Add RunWOT function

.Rbuildignore

@@ -12,4 +12,6 @@
 ^LICENSE\.md$
 ^SCExplorer$
 ^README$
-^README.md$
+^README.md$
+^tmaps$
+^src/.*[^.cpp]$

.gitignore

@@ -6,4 +6,5 @@
 SCExplorer
 docs
 renv
-renv.lock
+renv.lock
+tmaps

NAMESPACE

@@ -107,6 +107,7 @@ export(RunSlingshot)
 export(RunSymphonyMap)
 export(RunTriMap)
 export(RunUMAP2)
+export(RunWOT)
 export(SankeyPlot)
 export(Scanorama_integrate)
 export(Seurat_integrate)

R/SCP-analysis.R

@@ -5340,7 +5340,7 @@ maxDepth <- function(x, depth = 0) {
 #' @importFrom reticulate py_to_r
 #' @export
 check_python_element <- function(x, depth = maxDepth(x)) {
-  if (depth == 0 || !is.list(x)) {
+  if (depth == 0 || !is.list(x) || !inherits(x, "python.builtin.object")) {
     if (inherits(x, "python.builtin.object")) {
       x_r <- tryCatch(py_to_r(x), error = identity)
       if (inherits(x_r, "error")) {
@@ -5702,27 +5702,32 @@ RunPalantir <- function(srt = NULL, assay_X = "RNA", slot_X = "counts", assay_la
 #' @inheritParams RunSCVELO
 #'
 #' @examples
-#' @return A \code{anndata} object.
+#' @export
 RunWOT <- function(srt = NULL, assay_X = "RNA", slot_X = "counts", assay_layers = c("spliced", "unspliced"), slot_layers = "counts",
                    adata = NULL, group_by = NULL,
                    time_field = "Time", growth_iters = 3L, tmap_out = "tmaps/tmap_out",
-                   time_from = 1, time_to = NULL, get_coupling = FALSE, recalculate = FALSE,
+                   time_from = NULL, time_to = NULL, get_coupling = FALSE, recalculate = FALSE,
                    palette = "Paired", palcolor = NULL,
                    show_plot = TRUE, dpi = 300, save = FALSE, dirpath = "./", fileprefix = "",
                    return_seurat = !is.null(srt)) {
   check_Python("wot")
   if (all(is.null(srt), is.null(adata))) {
     stop("One of 'srt', 'adata' must be provided.")
   }
-  if (is.null(group_by) && any(!is.null(early_group), !is.null(terminal_groups))) {
-    stop("'group_by' must be provided when early_group or terminal_groups provided.")
+  if (is.null(group_by)) {
+    stop("'group_by' must be provided.")
   }
-  if (is.null(linear_reduction) && is.null(nonlinear_reduction)) {
-    stop("'linear_reduction' or 'nonlinear_reduction' must be provided at least one.")
+  if (is.null(time_field)) {
+    stop("'time_field' must be provided.")
   }
-  if (is.null(early_cell) && is.null(early_group)) {
-    stop("'early_cell' or 'early_group' must be provided.")
+  if (is.null(time_from)) {
+    stop("'time_from' must be provided.")
   }
+  if (isTRUE(get_coupling) && is.null(time_to)) {
+
+  }
+  warning("The 'get_coupling' paramter is only valid when 'time_to' is specified.")
+
   args <- mget(names(formals()))
   args <- lapply(args, function(x) {
     if (is.numeric(x)) {
@@ -5755,7 +5760,7 @@ RunWOT <- function(srt = NULL, assay_X = "RNA", slot_X = "counts", assay_layers
   args[["palette"]] <- palette_scp(levels(groups) %||% unique(groups), palette = palette, palcolor = palcolor)
 
   SCP_analysis <- reticulate::import_from_path("SCP_analysis", path = system.file("python", package = "SCP", mustWork = TRUE), convert = TRUE)
-  adata <- do.call(SCP_analysis$WOT, args)
+  adata <- do.call(WOT, args)
 
   if (isTRUE(return_seurat)) {
     srt_out <- adata_to_srt(adata)

R/SCP-workflow.R

@@ -189,7 +189,7 @@ check_srtList <- function(srtList, batch = NULL, assay = NULL,
         }
       }
       if (status == "unknown") {
-        warning("Can not determine whether data ", i, " is log-normalized...\n", immediate. = TRUE)
+        warning("Can not determine whether data ", i, " is log-normalized...", immediate. = TRUE)
       }
     }
     if (is.null(HVF)) {

inst/python/SCP_analysis.py

@@ -770,14 +770,15 @@ def Palantir(adata=None, h5ad=None,group_by=None,palette=None,
 
 def WOT(adata=None, h5ad=None, group_by=None,palette=None, 
         time_field = "Time", growth_iters = 3, tmap_out = "tmaps/tmap_out",
-        time_from = 1, time_to = None, get_coupling = False,recalculate=False,
+        time_from = None, time_to = None, get_coupling = False,recalculate=False,
         show_plot=True, dpi=300, save=False, dirpath="./", fileprefix=""):
   import matplotlib.pyplot as plt
   import scanpy as sc
   import numpy as np
   import statistics
   import pandas as pd
   from math import hypot
+  import wot
 
   import warnings
   warnings.simplefilter("ignore", category=UserWarning)
@@ -810,6 +811,10 @@ def WOT(adata=None, h5ad=None, group_by=None,palette=None,
     if group_by is None:
       print("group_by must be provided.")
       exit()
+
+    if time_field is None:
+      print("time_field must be provided.")
+      exit()
 
     adata.obs[group_by] = adata.obs[group_by].astype(dtype = "category")
     if pd.api.types.is_categorical_dtype(adata.obs[time_field]):
@@ -823,15 +828,19 @@ def WOT(adata=None, h5ad=None, group_by=None,palette=None,
       adata.obs["time_field"] = adata.obs[time_field]
 
     time_dict = dict(zip(adata.obs[time_field],adata.obs["time_field"]))
-    ot_model <- wot.ot.OTModel(adata, growth_iters = growth_iters, day_field = "time_field")
+    if time_from not in time_dict.keys():
+      print("'time_from' is incorrect")
+      exit()
+
+    ot_model = wot.ot.OTModel(adata, growth_iters = growth_iters, day_field = "time_field")
 
     if recalculate is True:
       ot_model.compute_all_transport_maps(tmap_out = tmap_out)
       tmap_model = wot.tmap.TransportMapModel.from_directory(tmap_out)
     else:
       try:
         tmap_model = wot.tmap.TransportMapModel.from_directory(tmap_out)
-      except ValueError:
+      except (FileNotFoundError, ValueError):
         ot_model.compute_all_transport_maps(tmap_out = tmap_out)
         tmap_model = wot.tmap.TransportMapModel.from_directory(tmap_out)
 
@@ -845,15 +854,23 @@ def WOT(adata=None, h5ad=None, group_by=None,palette=None,
 
     trajectory_ds = tmap_model.trajectories(from_populations)
     trajectory_df = pd.DataFrame(trajectory_ds.X, index=trajectory_ds.obs_names, columns=trajectory_ds.var_names)
-    adata.uns["trajectory_"+str(time_from)]= trajectory_df
+    adata.uns["trajectory_"+str(time_from)]= trajectory_df.reindex(adata.obs_names)
 
     fates_ds = tmap_model.fates(from_populations)
     fates_df = pd.DataFrame(fates_ds.X, index=fates_ds.obs_names, columns=fates_ds.var_names)
-    adata.uns["fates_"+str(time_from)]= fates_df
+    existing_rows = fates_df.index.tolist()
+    new_rows = list(set(adata.obs_names) - set(existing_rows))
+    new_df = pd.DataFrame(0, index=new_rows, columns=fates_df.columns)
+    fates_df = pd.concat([fates_df, new_df])
+    adata.uns["fates_"+str(time_from)]= fates_df.reindex(adata.obs_names)
 
     # obs_list = wot.tmap.trajectory_trends_from_trajectory(trajectory_ds = trajectory_ds, expression_ds = adata)
 
     if time_to is not None:
+      if time_to not in time_dict.keys():
+        print("'time_to' is incorrect")
+        exit()
+
       to_populations = tmap_model.population_from_cell_sets(cell_sets, at_time = time_dict[time_to])
       transition_table = tmap_model.transition_table(from_populations, to_populations)
       transition_df = pd.DataFrame(transition_table.X, index=transition_table.obs_names, columns=transition_table.var_names)