dyntoy

Here dyntoy method will be demonstrated clearly and hope that this document can help you.

Estimating parameters from a real dataset

Before simulating datasets, it is important to estimate some essential parameters from a real dataset in order to make the simulated data more real.

library(simmethods)
library(SingleCellExperiment)
# Load data
ref_data <- simmethods::data

To simulate trajectory datasets using dyntoy, we first performe trajectory inference analysis to the real data. If no group information is provided, the k-means algorithm is used to determine the number of clusters(groups) that the real data contains.

estimate_result <- simmethods::dyntoy_estimation(
  ref_data = ref_data,
  verbose = T,
  seed = 10
)
# Performing k-means and determin the best number of clusters...
# Add grouping to data...
# Estimating parameters using dyntoy
# Executing 'slingshot' on '20230816_112447__data_wrapper__Up1tJGIOUk'
# With parameters: list(cluster_method = "pam", ndim = 20L, shrink = 1L, reweight = TRUE,     reassign = TRUE, thresh = 0.001, maxit = 10L, stretch = 2L,     smoother = "smooth.spline", shrink.method = "cosine")
# inputs: expression
# priors :
# Using full covariance matrix

Users can also input the group information of cells and the k-means will not be used:

group <- as.numeric(simmethods::group_condition)
estimate_result <- simmethods::dyntoy_estimation(
  ref_data = ref_data,
  other_prior = list(group.condition = group),
  verbose = T,
  seed = 10
)
# Add grouping to data...
# Estimating parameters using dyntoy
# Executing 'slingshot' on '20230816_112451__data_wrapper__Kr1TdrsxL0'
# With parameters: list(cluster_method = "pam", ndim = 20L, shrink = 1L, reweight = TRUE,     reassign = TRUE, thresh = 0.001, maxit = 10L, stretch = 2L,     smoother = "smooth.spline", shrink.method = "cosine")
# inputs: expression
# priors :
# Using full covariance matrix

Simulating datasets with cell trajectory using dyntoy

After estimating parameter from a real dataset, we will simulate a dataset based on the learned parameters with different scenarios.

  1. Datasets with default parameters
  2. Determin the number of cells and genes
  3. Visualization

Datasets with default parameters

The reference data contains 160 cells and 4000 genes, if we simulate datasets with default parameters and then we will obtain a new data which has the same size as the reference data.

simulate_result <- simmethods::dyntoy_simulation(
  parameters = estimate_result[["estimate_result"]],
  other_prior = NULL,
  return_format = "SCE",
  seed = 111
)
# nCells: 160
# nGenes: 4000
# de.prob: 0.1
SCE_result <- simulate_result[["simulate_result"]]
dim(SCE_result)
# [1] 4000  160

Determin the number of cells and genes

In dyntoy, we can set nCells and nGenes to specify the number of cells and genes.

Here, we simulate a new dataset with 1000 cells and 1000 genes:

simulate_result <- simmethods::dyntoy_simulation(
  parameters = estimate_result[["estimate_result"]],
  return_format = "list",
  other_prior = list(nCells = 1000,
                     nGenes = 1000),
  seed = 111
)
# nCells: 1000
# nGenes: 1000
# de.prob: 0.1
result <- simulate_result[["simulate_result"]][["count_data"]]
dim(result)
# [1] 1000 1000

Visualization

Make sure that you have already installed several R packages:

if(!requireNamespace("dynwrap", quietly = TRUE)){install.packages("dynwrap")}
if(!requireNamespace("dyndimred", quietly = TRUE)){install.packages("dyndimred")}
if(!requireNamespace("dynplot", quietly = TRUE)){install.packages("dynplot")}
if(!requireNamespace("tislingshot", quietly = TRUE)){devtools::install_github("dynverse/ti_slingshot/package/")}

First we should wrap the data into a standard object:

dyn_object <- dynwrap::wrap_expression(counts = t(result),
                                       expression = log2(t(result) + 1))

Next, we infer the trajectory using SlingShot which has been proved to be the most best method to do this:

model <- dynwrap::infer_trajectory(dataset = dyn_object,
                                   method = tislingshot::ti_slingshot(),
                                   parameters = NULL,
                                   give_priors = NULL,
                                   seed = 111,
                                   verbose = TRUE)
# Executing 'slingshot' on '20230816_112514__data_wrapper__EuubW5InDz'
# With parameters: list(cluster_method = "pam", ndim = 20L, shrink = 1L, reweight = TRUE,     reassign = TRUE, thresh = 0.001, maxit = 10L, stretch = 2L,     smoother = "smooth.spline", shrink.method = "cosine")
# inputs: expression
# priors :
# Using full covariance matrix

Finally, we can plot the trajectory after performing dimensionality reduction:

dimred <- dyndimred::dimred_umap(dyn_object$expression)
dynplot::plot_dimred(model, dimred = dimred)
# Coloring by milestone
# Using milestone_percentages from trajectory

For more details about trajectory inference and visualization, please check dynverse.