def loadfcs():
fname = glob.glob('*.fcs')[0]
meta, data_numpy = fcsrd.parse_fcs(fname,
meta_data_only=False,
output_format='ndarray',
reformat_meta=True)
data = meta['_channels_']
a = (data['$PnS'].values)
index = []
for name in a:
if "CD" in name:
index.insert(0, name)
index.insert(0, "abTCR")
index.insert(0, "MHCII")
index.insert(0, "B220")
index.insert(0, "Areg")
index.insert(0, "Ly6G-C")
index.insert(0, "Ly6G")
index.insert(0, "Ly6C")
index.insert(0, "BST2")
index.insert(0, "Ter119")
index.insert(0, "F4-80")
index.insert(0, "gdTCR")
index.insert(0, "SiglecF")
index = np.array(index)
surface_marker = data[data['$PnS'].isin(index)]
surface_marker_data = data_numpy[:, surface_marker.index - 1]
suface_marker_data_transformed = np.arcsinh(5 * surface_marker_data)
quantile_995 = np.percentile(suface_marker_data_transformed, 99.5, axis=0)
suface_marker_data_normalized = np.divide(
suface_marker_data_transformed,
np.tile(np.transpose(quantile_995),
(np.shape(suface_marker_data_transformed)[0], 1)))
suface_marker_data_normalized[suface_marker_data_normalized > 1] = 1
return suface_marker_data_normalized
#for i in range(1):
fname = glob.glob('F:\\cytowork\\experiment_44185_files\\*.fcs'
)[n_conditions * (i):n_conditions * (i + 1)]
# create folder
sample_name = fname[0].split('\\')
sample_name = sample_name[-1]
sample_name = sample_name.split('_')
sample_name = sample_name[0]
print(sample_name)
# os.mkdir(sample_name)
sel = range(n_conditions)
for ii in sel:
if ii == sel[0]:
meta, data_numpy = fcsrd.parse_fcs(fname[ii],
meta_data_only=False,
output_format='ndarray',
reformat_meta=True)
# surface_marker_data = data_numpy[:,surface_marker.index - 1]
# choose channel
meta_data = meta['_channels_']
channel_names = (meta_data['$PnS'].values)
selected_index = []
for name in channel_names:
if "CD" in name:
selected_index.insert(0, name)
selected_index.insert(0, "HLA-DR")
selected_index = np.array(selected_index)
surface_marker = meta_data[meta_data['$PnS'].isin(selected_index)]
surface_marker_data = data_numpy[:, surface_marker.index - 1]
surface_marker_data = np.arcsinh(surface_marker_data / 5)
surface_marker_data = np.divide(