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annotate SingleCellDataExtraction.py @ 0:928db0f952e3 draft

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"planemo upload for repository https://github.com/ohsu-comp-bio/quantification commit a4349062e9177b5e60fb7c49115c57299e0d648d-dirty"
author perssond
date Fri, 12 Mar 2021 00:19:24 +0000
parents
children aba3655fdef0
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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 from pathlib import Path
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12 import csv
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14 import sys
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17 def MaskChannel(mask_loaded,image_loaded_z):
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18 """Function for quantifying a single channel image
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19
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20 Returns a table with CellID according to the mask and the mean pixel intensity
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21 for the given channel for each cell"""
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22 print(f'Mask loaded: {mask_loaded.shape}', file=sys.stderr)
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23 print(f'Image loaded: {image_loaded_z.shape}', file=sys.stderr)
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24 dat = measure.regionprops(mask_loaded, image_loaded_z)
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25 n = len(dat)
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26 intensity_z = np.empty(n)
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27 for i in range(n):
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28 intensity_z[i] = dat[i].mean_intensity
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29 # Clear reference to avoid memory leak -- see MaskIDs for explanation.
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30 dat[i] = None
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31 return intensity_z
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32
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34 def MaskIDs(mask):
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35 """This function will extract the CellIDs and the XY positions for each
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36 cell based on that cells centroid
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37
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38 Returns a dictionary object"""
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39
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40 dat = measure.regionprops(mask)
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41 n = len(dat)
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42
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43 # Pre-allocate numpy arrays for all properties we'll calculate.
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44 labels = np.empty(n, int)
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45 xcoords = np.empty(n)
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46 ycoords = np.empty(n)
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47 area = np.empty(n, int)
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48 minor_axis_length = np.empty(n)
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49 major_axis_length = np.empty(n)
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50 eccentricity = np.empty(n)
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51 solidity = np.empty(n)
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52 extent = np.empty(n)
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53 orientation = np.empty(n)
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54
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55 for i in range(n):
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56 labels[i] = dat[i].label
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57 xcoords[i] = dat[i].centroid[1]
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58 ycoords[i] = dat[i].centroid[0]
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59 area[i] = dat[i].area
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60 major_axis_length[i] = dat[i].major_axis_length
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61 minor_axis_length[i] = dat[i].minor_axis_length
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62 eccentricity[i] = dat[i].eccentricity
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63 solidity[i] = dat[i].solidity
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64 extent[i] = dat[i].extent
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65 orientation[i] = dat[i].orientation
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66 # By clearing the reference to each RegionProperties object, we allow it
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67 # and its cache to be garbage collected immediately. Otherwise memory
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68 # usage creeps up needlessly while this function is executing.
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69 dat[i] = None
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70
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71 IDs = {
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72 "CellID": labels,
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73 "X_centroid": xcoords,
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74 "Y_centroid": ycoords,
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75 "column_centroid": xcoords,
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76 "row_centroid": ycoords,
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77 "Area": area,
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78 "MajorAxisLength": major_axis_length,
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79 "MinorAxisLength": minor_axis_length,
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80 "Eccentricity": eccentricity,
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81 "Solidity": solidity,
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82 "Extent": extent,
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83 "Orientation": orientation,
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84 }
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85
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86 return IDs
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87
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88
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89 def PrepareData(image,z):
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90 """Function for preparing input for maskzstack function. Connecting function
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91 to use with mc micro ilastik pipeline"""
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92
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93 image_path = Path(image)
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94 print(f'{image_path} at {z}', file=sys.stderr)
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95
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96 #Check to see if image tif(f)
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97 if image_path.suffix == '.tiff' or image_path.suffix == '.tif' or image_path.suffix == '.btf':
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98 #Check to see if the image is ome.tif(f)
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99 if image.endswith(('.ome.tif','.ome.tiff')):
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100 #Read the image
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101 image_loaded_z = skimage.io.imread(image,img_num=z,plugin='tifffile')
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102 #print('OME TIF(F) found')
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103 else:
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104 #Read the image
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105 image_loaded_z = skimage.io.imread(image,img_num=z,plugin='tifffile')
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106 #print('TIF(F) found')
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107 # Remove extra axis
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108 #image_loaded = image_loaded.reshape((image_loaded.shape[1],image_loaded.shape[3],image_loaded.shape[4]))
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109
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110 #Check to see if image is hdf5
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111 elif image_path.suffix == '.h5' or image_path.suffix == '.hdf5':
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112 #Read the image
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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 ###If the hdf5 is exported from ilastik fiji plugin, the dat_name will be 'data'
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117 #Get the image data
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118 image_loaded = np.array(f[dat_name])
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119 #Remove the first axis (ilastik convention)
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120 image_loaded = image_loaded.reshape((image_loaded.shape[1],image_loaded.shape[2],image_loaded.shape[3]))
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121 ###If the hdf5 is exported from ilastik fiji plugin, the order will need to be
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122 ###switched as above --> z_stack = np.swapaxes(z_stack,0,2) --> z_stack = np.swapaxes(z_stack,0,1)
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123
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124 #Return the objects
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125 return image_loaded_z
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126
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127
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128 def MaskZstack(masks_loaded,image,channel_names_loaded):
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129 """This function will extract the stats for each cell mask through each channel
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130 in the input image
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131
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132 mask_loaded: dictionary containing Tiff masks that represents the cells in your image.
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133
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134 z_stack: Multichannel z stack image"""
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135
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136 #Get the names of the keys for the masks dictionary
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137 mask_names = list(masks_loaded.keys())
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138 #Get the CellIDs for this dataset by using only a single mask (first mask)
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139 IDs = pd.DataFrame(MaskIDs(masks_loaded[mask_names[0]]))
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140 #Create empty dictionary to store channel results per mask
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141 dict_of_chan = {m_name: [] for m_name in mask_names}
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142 #Get the z channel and the associated channel name from list of channel names
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143 print(f'channels: {channel_names_loaded}', file=sys.stderr)
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144 print(f'num channels: {len(channel_names_loaded)}', file=sys.stderr)
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145 for z in range(len(channel_names_loaded)):
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146 #Run the data Prep function
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147 image_loaded_z = PrepareData(image,z)
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148
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149 #Iterate through number of masks to extract single cell data
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150 for nm in range(len(mask_names)):
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151 #Use the above information to mask z stack
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152 dict_of_chan[mask_names[nm]].append(MaskChannel(masks_loaded[mask_names[nm]],image_loaded_z))
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153 #Print progress
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154 print("Finished "+str(z))
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155
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156 #Iterate through the rest of the masks to modify names of channels and convert to data table
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157 for nm in mask_names:
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158 #Check if this is the first mask
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159 if nm == mask_names[0]:
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160 #Create channel names for this mask
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161 new_names = [channel_names_loaded[i]+"_"+str(nm) for i in range(len(channel_names_loaded))]
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162 #Convert the channel names list and the list of intensity values to a dictionary and combine with CellIDs and XY
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163 dict_of_chan[nm] = pd.concat([IDs,pd.DataFrame(dict(zip(new_names,dict_of_chan[nm])))],axis=1)
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164 #Get the name of the columns in the dataframe so we can reorder to histoCAT convention
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165 cols = list(dict_of_chan[nm].columns.values)
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166 #Reorder the list (Move xy position to end with spatial information)
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167 cols.append(cols.pop(cols.index("X_centroid")))
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168 cols.append(cols.pop(cols.index("Y_centroid")))
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169 cols.append(cols.pop(cols.index("column_centroid")))
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170 cols.append(cols.pop(cols.index("row_centroid")))
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171 cols.append(cols.pop(cols.index("Area")))
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172 cols.append(cols.pop(cols.index("MajorAxisLength")))
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173 cols.append(cols.pop(cols.index("MinorAxisLength")))
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174 cols.append(cols.pop(cols.index("Eccentricity")))
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175 cols.append(cols.pop(cols.index("Solidity")))
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176 cols.append(cols.pop(cols.index("Extent")))
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177 cols.append(cols.pop(cols.index("Orientation")))
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178 #Reindex the dataframe with new order
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179 dict_of_chan[nm] = dict_of_chan[nm].reindex(columns=cols)
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180 #Otherwise, add no spatial information
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181 else:
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182 #Create channel names for this mask
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183 new_names = [channel_names_loaded[i]+"_"+str(nm) for i in range(len(channel_names_loaded))]
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184 #Use the above information to mask z stack
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185 dict_of_chan[nm] = pd.DataFrame(dict(zip(new_names,dict_of_chan[nm])))
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186
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187 #Concatenate all data from all masks to return
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188 dat = pd.concat([dict_of_chan[nm] for nm in mask_names],axis=1)
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189
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190 #Return the dataframe
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191 return dat
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192
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193
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194 def ExtractSingleCells(masks,image,channel_names,output):
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195 """Function for extracting single cell information from input
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196 path containing single-cell masks, z_stack path, and channel_names path."""
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197
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198 #Create pathlib object for output
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199 output = Path(output)
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200
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201 #Check if header available
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202 #sniffer = csv.Sniffer()
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203 #sniffer.has_header(open(channel_names).readline())
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204 #If header not available
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205 #if not sniffer:
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206 #If header available
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207 #channel_names_loaded = pd.read_csv(channel_names)
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208 #channel_names_loaded_list = list(channel_names_loaded.marker_name)
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209 #else:
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210 #print("negative")
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211 #old one column version
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212 #channel_names_loaded = pd.read_csv(channel_names,header=None)
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213 #Add a column index for ease
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214 #channel_names_loaded.columns = ["marker"]
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215 #channel_names_loaded = list(channel_names_loaded.marker.values)
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216
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217 #Read csv channel names
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218 channel_names_loaded = pd.read_csv(channel_names)
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219 #Check for size of columns
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220 if channel_names_loaded.shape[1] > 1:
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221 #Get the marker_name column if more than one column (CyCIF structure)
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222 channel_names_loaded_list = list(channel_names_loaded.marker_name)
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223 else:
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224 #old one column version -- re-read the csv file and add column name
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225 channel_names_loaded = pd.read_csv(channel_names, header = None)
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226 #Add a column index for ease and for standardization
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227 channel_names_loaded.columns = ["marker"]
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228 channel_names_loaded_list = list(channel_names_loaded.marker)
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229
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230 #Check for unique marker names -- create new list to store new names
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231 channel_names_loaded_checked = []
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232 for idx,val in enumerate(channel_names_loaded_list):
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233 #Check for unique value
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234 if channel_names_loaded_list.count(val) > 1:
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235 #If unique count greater than one, add suffix
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236 channel_names_loaded_checked.append(val + "_"+ str(channel_names_loaded_list[:idx].count(val) + 1))
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237 else:
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238 #Otherwise, leave channel name
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239 channel_names_loaded_checked.append(val)
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240
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241 #Clear small memory amount by clearing old channel names
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242 channel_names_loaded, channel_names_loaded_list = None, None
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243
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244 #Read the masks
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245 masks_loaded = {}
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246 #iterate through mask paths and read images to add to dictionary object
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247 for m in masks:
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248 m_full_name = os.path.basename(m)
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249 m_name = m_full_name.split('.')[0]
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250 masks_loaded.update({str(m_name):skimage.io.imread(m,plugin='tifffile')})
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251
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252 scdata_z = MaskZstack(masks_loaded,image,channel_names_loaded_checked)
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253 #Write the singe cell data to a csv file using the image name
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254
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255 im_full_name = os.path.basename(image)
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256 im_name = im_full_name.split('.')[0]
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257 scdata_z.to_csv(str(Path(os.path.join(str(output),str(im_name+".csv")))),index=False)
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258
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259
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260 def MultiExtractSingleCells(masks,image,channel_names,output):
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261 """Function for iterating over a list of z_stacks and output locations to
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262 export single-cell data from image masks"""
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263
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264 print("Extracting single-cell data for "+str(image)+'...')
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265
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266 #Run the ExtractSingleCells function for this image
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267 ExtractSingleCells(masks,image,channel_names,output)
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268
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269 #Print update
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270 im_full_name = os.path.basename(image)
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271 im_name = im_full_name.split('.')[0]
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272 print("Finished "+str(im_name))