Detect top row format for prediction and training

celltypist/classifier.py

@@ -287,7 +287,7 @@ def __init__(self, filename: Union[AnnData,str] = "", model: Union[Model,str] =
  f"🛑 The number of cells in {cell_file} does not match the number of cells in {self.filename}")
  self.adata.var_names = genes_mtx
  self.adata.obs_names = cells_mtx
- if not float(self.adata.X.max()).is_integer():
+ if not float(self.adata.X[:1000].max()).is_integer():
  logger.warn(f"⚠️ Warning: the input file seems not a raw count matrix. The prediction result may not be accurate")
  if (self.adata.n_vars >= 100000) or (len(self.adata.var_names[0]) >= 30) or (len(self.adata.obs_names.intersection(['GAPDH', 'ACTB', 'CALM1', 'PTPRC', 'MALAT1'])) >= 1):
  logger.warn(f"⚠️ The input matrix is detected to be a gene-by-cell matrix, will transpose it")
@@ -301,7 +301,7 @@ def __init__(self, filename: Union[AnnData,str] = "", model: Union[Model,str] =
  elif isinstance(filename, AnnData) or (isinstance(filename, str) and filename.endswith('.h5ad')):
  self.adata = sc.read(filename) if isinstance(filename, str) else filename
  self.adata.var_names_make_unique()
- if (self.adata.X.min() < 0) or (self.adata.X.max() > np.log1p(10000)):
+ if (self.adata.X[:1000].min() < 0) or (self.adata.X[:1000].max() > np.log1p(10000)):
  logger.info("👀 Invalid expression matrix in `.X`, expect log1p normalized expression to 10000 counts per cell; will try the `.raw` attribute")
  try:
  self.indata = self.adata.raw.X
@@ -310,7 +310,7 @@ def __init__(self, filename: Union[AnnData,str] = "", model: Union[Model,str] =
  except Exception as e:
  raise Exception(
  f"🛑 Fail to use the `.raw` attribute in the input object. {e}")
- if (self.indata.min() < 0) or (self.indata.max() > np.log1p(10000)):
+ if (self.indata[:1000].min() < 0) or (self.indata[:1000].max() > np.log1p(10000)):
  raise ValueError(
  "🛑 Invalid expression matrix in both `.X` and `.raw.X`, expect log1p normalized expression to 10000 counts per cell")
  else:
@@ -391,7 +391,7 @@ def _construct_neighbor_graph(adata: AnnData) -> tuple:
  adata.uns['log1p']['base'] = None
 
  if 'X_pca' not in adata.obsm.keys():
- if adata.X.min() < 0:
+ if adata.X[:1000].min() < 0:
  adata = adata.raw.to_adata()
  if 'highly_variable' not in adata.var:
  sc.pp.filter_genes(adata, min_cells=5)

celltypist/train.py

@@ -51,7 +51,7 @@ def _prepare_data(X, labels, genes, transpose) -> tuple:
  if isinstance(X, AnnData) or (isinstance(X, str) and X.endswith('.h5ad')):
  adata = sc.read(X) if isinstance(X, str) else X
  adata.var_names_make_unique()
- if adata.X.min() < 0:
+ if adata.X[:1000].min() < 0:
  logger.info("👀 Detected scaled expression in the input data, will try the .raw attribute")
  try:
  indata = adata.raw.X
@@ -80,7 +80,7 @@ def _prepare_data(X, labels, genes, transpose) -> tuple:
  f"🛑 The number of genes provided does not match the number of genes in {X}")
  adata.var_names = np.array(genes)
  adata.var_names_make_unique()
- if not float(adata.X.max()).is_integer():
+ if not float(adata.X[:1000].max()).is_integer():
  logger.warn(f"⚠️ Warning: the input file seems not a raw count matrix. The trained model may be biased")
  sc.pp.normalize_total(adata, target_sum=1e4)
  sc.pp.log1p(adata)