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Analyzing time series perturbation data with Platt

Platt can be used to analyze data with multiple time points across perturbations, such as the data found in Saunders, Srivatsan, et al. Nature, in press (2023). This study in includes ~3 million single cells across almost 2000 individual barcoded embryos. It includes 19 timepoints (18-96 hpf) and 23 genetic loss of function experiments. For more information about this dataset, see the ZSCAPE website.

Analysis of skeletal muscle with Platt

This vignette focuses on the skeletal muscle subset. Platt wraps some base Hooke functions to more easily fit kinetic models.
You can see our Hooke kinetics tutorial here.

The function fit_wt_model fits a Hooke model over time. It requires the following inputs:

  • cds - a monocle3 cell_data_set object
  • sample_group - a column in colData(cds) that specifies how cells are grouped into samples
  • cell_group - a column in colData(cds) that specifies how cells are grouped into types or states (e.g. cluster)
  • perturbation_col - column name of the perturbation ids
  • ctrl_ids - a list of control ids. These will be collapsed as a single control. 
muscle_wt_ccm = fit_wt_model(skeletal_muscle_cds, 
                             sample_group = "embryo", 
                             cell_group = "cell_type", 
                             perturbation_col = "perturbation", 
                             ctrl_ids = c("ctrl-inj"))

The functionplot_cell_type_control_kinetics() allows you to view when cell types are at their peak abundance in the data set. It requires the following inputs:

  • wt_ccm - a Hooke cell_count_model object (output of fit_wt_model)
  • start_time - start of time interval
  • stop_time - end of time interval
  • new_data - tibble of covariates to predict on
  • color_points_by - how to color counts
  • raw_counts - whether to use raw counts of conditionally predicted counts
plot_cell_type_control_kinetics(muscle_wt_ccm, 
                                start_time = 18, 
                                stop_time = 48, 
                                newdata = tibble(expt = "GAP16"),
                                color_points_by = "expt",
                                raw_counts = F)

Plotting perturbation kinetics

The function fit_mt_models() fits a kinetic model over time and perturbation. It requires the following inputs:

  • cds - a monocle3 cell_data_set object
  • sample_group - a column in colData(cds) that specifies how cells are grouped into samples
  • cell_group - a column in colData(cds) that specifies how cells are grouped into types or states (e.g. cluster)
  • perturbation_col - column name of the perturbation ids
  • ctrl_ids - a list of control ids. These will be collapsed as a single control. 
skeletal_muscle_comb_cds = load_monocle_objects("~/OneDrive/UW/Trapnell/hooke_manuscript/R_objects/partition_skeletal_ref_gap16_cds_v2.1.0/")

tbx16_msgn1_ccm = fit_mt_models(skeletal_muscle_comb_cds, 
                                sample_group = "embryo", 
                                cell_group = "cell_type", 
                                perturbation_col = "perturbation", 
                                ctrl_ids = c("ctrl-inj"), 
                                mt_ids = c("tbx16-msgn1"))

The functionplot_cell_type_perturb_kinetics() plots a given perturbation's kinetics against the wild type. It requires the following inputs:

  • mt_ccm - a Hooke cell_count_model object (output of fit_mt_model)
  • start_time - start of time interval
  • stop_time - end of time interval
  • new_data - tibble of covariates to predict on
  • color_points_by - how to color counts
  • raw_counts - whether to use raw counts of conditionally predicted counts
plot_cell_type_perturb_kinetics(tbx16_msgn1_ccm, 
                                newdata = tibble("expt"= "GAP16"), 
                                raw_counts = F) + 
                                xlab("time")