def save(self, tile_indices, output_dir, data, compress=6):
region_index, tile_index, tx, ty = tile_indices
img_label, stats = data
# Save label volumes if present (use compression as these are often highly redundant)
label_tile_path = None
if img_label is not None:
label_tile_path = cytokit_io.get_cytometry_image_path(
region_index, tx, ty)
cytokit_io.save_tile(osp.join(output_dir, label_tile_path),
img_label,
config=self.config,
compress=compress)
# Save statistics if present
stats_path = None
if stats is not None:
# Append useful metadata to cytometry stats (align these names to those used in config.TileDims)
# and export as csv
stats.insert(0, 'tile_y', ty)
stats.insert(0, 'tile_x', tx)
stats.insert(0, 'tile_index', tile_index)
stats.insert(0, 'region_index', region_index)
stats_path = cytokit_io.get_cytometry_stats_path(
region_index, tx, ty)
cytokit_io.save_csv(osp.join(output_dir, stats_path),
stats,
index=False)
return label_tile_path, stats_path
def aggregate(config, output_dir):
"""Aggregate cytometry data associated with an experiment into a single dataframe
Args:
config: Experiment configuration
output_dir: Output directory for experiment
Returns:
DataFrame containing concatenation of all tile-based cytometry datasets with a global
cell id as well as global x/y coordinates (where "global" means across region)
"""
# Load per-tile csv exports
df = []
for idx in config.get_tile_indices():
path = cytokit_io.get_cytometry_stats_path(idx.region_index,
idx.tile_x, idx.tile_y)
path = osp.join(output_dir, path)
if not osp.exists(path):
logger.warning(
'Expected cytometry data file at "%s" does not exist. '
'It will be ignored but this may be worth investigating', path)
continue
df.append(pd.read_csv(path))
df = pd.concat(df)
# Start inserting before 'id' to get order rid, rx, ry (so they have to be inserted in reverse order)
id_idx = df.columns.tolist().index('id')
# Determine region coords for tile coordinate / point coordinate pairs
def get_region_point_coords(r):
tile_coord = r['tile_x'], r['tile_y']
tile_point = r['x'], r['y']
return config.get_region_point_coordinates(tile_coord, tile_point)
reg_coords = []
if len(df) > 0:
reg_coords = df[['tile_x', 'tile_y', 'x',
'y']].apply(get_region_point_coords, axis=1)
# Add region / global coordinates as separate fields
df.insert(id_idx, 'ry', [c[1] for c in reg_coords])
df.insert(id_idx, 'rx', [c[0] for c in reg_coords])
# Insert global id for cells (i.e. across region)
df.insert(id_idx, 'rid', np.arange(len(df)))
return df