores
df$scaled_scores <- scores$scaled_scores

# 2. Infer a common trajectory using slingshot
rd <- as.matrix(df[, c("Dim1", "Dim2")])
sds <- slingshot(rd, df$cl)

# 3. Test trajectory topology
top_res <- topologyTest(sds = sds, conditions = df$conditions, rep = 10)

# 4. Test for differential progression along lineages
prog_res <- progressionTest(sds, conditions = df$conditions, global = TRUE, lineages = TRUE)

# 5. Test for differential fate selection between lineages
dif_res <- fateSelectionTest(sds, conditions = df$conditions, global = FALSE, pairwise = TRUE)

Note on inputs/outputs: Input is a cell metadata data frame containing reduced dimensions and condition assignments; outputs are statistical test tables assessing differences in topology, progression, and fate selection between conditions.

When to Use

• Differential Topology Analysis: Determine if a common trajectory can be fitted across conditions or if separate trajectories are required using topologyTest.
• Differential Progression Analysis: Test if cells from different conditions are equally represented along pseudotime within a lineage using progressionTest.
• Differential Fate Selection: Assess whether cells differentiate preferentially along specific lineages across different conditions using fateSelectionTest.
• Imbalance Score Estimation: Identify regions of local condition imbalance in a reduced dimensional space using imbalance_score.

When NOT to Use

• Gene-Level Differential Expression: For testing individual gene expression changes along a trajectory, use tradeSeq (specifically fitGAM and conditionTest) instead of condiments directly.
• Strong Topology Disruption: If topologyTest rejects the null hypothesis, do not fit a common trajectory; instead, infer separate trajectories for each condition.

Data Requirements

• Reduced Dimensions: A matrix of coordinates in a reduced dimension space (e.g., PCA, UMAP, t-SNE).
• Condition Labels: A vector assigning each cell to a specific condition (e.g., "A" or "B").
• Cluster Labels: Cell cluster assignments for trajectory construction.
• Example Structure:

  data("toy_dataset", package = "condiments")
  df <- toy_dataset$sd
  rd <- as.matrix(df[, c("Dim1", "Dim2")])
  conditions <- df$conditions
  cl <- df$cl
  

Key Parameters

• Object: A matrix of reduced dimensions passed to imbalance_score.
• conditions: A vector of condition assignments for each cell.
• sds: A PseudotimeOrdering object (from slingshot) representing the inferred trajectory.
• rep (100): Number of permutations used to generate trajectories under the null in topologyTest.
• methods ("KS_mean"): The test method(s) to use in topologyTest (e.g., "KS_mean", "Classifier", "wasserstein_permutation").
• global (TRUE): Logical indicating whether to run a global test pooling all lineages in progressionTest or fateSelectionTest.
• lineages (TRUE): Logical indicating whether to test every lineage independently in progressionTest.
• pairwise (TRUE): Logical indicating whether to test every pair of lineages independently in fateSelectionTest.

Best Practices

• Check Integration Quality: Compute local imbalance scores using imbalance_score to verify if integration was successful (some regions should be balanced).
• Pre-test Topology: Always run topologyTest before downstream differential analysis to justify fitting a common trajectory.
• Multiple Testing Correction: Correct the resulting p-values from progressionTest and fateSelectionTest for multiple testing, especially for trajectories with a large number of lineages.
• Treat P-values as Suggestions: Trajectory inference is at the end of a long pipeline; do not put absolute faith in raw p-values and treat them as exploratory suggestions.

Common Pitfalls

• Slow Execution of Topology Test: topologyTest can be slow on large datasets; mitigate this by reducing the rep parameter or enabling parallelization with parallel = TRUE and a configured BPPARAM.
• Noisy Imbalance Scores: Raw imbalance scores can be noisy; use the smooth argument in imbalance_score to obtain smoothed, scaled scores.
• Over-interpreting Uncorrected P-values: Trajectory tests make multiple comparisons; always apply multiple testing correction to avoid false positives.

Alternatives

• slingshot: For basic trajectory inference without condition-level differential topology tests.
• tradeSeq: For gene-level differential expression analysis along trajectories using fitGAM and conditionTest.
• scater / scran: For upstream single-cell preprocessing, normalization, and dimensionality reduction.

Citations

• Street et al. 2018, BMC Genomics (Slingshot trajectory inference)
• Van den Berge et al. 2020, Nature Communications (tradeSeq differential expression)
• Lopez-Paz and Oquab 2016, Arxiv (Classifier two-sample tests)
• Smirnov 1939, Bull. Math. Univ. Moscou (Kolmogorov-Smirnov test)

References

• Homepage: https://bioconductor.org/packages/condiments
• Vignette: https://bioconductor.org/packages/release/bioc/vignettes/condiments/inst/doc/condiments.html