Project Pseudotime and Branch Probabilities to Single Cells

This function projects pseudotime and branch probabilities from pseudobulk data to single-cell resolution (milo). The results are stored in the colData of the milo object.

Usage

projectPseudotimeToCell(
  milo,
  pb_milo,
  term_states = NULL,
  pseudotime_key = "pseudotime",
  suffix = "",
  verbose = TRUE
)

Arguments

milo

A SingleCellExperiment or Milo object. Represents single-cell data where pseudotime and branch probabilities will be projected.

pb_milo

A pseudobulk Milo object. Contains aggregated branch probabilities and pseudotime information to be transferred to single cells.

term_states

Named vector of terminal states, with branch probabilities to be transferred. The names should correspond to branches of interest.

pseudotime_key

Character. The column name in colData of pb_milo that contains the pseudotime information which was used in the markovProbability function. Default is "pseudotime".

suffix

Character. A suffix to be added to the new column names in colData. Default is an empty string ('').

verbose

Boolean, whether to print messages/warnings.

Value

subset of milo or SingleCellExperiment object where cell that do not belong to any neighbourhood are removed and projected pseudotime information stored colData

Examples

data(sce_vdj)
# downsample to first 2000 cells
sce_vdj <- sce_vdj[, 1:2000]
sce_vdj <- setupVdjPseudobulk(sce_vdj,
    already.productive = FALSE,
    allowed_chain_status = c("Single pair", "Extra pair")
)
#> Checking productivity from productive_abT_VDJ, productive_abT_VJ ...
#> 1461 of cells filtered
#> checking allowed chains...
#> 3 of cells filtered
#> VDJ data extraction begin:
#> extract_cols not specified, automatically generate colnames for extraction.
#> Extract main TCR from v_call_abT_VDJ, d_call_abT_VDJ, j_call_abT_VDJ, v_call_abT_VJ, j_call_abT_VJ ...
#> Complete.
#> Filtering cells from v_call_abT_VDJ_main, j_call_abT_VDJ_main, v_call_abT_VJ_main, j_call_abT_VJ_main ...
#> 11 of cells filtered
#> 525 of cells remain.
# Build Milo Object
set.seed(100)
milo_object <- miloR::Milo(sce_vdj)
milo_object <- miloR::buildGraph(milo_object,
    k = 50, d = 20,
    reduced.dim = "X_scvi"
)
#> Constructing kNN graph with k:50
milo_object <- miloR::makeNhoods(milo_object,
    reduced_dims = "X_scvi",
    d = 20
)
#> Checking valid object
#> Running refined sampling with reduced_dim

# Construct Pseudobulked VDJ Feature Space
pb.milo <- vdjPseudobulk(milo_object, col_to_take = "anno_lvl_2_final_clean")
pb.milo <- scater::runPCA(pb.milo, assay.type = "Feature_space")
#> Warning: more singular values/vectors requested than available
#> Warning: You're computing too large a percentage of total singular values, use a standard svd instead.

# Define root and branch tips
pca <- t(as.matrix(SingleCellExperiment::reducedDim(pb.milo, type = "PCA")))
branch.tips <- c(which.min(pca[, 2]), which.max(pca[, 2]))
names(branch.tips) <- c("CD8+T", "CD4+T")
root <- which.min(pca[, 1])

# Construct Diffusion Map
dm <- destiny::DiffusionMap(t(pca), n_pcs = 10, n_eigs = 5)
dif.pse <- destiny::DPT(dm, tips = c(root, branch.tips), w_width = 0.1)

# Markov Chain Construction
pb.milo <- markovProbability(
    milo = pb.milo,
    diffusionmap = dm,
    diffusiontime = dif.pse[[paste0("DPT", root)]],
    terminal_state = branch.tips,
    root_cell = root,
    pseudotime_key = "pseudotime"
)
#> Sampling and flocking waypoints...
#> Markov chain construction...
#> Computing fundamental matrix and absorption probabilities...
#> Project probabilites from waypoints to each pseudobulk...
#> Complete.
# Project Pseudobulk Data
projected_milo <- projectPseudotimeToCell(
    milo_object,
    pb.milo,
    branch.tips,
    pseudotime_key = "pseudotime"
)
#> 0 number of cells removed due to not belonging to any neighbourhood