-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathKP_2PVR_select_significantPlaceFields_v2.m
More file actions
executable file
·205 lines (187 loc) · 10.9 KB
/
Copy pathKP_2PVR_select_significantPlaceFields_v2.m
File metadata and controls
executable file
·205 lines (187 loc) · 10.9 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
% IMPORTANT NOTE: always make sure fineQuantInputStruct.fieldDataCats (defined upstream) is in accordance with
% KP_2PVR_select_significantPlaceFields_v2 and KP_2PVR_assign_PFs_to_VRSectors_v2
function [baseCutoff, binarySMAPs, baseSMAPs, baseSMAPs_mean, baseSMAPs_std, surroundBorders, meanPeakWindow, peakZScores, placeFields] = KP_2PVR_select_significantPlaceFields_v2(peaksPerNeuron, valleysPerNeuron, SMAPsPerNeuron, binMeanActivity, lowPassedBinMeanActivity, percBase, peakWindow, thresh_peakZScore, placeSizeVRUnits, IDs_string, MAPs_string, outPath, baseType)
baseCutoff = NaN(1,size(SMAPsPerNeuron,3));
binarySMAPs = NaN(size(SMAPsPerNeuron,1) ,size(SMAPsPerNeuron,2), size(SMAPsPerNeuron,3));
baseSMAPs_mean = NaN(1,size(SMAPsPerNeuron,3));
baseSMAPs_std = NaN(1,size(SMAPsPerNeuron,3));
for n = 1:size(SMAPsPerNeuron,3)
SMAP = SMAPsPerNeuron(:,:,n);
if ~all(all(isnan(SMAP)))
neuron = sprintf('neuron_%i',n);
flag_empty = true;
baseCutoff(n) = prctile(SMAP,percBase,"all");
binarySMAPs(:,:,n) = SMAP > baseCutoff(n);
baseSMAPs.(neuron) = SMAP(~binarySMAPs(:,:,n));
baseSMAPs_mean(n) = mean(baseSMAPs.(neuron),"omitnan");
baseSMAPs_std(n) = std(baseSMAPs.(neuron),"omitnan");
modSMAP = SMAP;
nanRows = NaN(2,size(modSMAP,2));
modSMAP = [modSMAP;nanRows];
modSMAP = [modSMAP;binMeanActivity(n,:);binMeanActivity(n,:);binMeanActivity(n,:)];
if ismember(neuron,fieldnames(valleysPerNeuron))
refTrace = lowPassedBinMeanActivity(n,:);
valleysIdx = valleysPerNeuron.(neuron);
acqMeanValleys = mean(refTrace(valleysIdx));
yline(acqMeanValleys,'Color','blue','LineStyle',':','LineWidth',1);
yl = ylim;
if ismember(neuron,fieldnames(peaksPerNeuron))
for p = 1:size(peaksPerNeuron.(neuron),1)
peakIdx = peaksPerNeuron.(neuron)(p);
leftBorder = peakIdx - (floor(peakWindow/2));
if leftBorder <= 0
leftBorder = 1;
end
surroundBorders.(neuron)(p,1) = leftBorder;
rightBorder = peakIdx + (floor(peakWindow/2));
if rightBorder > size(SMAPsPerNeuron,2)
rightBorder = size(SMAPsPerNeuron,2);
end
surroundBorders.(neuron)(p,2) = rightBorder;
meanPeak = mean(binMeanActivity(n,leftBorder:rightBorder));
meanPeakWindow.(neuron)(p) = meanPeak;
peakZScores.(neuron)(p) = (meanPeak - baseSMAPs_mean(n))/baseSMAPs_std(n);
data = surroundBorders.(neuron);
x = [data(p,1), data(p,2), data(p,2), data(p,1)];
y = [yl(1), yl(1), yl(2), yl(2)];
if peakZScores.(neuron)(p) >= thresh_peakZScore
if baseType == "localBase"
flag_leftValley = false;
flag_rightValley = false;
end
xline(peaksPerNeuron.(neuron)(p),'Color','black','LineStyle','-','LineWidth',1.5);
fill(x,y,'green','FaceAlpha',0.3)
if flag_empty
placeFields.(neuron)(1,1) = p;
placeFields.(neuron)(1,2) = peakZScores.(neuron)(p);
placeFields.(neuron)(1,3) = peakIdx;
placeFields.(neuron)(1,4) = (peakIdx * placeSizeVRUnits) - (placeSizeVRUnits/2);
else
row = size(placeFields.(neuron),1)+1;
placeFields.(neuron)(row,1) = p;
placeFields.(neuron)(row,2) = peakZScores.(neuron)(p);
placeFields.(neuron)(row,3) = peakIdx;
placeFields.(neuron)(row,4) = (peakIdx * placeSizeVRUnits) - (placeSizeVRUnits/2);
end
if baseType == "localBase"
preValleys = valleysPerNeuron.(neuron) < peakIdx;
if ~sum(preValleys) == 0
leftValleyIdx = max(valleysPerNeuron.(neuron)(preValleys));
leftValleyHeight = refTrace(leftValleyIdx);
flag_leftValley = true;
end
postValleys = valleysPerNeuron.(neuron) > peakIdx;
if ~sum(postValleys) == 0
rightValleyIdx = min(valleysPerNeuron.(neuron)(postValleys));
rightValleyHeight = refTrace(rightValleyIdx);
flag_rightValley = true;
end
if flag_leftValley && flag_rightValley
localBase = min(leftValleyHeight,rightValleyHeight);
disp(localBase)
leftOrRight = find([leftValleyHeight rightValleyHeight] == localBase);
else
if flag_leftValley
localBase = leftValleyHeight;
elseif flag_rightValley
localBase = rightValleyHeight;
end
end
base = localBase;
elseif baseType == "globalBase"
base = acqMeanValleys;
end
height = refTrace(peakIdx) - base;
if flag_empty
placeFields.(neuron)(1,5) = height;
else
placeFields.(neuron)(row,5) = height;
end
leftIdx = peakIdx-1;
foundLeft = false;
while ~foundLeft && leftIdx >= 1
if refTrace(leftIdx) - base <= height/2
foundLeft = true;
else
leftIdx = leftIdx-1;
end
end
if foundLeft
if flag_empty
placeFields.(neuron)(1,6) = leftIdx;
placeFields.(neuron)(1,7) = refTrace(leftIdx) - base;
placeFields.(neuron)(1,8) = leftIdx * placeSizeVRUnits;
placeFields.(neuron)(1,9) = placeFields.(neuron)(1,4) - placeFields.(neuron)(1,8);
else
placeFields.(neuron)(row,6) = leftIdx;
placeFields.(neuron)(row,7) = refTrace(leftIdx) - base;
placeFields.(neuron)(row,8) = leftIdx * placeSizeVRUnits;
placeFields.(neuron)(row,9) = placeFields.(neuron)(row,4) - placeFields.(neuron)(row,8);
end
else
if flag_empty
placeFields.(neuron)(1,6:9) = NaN;
else
placeFields.(neuron)(row,6:9) = NaN;
end
end
rightIdx = peakIdx+1;
foundRight = false;
while ~foundRight && rightIdx <= size(SMAPsPerNeuron,2)
if refTrace(rightIdx) - base <= height/2
foundRight = true;
else
rightIdx = rightIdx+1;
end
end
if foundRight
if flag_empty
placeFields.(neuron)(1,10) = rightIdx;
placeFields.(neuron)(1,11) = refTrace(rightIdx) - base;
placeFields.(neuron)(1,12) = (rightIdx-1) * placeSizeVRUnits;
placeFields.(neuron)(1,13) = placeFields.(neuron)(1,12) - placeFields.(neuron)(1,4);
else
placeFields.(neuron)(row,10) = rightIdx;
placeFields.(neuron)(row,11) = refTrace(rightIdx) - base;
placeFields.(neuron)(row,12) = (rightIdx-1) * placeSizeVRUnits;
placeFields.(neuron)(row,13) = placeFields.(neuron)(row,12) - placeFields.(neuron)(row,4);
end
else
if flag_empty
placeFields.(neuron)(1,10:13) = NaN;
else
placeFields.(neuron)(row,10:13) = NaN;
end
end
if foundLeft && foundRight
if flag_empty
placeFields.(neuron)(1,14) = placeFields.(neuron)(1,9) / placeFields.(neuron)(1,13);
placeFields.(neuron)(1,15) = placeFields.(neuron)(1,12) - placeFields.(neuron)(1,8);
else
placeFields.(neuron)(row,14) = placeFields.(neuron)(row,9) / placeFields.(neuron)(row,13);
placeFields.(neuron)(row,15) = placeFields.(neuron)(row,12) - placeFields.(neuron)(row,8);
end
else
if flag_empty
placeFields.(neuron)(1,14:15) = NaN;
else
placeFields.(neuron)(row,14:15) = NaN;
end
end
if flag_empty
placeFields.(neuron)(1,16) = acqMeanValleys;
else
placeFields.(neuron)(row,16) = acqMeanValleys;
end
if flag_empty
flag_empty = false;
end
end
end
end
end
end
end
filename = 'SMAPsSignificantPeaksPerNeuron';
save([outPath, filename],'baseCutoff', 'binarySMAPs', 'baseSMAPs', 'baseSMAPs_mean', 'baseSMAPs_std', 'surroundBorders', 'meanPeakWindow', 'peakZScores', 'placeFields')
end