Merge pull request #141 from KrishnaswamyLab/dev

MAGIC v1.4.0

Author: scottgigante

Rmagic/DESCRIPTION

@@ -1,7 +1,7 @@
 Package: Rmagic
 Type: Package
 Title: MAGIC - Markov Affinity-Based Graph Imputation of Cells
-Version: 1.3.0
+Version: 1.4.0
 Authors@R: c(person(given = "David", family = "van Dijk", email = "davidvandijk@gmail.com", role = c("aut")),
 		         person(given = 'Scott', family = 'Gigante', email = 'scott.gigante@yale.edu', role = 'cre', 
 		                comment = c(ORCID = '0000-0002-4544-2764')))

Rmagic/R/magic.R

@@ -5,7 +5,7 @@
 #' applied to single-cell RNA sequencing data, as described in
 #' van Dijk et al, 2018.
 #'
-#' @param data input data matrix
+#' @param data input data matrix or Seurat object
 #' @param genes character or integer vector, default: NULL
 #' vector of column names or column indices for which to return smoothed data
 #' If 'all_genes' or NULL, the entire smoothed matrix is returned
@@ -38,6 +38,11 @@
 #' n_jobs = -2, all CPUs but one are used
 #' @param seed int or `NULL`, random state (default: `NULL`)
 #'
+#' @return If a Seurat object is passed, a Seurat object is returned. Otherwise, a "magic" object containing:
+#'  * **result**: matrix containing smoothed expression values
+#'  * **operator**: The MAGIC operator (python magic.MAGIC object)
+#'  * **params**: Parameters passed to magic
+#'
 #' @examples
 #' if (reticulate::py_module_available("magic")) {
 #'
@@ -113,7 +118,12 @@ magic <- function(data,
   if (is.numeric(verbose)) {
     verbose <- as.integer(verbose)
   }
-  if (!methods::is(data, "Matrix")) {
+  use_seurat <- FALSE
+  if (methods::is(data, "seurat")) {
+    seurat_obj <- data
+    use_seurat <- TRUE
+    data <- t(data@data)
+  } else if (!methods::is(data, "Matrix")) {
     data <- as.matrix(data)
   }
   if (is.null(genes) || is.na(genes)) {
@@ -170,13 +180,18 @@ magic <- function(data,
   result <- operator$fit_transform(data,
                                    genes = genes,
                                    t_max = t.max)
-  result <- as.data.frame(result)
   colnames(result) <- gene_names
   rownames(result) <- rownames(data)
-  result <- list("result" = result, "operator" = operator,
-                 "params" = params)
-  class(result) <- c("magic", "list")
-  return(result)
+  if (use_seurat) {
+    seurat_obj@data <- t(result)
+    return(seurat_obj)
+  } else {
+    result <- as.data.frame(result)
+    result <- list("result" = result, "operator" = operator,
+                   "params" = params)
+    class(result) <- c("magic", "list")
+    return(result)
+  }
 }
 
 

Rmagic/README.Rmd

@@ -1,5 +1,5 @@
 ---
-title : Rmagic v1.3.0
+title : Rmagic v1.4.0
 output: github_document
 toc: true
 ---

Rmagic/README.md

@@ -1,4 +1,4 @@
-Rmagic v1.3.0
+Rmagic v1.4.0
 ================
 
 <!-- README.md is generated from README.Rmd. Please edit that file -->

Rmagic/tests/test_magic.R

@@ -24,3 +24,14 @@ test_magic <- function() {
            scale_colour_viridis(option="B")
   ggsave('EMT_data_R_after_magic.png', plot=p_m, width=5, height=5)
 }
+
+test_seurat <- function() {
+  data(magic_testdata)
+
+  seurat_obj <- Seurat::CreateSeuratObject(raw.data=t(magic_testdata))
+
+  # run MAGIC
+  data_MAGIC <- magic(magic_testdata, seed = 42)
+  seurat_MAGIC <- magic(seurat_obj, seed = 42)
+  stopifnot(all(data_MAGIC$result == t(seurat_MAGIC@data)))
+}

python/doc/source/requirements.txt

@@ -5,5 +5,6 @@ scipy>=1.1.0
 matplotlib>=2.0.1
 future
 graphtools>=0.1.8
+scprep>=0.7.1
 sphinx
 sphinxcontrib-napoleon

python/magic/magic.py

@@ -521,8 +521,24 @@ def fit_transform(self, X, graph=None, **kwargs):
             If given, provides a precomputed kernel matrix with which to
             perform diffusion.
 
-        kwargs : further arguments for `PHATE.transform()`
-            Keyword arguments as specified in :func:`~phate.PHATE.transform`
+        genes : list or {"all_genes", "pca_only"}, optional (default: None)
+            List of genes, either as integer indices or column names
+            if input data is a pandas DataFrame. If "all_genes", the entire
+            smoothed matrix is returned. If "pca_only", PCA on the smoothed
+            data is returned. If None, the entire matrix is also
+            returned, but a warning may be raised if the resultant matrix
+            is very large.
+
+        t_max : int, optional, default: 20
+            maximum t to test if `t` is set to 'auto'
+
+        plot_optimal_t : boolean, optional, default: False
+            If true and `t` is set to 'auto', plot the disparity used to
+            select t
+
+        ax : matplotlib.axes.Axes, optional
+            If given and `plot_optimal_t` is true, plot will be drawn
+            on the given axis.
 
         Returns
         -------

python/magic/version.py

@@ -1 +1 @@
-__version__ = "1.3.0"
+__version__ = "1.4.0"