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.