fix: avoid basilisk cluster arg collisions

Author: GuangchuangYu

R/celloracle.R

@@ -42,20 +42,22 @@ RunInSilicoPerturbation <- function(sce, target_gene, perturbation_value = 0.0,
 
     message("Running CellOracle via basilisk...")
 
-    co_res <- basilisk::basiliskRun(env = sclet_celloracle_env, fun = function(counts, cl, emb, tgt, pval, grn_f, ...) {
+    co_res <- basilisk::basiliskRun(
+        env = sclet_celloracle_env,
+        fun = function(counts, cluster_labels, emb, tgt, pval, grn_f, reduction_name, ...) {
         ad <- reticulate::import("anndata")
         pd <- reticulate::import("pandas")
         co <- reticulate::import("celloracle")
         sc <- reticulate::import("scanpy")
 
-        obs <- pd$DataFrame(list(clusters = cl), index = rownames(counts))
+        obs <- pd$DataFrame(list(clusters = cluster_labels), index = rownames(counts))
         adata <- ad$AnnData(X = counts, obs = obs)
-        adata$obsm[paste0("X_", tolower(reduction))] <- emb
+        adata$obsm[paste0("X_", tolower(reduction_name))] <- emb
 
         # Preprocessing inside Python
         sc$pp$normalize_total(adata, target_sum = 10000)
         sc$pp$log1p(adata)
-        sc$pp$neighbors(adata, use_rep = paste0("X_", tolower(reduction)))
+        sc$pp$neighbors(adata, use_rep = paste0("X_", tolower(reduction_name)))
 
         # Load base GRN
         base_grn <- pd$read_csv(grn_f, index_col = 0L)
@@ -81,7 +83,15 @@ RunInSilicoPerturbation <- function(sce, target_gene, perturbation_value = 0.0,
         shift <- oracle$adata$obsm["delta_X"]
 
         return(as.matrix(shift))
-    }, counts = counts_mat, cl = clusters, emb = emb, tgt = target_gene, pval = perturbation_value, grn_f = base_grn_path, ...)
+    },
+    counts = counts_mat,
+    cluster_labels = clusters,
+    emb = emb,
+    tgt = target_gene,
+    pval = perturbation_value,
+    grn_f = base_grn_path,
+    reduction_name = reduction,
+    ...)
 
     # Store the shift vector in reducedDim, e.g., "celloracle_shift"
     rownames(co_res) <- colnames(sce)

R/cellrank.R

@@ -49,22 +49,24 @@ RunCellRank <- function(sce, reduction = "PCA", cluster_key = ActiveIdent(sce),
 
     message("Running CellRank via basilisk...")
 
-    cr_res <- basilisk::basiliskRun(env = sclet_cellrank_env, fun = function(v, s, u, emb, cl, ...) {
+    cr_res <- basilisk::basiliskRun(
+        env = sclet_cellrank_env,
+        fun = function(v, s, u, emb, cluster_labels, reduction_name, ...) {
         ad <- reticulate::import("anndata")
         cr <- reticulate::import("cellrank")
         pd <- reticulate::import("pandas")
 
-        obs <- pd$DataFrame(list(clusters = cl), index = rownames(v))
+        obs <- pd$DataFrame(list(clusters = cluster_labels), index = rownames(v))
         adata <- ad$AnnData(X = s, obs = obs)
         adata$layers["spliced"] <- s
         adata$layers["unspliced"] <- u
         adata$layers["velocity"] <- v
-        adata$obsm[paste0("X_", tolower(reduction))] <- emb
+        adata$obsm[paste0("X_", tolower(reduction_name))] <- emb
 
         # We need a neighbor graph and velocity graph.
         # Since we just have the matrices, we can use scvelo to compute velocity graph first.
         scv <- reticulate::import("scvelo")
-        scv$pp$neighbors(adata, use_rep = paste0("X_", tolower(reduction)))
+        scv$pp$neighbors(adata, use_rep = paste0("X_", tolower(reduction_name)))
         scv$tl$velocity_graph(adata)
 
         # CellRank pipeline
@@ -84,7 +86,14 @@ RunCellRank <- function(sce, reduction = "PCA", cluster_key = ActiveIdent(sce),
             terminal_states = as.character(term_states),
             absorption_probs = as.matrix(abs_probs$X)
         ))
-    }, v = v_mat, s = s_mat, u = u_mat, emb = emb, cl = clusters, ...)
+    },
+    v = v_mat,
+    s = s_mat,
+    u = u_mat,
+    emb = emb,
+    cluster_labels = clusters,
+    reduction_name = reduction,
+    ...)
 
     # Store results
     sce$cellrank_terminal_states <- cr_res$terminal_states