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annotate SingleCellDataExtraction.py @ 2:46b897eb2c8e draft

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"planemo upload for repository https://github.com/ohsu-comp-bio/quantification commit 150f21e52974f99ec39bf92d9e7e611861860d0f"
author watsocam
date Wed, 30 Mar 2022 16:56:29 +0000
parents aba3655fdef0
children
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1 #Functions for reading in single cell imaging data
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2 #Joshua Hess
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4 #Import necessary modules
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5 import skimage.io
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6 import h5py
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7 import pandas as pd
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8 import numpy as np
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9 import os
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10 import skimage.measure as measure
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11 import tifffile
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13 from pathlib import Path
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15 import sys
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18 def gini_index(mask, intensity):
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19 x = intensity[mask]
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20 sorted_x = np.sort(x)
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21 n = len(x)
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22 cumx = np.cumsum(sorted_x, dtype=float)
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23 return (n + 1 - 2 * np.sum(cumx) / cumx[-1]) / n
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25 def intensity_median(mask, intensity):
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26 return np.median(intensity[mask])
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28 def MaskChannel(mask_loaded, image_loaded_z, intensity_props=["intensity_mean"]):
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29 """Function for quantifying a single channel image
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31 Returns a table with CellID according to the mask and the mean pixel intensity
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32 for the given channel for each cell"""
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33 # Look for regionprops in skimage
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34 builtin_props = set(intensity_props).intersection(measure._regionprops.PROP_VALS)
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35 # Otherwise look for them in this module
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36 extra_props = set(intensity_props).difference(measure._regionprops.PROP_VALS)
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37 dat = measure.regionprops_table(
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38 mask_loaded, image_loaded_z,
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39 properties = tuple(builtin_props),
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40 extra_properties = [globals()[n] for n in extra_props]
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41 )
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42 return dat
0
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44
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45 def MaskIDs(mask, mask_props=None):
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46 """This function will extract the CellIDs and the XY positions for each
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47 cell based on that cells centroid
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49 Returns a dictionary object"""
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50
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51 all_mask_props = set(["label", "centroid", "area", "major_axis_length", "minor_axis_length", "eccentricity", "solidity", "extent", "orientation"])
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52 if mask_props is not None:
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53 all_mask_props = all_mask_props.union(mask_props)
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55 dat = measure.regionprops_table(
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56 mask,
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57 properties=all_mask_props
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58 )
0
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60 name_map = {
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61 "CellID": "label",
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62 "X_centroid": "centroid-1",
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63 "Y_centroid": "centroid-0",
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64 "Area": "area",
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65 "MajorAxisLength": "major_axis_length",
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66 "MinorAxisLength": "minor_axis_length",
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67 "Eccentricity": "eccentricity",
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68 "Solidity": "solidity",
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69 "Extent": "extent",
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70 "Orientation": "orientation",
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71 }
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72 for new_name, old_name in name_map.items():
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73 dat[new_name] = dat[old_name]
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74 for old_name in set(name_map.values()):
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75 del dat[old_name]
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76
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77 return dat
0
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78
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79 def n_channels(image):
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80 """Returns the number of channel in the input image. Supports [OME]TIFF and HDF5."""
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81
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82 image_path = Path(image)
0
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83
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84 if image_path.suffix in ['.tiff', '.tif', '.btf']:
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85 s = tifffile.TiffFile(image).series[0]
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86 ndim = len(s.shape)
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87 if ndim == 2: return 1
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88 elif ndim == 3: return min(s.shape)
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89 else: raise Exception('mcquant supports only 2D/3D images.')
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90
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91 elif image_path.suffix in ['.h5', '.hdf5']:
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92 f = h5py.File(image, 'r')
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93 dat_name = list(f.keys())[0]
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94 return f[dat_name].shape[3]
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95
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96 else:
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97 raise Exception('mcquant currently supports [OME]TIFF and HDF5 formats only')
0
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98
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99 def PrepareData(image,z):
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100 """Function for preparing input for maskzstack function. Connecting function
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101 to use with mc micro ilastik pipeline"""
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102
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103 image_path = Path(image)
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104 print(f'{image_path} at {z}', file=sys.stderr)
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105
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106 #Check to see if image tif(f)
1
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107 if image_path.suffix in ['.tiff', '.tif', '.btf']:
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108 image_loaded_z = tifffile.imread(image, key=z)
0
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109
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110 #Check to see if image is hdf5
1
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111 elif image_path.suffix in ['.h5', '.hdf5']:
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112 #Read the image
1
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113 f = h5py.File(image,'r')
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114 #Get the dataset name from the h5 file
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115 dat_name = list(f.keys())[0]
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116 #Retrieve the z^th channel
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117 image_loaded_z = f[dat_name][0,:,:,z]
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118
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119 else:
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120 raise Exception('mcquant currently supports [OME]TIFF and HDF5 formats only')
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121
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122 #Return the objects
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123 return image_loaded_z
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124
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125
1
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126 def MaskZstack(masks_loaded,image,channel_names_loaded, mask_props=None, intensity_props=["intensity_mean"]):
0
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127 """This function will extract the stats for each cell mask through each channel
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128 in the input image
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129
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130 mask_loaded: dictionary containing Tiff masks that represents the cells in your image.
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131
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132 z_stack: Multichannel z stack image"""
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133
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134 #Get the names of the keys for the masks dictionary
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135 mask_names = list(masks_loaded.keys())
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136
0
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137 #Create empty dictionary to store channel results per mask
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138 dict_of_chan = {m_name: [] for m_name in mask_names}
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139 #Get the z channel and the associated channel name from list of channel names
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140 print(f'channels: {channel_names_loaded}', file=sys.stderr)
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141 print(f'num channels: {len(channel_names_loaded)}', file=sys.stderr)
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142 for z in range(len(channel_names_loaded)):
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143 #Run the data Prep function
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144 image_loaded_z = PrepareData(image,z)
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145
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146 #Iterate through number of masks to extract single cell data
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147 for nm in range(len(mask_names)):
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148 #Use the above information to mask z stack
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149 dict_of_chan[mask_names[nm]].append(
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150 MaskChannel(masks_loaded[mask_names[nm]],image_loaded_z, intensity_props=intensity_props)
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151 )
0
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152 #Print progress
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153 print("Finished "+str(z))
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154
1
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155 # Column order according to histoCAT convention (Move xy position to end with spatial information)
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156 last_cols = (
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157 "X_centroid",
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158 "Y_centroid",
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159 "column_centroid",
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160 "row_centroid",
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161 "Area",
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162 "MajorAxisLength",
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163 "MinorAxisLength",
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164 "Eccentricity",
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165 "Solidity",
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166 "Extent",
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167 "Orientation",
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168 )
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169 def col_sort(x):
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170 if x == "CellID":
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171 return -2
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172 try:
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173 return last_cols.index(x)
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174 except ValueError:
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175 return -1
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176
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177 #Iterate through the masks and format quantifications for each mask and property
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178 for nm in mask_names:
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179 mask_dict = {}
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180 # Mean intensity is default property, stored without suffix
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181 mask_dict.update(
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182 zip(channel_names_loaded, [x["intensity_mean"] for x in dict_of_chan[nm]])
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183 )
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184 # All other properties are suffixed with their names
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185 for prop_n in set(dict_of_chan[nm][0].keys()).difference(["intensity_mean"]):
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186 mask_dict.update(
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187 zip([f"{n}_{prop_n}" for n in channel_names_loaded], [x[prop_n] for x in dict_of_chan[nm]])
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188 )
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189 # Get the cell IDs and mask properties
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190 mask_properties = pd.DataFrame(MaskIDs(masks_loaded[nm], mask_props=mask_props))
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191 mask_dict.update(mask_properties)
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192 dict_of_chan[nm] = pd.DataFrame(mask_dict).reindex(columns=sorted(mask_dict.keys(), key=col_sort))
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193
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194 # Return the dict of dataframes for each mask
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195 return dict_of_chan
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196
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197 def ExtractSingleCells(masks,image,channel_names,output, mask_props=None, intensity_props=["intensity_mean"]):
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198 """Function for extracting single cell information from input
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199 path containing single-cell masks, z_stack path, and channel_names path."""
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200
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201 #Create pathlib object for output
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202 output = Path(output)
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203
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204 #Read csv channel names
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205 channel_names_loaded = pd.read_csv(channel_names)
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206 #Check for the presence of `marker_name` column
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207 if 'marker_name' in channel_names_loaded:
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208 #Get the marker_name column if more than one column (CyCIF structure)
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209 channel_names_loaded_list = list(channel_names_loaded.marker_name)
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210 #Consider the old one-marker-per-line plain text format
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211 elif channel_names_loaded.shape[1] == 1:
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212 #re-read the csv file and add column name
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213 channel_names_loaded = pd.read_csv(channel_names, header = None)
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214 channel_names_loaded_list = list(channel_names_loaded.iloc[:,0])
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215 else:
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216 raise Exception('%s must contain the marker_name column'%channel_names)
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217
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218 #Contrast against the number of markers in the image
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219 if len(channel_names_loaded_list) != n_channels(image):
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220 raise Exception("The number of channels in %s doesn't match the image"%channel_names)
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221
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222 #Check for unique marker names -- create new list to store new names
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223 channel_names_loaded_checked = []
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224 for idx,val in enumerate(channel_names_loaded_list):
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225 #Check for unique value
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226 if channel_names_loaded_list.count(val) > 1:
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227 #If unique count greater than one, add suffix
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228 channel_names_loaded_checked.append(val + "_"+ str(channel_names_loaded_list[:idx].count(val) + 1))
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229 else:
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230 #Otherwise, leave channel name
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231 channel_names_loaded_checked.append(val)
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232
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233 #Read the masks
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234 masks_loaded = {}
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235 #iterate through mask paths and read images to add to dictionary object
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236 for m in masks:
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237 m_full_name = os.path.basename(m)
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238 m_name = m_full_name.split('.')[0]
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239 masks_loaded.update({str(m_name):skimage.io.imread(m,plugin='tifffile')})
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240
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241 scdata_z = MaskZstack(masks_loaded,image,channel_names_loaded_checked, mask_props=mask_props, intensity_props=intensity_props)
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242 #Write the singe cell data to a csv file using the image name
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243
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244 im_full_name = os.path.basename(image)
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245 im_name = im_full_name.split('.')[0]
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246
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247 # iterate through each mask and export csv with mask name as suffix
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248 for k,v in scdata_z.items():
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249 # export the csv for this mask name
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250 scdata_z[k].to_csv(
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251 str(Path(os.path.join(str(output),
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252 str(im_name+"_{}"+".csv").format(k)))),
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253 index=False
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254 )
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255
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256
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257 def MultiExtractSingleCells(masks,image,channel_names,output, mask_props=None, intensity_props=["intensity_mean"]):
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258 """Function for iterating over a list of z_stacks and output locations to
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259 export single-cell data from image masks"""
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260
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261 print("Extracting single-cell data for "+str(image)+'...')
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262
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263 #Run the ExtractSingleCells function for this image
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264 ExtractSingleCells(masks,image,channel_names,output, mask_props=mask_props, intensity_props=intensity_props)
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265
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266 #Print update
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267 im_full_name = os.path.basename(image)
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268 im_name = im_full_name.split('.')[0]
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269 print("Finished "+str(im_name))