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firing_vs_light_polygon_SCRIPT.m
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%{
DOCUMENTATION
Created: 2022 07 21
Author: PA
This function is used to analyze light-evoked changes in firing rate in
sCRACM experiments using the polygon.
This function was derived from firing_vs_light_ch, but went through A LOT
of rewritting.
INPUTS explained:
- gridSize: number of columns in the square grid
- discardedSweeps: specific sweeps that should NOT be analyzed due to
artifacts. If I want to discard sweep 0024, just write 24.
- discardedSweepsFromEnd: number of sweeps from the end that will not be
analyzed. Used to remove incomplete sweeps or sweeps with artifacts.
- peaksOrValleys: find APs based on found peaks ('peaks') or valleys
('v').
- highpassThreshold: bandpass filter parameter - attenuates low
frequency noise below this frequency. Removes baseline drift.
- lowpassThreshold: bandpass filter parameter - attenuates high
frequency noise above this frequency. Removes fast artifacts (such as
light stim onset/offset and outflow bubbles). Should be > 1000 to avoid
sinusoidal artifacts. Use 2000 for striatum cells.
- minPeakHeight: amplitude threshold for finding APs - detect peaks or
valleys above/below this amplitude threshold.
- minPeakDistance: min distance in seconds between found peaks. Used to
ignore fast artifacts that cross the minPeakHeight threshold. Note that
this input will cap the max firing frequency output. In other words: to
further discard high frequency noise, only look for peaks that are this
time interval apart (note that this interval caps the max signal
frequency you can detect).
- lightExtensionFactor: to account for lingering effects after the end
of the light pulse, extend the time interval considered under "light
effect". To NOT extend the light pulse (my default), set this variable
to 1.
- lightChannel: channel where the info about the light stim is stored.
Usually 2 or 3.
- singleLightPulse: boolean variable (0 or 1) determining whether this
sweep has a single light pulse (1) or a train of light pulses (0).
Added to avoid errors
- ymax: for illustration purposes, use this value as the max range for
the y-axis when plotting current vs time.
- ymaxhist: for illustration purposes, use this value as the max range
for the y-axis in the firing rate histogram (max instantaneous firing
frequency).
- zoomWindow: time before/after LightOnsetTime that will be shown in
the zoomed plot.
- ymaxIsiCV: for illustration purposes, use this value as the max range
for the y-axis in the ISI histogram.
- heatmapMin: sets min value for color-coding the heatmap
- heatmapMax: sets max value for color-coding the heatmap
- cellImageFileName:
- cellImageDir: folder where cell image file is located
- savefileto: save CSV files to this folder.
INPUTS defaults:
discardedSweeps = [];
discardedSweepsFromEnd = 0;
peaksOrValleys = 'v';
highpassThreshold = 100;
lowpassThreshold = 1500;
minPeakHeight = 15;
minPeakDistance = 0.025;
lightExtensionFactor = 1;
lightChannel = 2;
singlelightpulse = 0;
preAPinSeconds = 0.005;
postAPinSeconds = 0.01;
preAPbaselineDurationSeconds = 0.002;
ddyValleyThreshold = 30;
ymax = 75;
ymaxhist = 15;
ZoomWindow = 0.25;
ymaxIsiCV = 150;
heatmapMin = -2;
heatmapMax = 0;
cellImageFileName = 's2c1_dic2.tif';
cellImageDir = 'E:\Priscilla - BACKUP 20200319\Ephys\2022\20220720 m571 dat nphr';
savefileto = 'D:\CORONAVIRUS DATA\From MATLAB';
OUTPUTS:
Fig
XLS
ASSUMPTIONS:
- Recording was done in cell attached or loose cell mode in VClamp -
bandpass filter is appropriate.
- For Avg XLS file: light stim parameters are the same in all sweeps.
- Dopaminergic cells have total AP duration > 2 ms.
- Irregular cells have ISI CV > 0.2
- An ordered polygon grid design was used - aka not random
- Width and frequency of polygon stim is the the same as LED o-stim
(only relevant if you use the polygon channel as the light stim
channel)
BEWARE:
- if you add more variables into "data" for exporting, you need to
adjust the code at multiple places. i think I finally fixed this
TO DO:
- create 'test' version with sweep by sweep plots - DONE
- complete documentation - ALMOST DONE
%}
% function firing_vs_light_polygon(obj)
obj = m571.s0138;
%% USER INPUT ============================================================
% Affects data analysis - Organizing data by o-stim grid
gridColumns = 5;
gridRows = 5;
% Affects data analysis - Finding APs:
discardedSweeps = [157,158];
discardedSweepsFromEnd = 0;
peaksOrValleys = 'v';
highpassThreshold = 100;
lowpassThreshold = 1500;
minPeakHeight = 20;
minPeakDistance = 0.001;
lightExtensionFactor = 1;
lightChannel = 2;
singleLightPulse = 1;
% Affects data analysis - AP shape:
preAPinSeconds = 0.005;
postAPinSeconds = 0.01;
preAPbaselineDurationSeconds = 0.002;
ddyValleyThreshold = 600;
% Affects data display:
ymax = 200;
ymaxhist = 15;
zoomWindow = 0.25;
ymaxIsiCV = 150;
heatmapMin = -2;
heatmapMax = 0;
cellImageFileName = 's2c1_dic2.tif';
cellImageDir = 'E:\Priscilla - BACKUP 20200319\Ephys\2022\20220720 m571 dat nphr';
% Affects data saving:
savefileto = 'R:\Basic_Sciences\Phys\Lerner_Lab_tnl2633\Priscilla\Data summaries\2022\2022-08-11 polygon';
%% PREP - get monitor info for plot display organization =====================================================
monitorPositions = get(0, 'MonitorPositions' );
maxHeight = monitorPositions(1,4) - 100;
maxWidth = monitorPositions(1,3) - 100;
%% PREP - get info from h5 file and create arrays ============================================================
% get today's date for naming output files
analysisDate = datestr(datetime('today'),'yyyy-mm-dd');
% getting info from h5 file
mouseNumber = getMouseNumber(obj);
experimentDate = getExperimentDate(obj);
samplingFrequency = obj.header.Acquisition.SampleRate;
fileName = obj.file;
sweepDurationInSec = obj.header.SweepDuration;
sweepDurationInDataPts = sweepDurationInSec * samplingFrequency;
% add 'subset' to fileName in case discardedSweeps is not empty
% to prevent overwritting of saved files with the whole dataset
if ~isempty(discardedSweeps)
fileName = strcat(obj.file, '_subset');
end
% getting sweep numbers from file name
[firstSweepNumber, lastSweepNumber, allSweeps] = getSweepNumbers(obj);
% checking for incomplete sweeps and not analyzing incomplete sweeps - to
% avoid this error: "Index exceeds the number of array elements (0)".
if numel(fieldnames(obj.sweeps)) <= obj.header.NSweepsPerRun
lastSweepNumber = firstSweepNumber + numel(fieldnames(obj.sweeps)) -1 -discardedSweepsFromEnd;
allSweeps = firstSweepNumber:lastSweepNumber;
end
% now checking if the total number of sweeps is a multiple of the total
% number of squares. If not, remove the extra sweeps from the end.
totalSweeps = length(allSweeps);
totalSquares = gridColumns * gridRows;
sweepsPerSquare = totalSweeps / totalSquares;
if mod(totalSweeps, totalSquares) ~= 0
lastSweepNumber = lastSweepNumber - mod(totalSweeps, totalSquares);
allSweeps = firstSweepNumber:lastSweepNumber;
totalSweeps = length(allSweeps);
sweepsPerSquare = totalSweeps / totalSquares;
end
% creating matrixes/arrays that will be filled
yFilteredAll = [];
allTimeStamps = [];
baselineTimeStamps = [];
allBaselineIsi = [];
hzBaselineBySweep = [];
hzPreLightBySweep = [];
hzDuringLightBySweep = [];
hzPostLightBySweep = [];
isiMeanBySweep = [];
isiStdBySweep = [];
isiCvBySweep = [];
isIrregularBySweep = [];
data = [];
isDiscardedBySweep = [];
% creating matrixes/arrays that will be filled for AP shape
xSubset = [];
ySubsetForAPshape = [];
yFilteredSubset = [];
xSubsetAll = [];
ySubsetAll = [];
yFilteredSubsetAll = [];
nAPtotal = 0;
% creating cell arrays that will be filled (can take columns of different sizes)
tsBySweep = {};
isiBySweep = {};
sweepNumberArrayBySweep = {};
%% SWEEP BY SWEEP ANALYSIS ===================================================================================
% get data from all sweeps in file
for sweepNumber = allSweeps
% ignore discarded sweeps
if ismember(sweepNumber, discardedSweeps)
isDiscarded = 1;
yFiltered = NaN(sweepDurationInDataPts, 1);
pks = NaN;
locs = NaN;
% analyze all other sweeps
else
isDiscarded = 0;
% get raw data
[x,y] = obj.xy(sweepNumber, 1);
% filter data
yFiltered = bandpass(y,[highpassThreshold lowpassThreshold],samplingFrequency);
% find peaks or valleys based on user input
if peaksOrValleys == 'peaks'
[pks,locs,w,p] = findpeaks(yFiltered,x,'MinPeakHeight',minPeakHeight,'MinPeakDistance',minPeakDistance);
else
[pks,locs,w,p] = findpeaks(-yFiltered,x,'MinPeakHeight',minPeakHeight,'MinPeakDistance',minPeakDistance);
end
end
% store info about which sweeps were discarded
isDiscardedBySweep = [isDiscardedBySweep; isDiscarded];
%----------------------------------------------------------------------
% get light stim data
[xch2,ych2] = obj.xy(sweepNumber, lightChannel);
% get light stim parameters
% if you are using a TTL pulse (5V) to control the LED, use code #1
% if you are using an analog output (0-5V), use code #2
% ALERT let's just use the polygon TTL pulse for now, since the analog
% output is soooo small, making the detection of the light pulse start
% and end times really hard with simple methods.
% code #1 - works
% look for a big change
lightPulseStart = find(diff(ych2>1)>0);
lightPulseEnd = find(diff(ych2<1)>0);
% % code #2 - does not always work
% % look for a small change
% % to avoid artifacts, use both the derivative and the absolute value of
% % ych2 to find the start and end times of each light pulse
% % ALERT need to check this code with a train o-stim
% lightPulseStart = find(diff(ych2)>0.075 & ych2(1:end-1)<0.05);
% lightPulseEnd = find(diff(ych2)<-0.075 & ych2(1:end-1)>0.05);
% continue to get light stim info
lightOnsetTime = lightPulseStart(1)/samplingFrequency; % in seconds
stimDur = (lightPulseEnd(end)-lightPulseStart(end))/samplingFrequency; % duration of each light pulse in the train (s)
% if the light stim is a train (singleLightPulse = 0), compute light
% train information.
if singleLightPulse == 0
stimInterval = (lightPulseStart(2)-lightPulseStart(1))/samplingFrequency; % interval between each pulse (s)
stimFreq = 1/stimInterval; % frequency of the light stim (Hz)
lightDur = (lightPulseStart(end)-lightPulseStart(1))/samplingFrequency + stimInterval; % duration of the whole light train stim (s)
% if the light stim is a single pulse (singleLightPulse = 1), set the
% train information to the following values (to avoid errors)
else
stimInterval = 0;
stimFreq = 1;
lightDur = stimDur;
end
%----------------------------------------------------------------------
% save data for niceplot
% y data for each sweep is in a column
yFilteredAll = [yFilteredAll, yFiltered];
%----------------------------------------------------------------------
% storing all AP timestamps
allTimeStamps = [allTimeStamps; locs];
% getting all of the APs timestamps prior to light stim (aka full
% baseline, from 0s to lightOnsetTime)
% to calculate mean baseline Hz and SD later
locsBaseline = locs;
indicesToDelete = find(locs >= lightOnsetTime);
locsBaseline(indicesToDelete) = [];
baselineTimeStamps = [baselineTimeStamps; locsBaseline];
%----------------------------------------------------------------------
% Getting peak/valley amplitudes pre, during and post light
% to do manual quality control of found peaks later
if peaksOrValleys == 'peaks'
pksPreLight = pks;
pksDuringLight = pks;
pksPostLight = pks;
peaksOrValleysAsNum = 1;
else
pksPreLight = -pks;
pksDuringLight = -pks;
pksPostLight = -pks;
peaksOrValleysAsNum = -1;
end
%----------------------------------------------------------------
% Getting timestamps pre, during, and post light
% locsPreLight will be different from locsBaseline: locsPreLight is
% the baseline immediately before the light stim (from
% lightOnsetTime-lightDur to lightOnsetTime), while locsBaseline is
% the full baseline (from 0s to lightOnsetTime).
% first, store locs (timestamps) in new variables
locsPreLight = locs;
locsDuringLight = locs;
locsPostLight = locs;
% second, find indices at which the value of locs is smaller than the
% light onset or larger than the light offset (with optional extension
% factor to look for lingering light effect).
indicesToDelete = find(locs<lightOnsetTime | locs>(lightOnsetTime+lightDur*lightExtensionFactor));
% then, delete all peaks found before or after the light pulse
locsDuringLight(indicesToDelete) = [];
pksDuringLight(indicesToDelete) = [];
% third, find indices for timestamps beyond the immediate pre-light
% period and delete unwanted peaks
indicesToDelete = find(locs<lightOnsetTime-lightDur | locs>=lightOnsetTime);
locsPreLight(indicesToDelete) = [];
pksPreLight(indicesToDelete) = [];
% fourth, find indices for timestamps beyond the immediate post-light
% period and delete unwanted peaks
indicesToDelete = find(locs<(lightOnsetTime+lightDur*lightExtensionFactor) | locs>(lightOnsetTime+(lightDur*lightExtensionFactor)+lightDur));
locsPostLight(indicesToDelete) = [];
pksPostLight(indicesToDelete) = [];
%----------------------------------------------------------------
% check if there are any NaNs in locs - if so, this is data from a
% discarded sweep, and we will signal that by assigning NaNs to all
% stored variables.
% among the stored variables we have all the ISIs (inter-spike-intervals)
% prior to light stim.
% These ISIs will be used to plot the ISI histogram and calculate ISI CV
if any(isnan(locs))
hzBaseline = NaN;
hzPreLight = NaN;
hzDuringLight = NaN;
hzPostLight = NaN;
isiBaseline = NaN;
isiCv = NaN;
isiMean = NaN;
isiStd = NaN;
else
hzBaseline = length(locsBaseline)/lightOnsetTime;
hzPreLight = length(locsPreLight)/lightDur;
hzDuringLight = length(locsDuringLight)/lightDur;
hzPostLight = length(locsPostLight)/lightDur;
isiBaseline = diff(locsBaseline);
isiCv = std(isiBaseline)/mean(isiBaseline);
isiMean = mean(isiBaseline);
isiStd = std(isiBaseline);
end
% storing sweep by sweep data in arrays for easy mean & std calculations later
hzBaselineBySweep = [hzBaselineBySweep; hzBaseline];
hzPreLightBySweep = [hzPreLightBySweep; hzPreLight];
hzDuringLightBySweep = [hzDuringLightBySweep; hzDuringLight];
hzPostLightBySweep = [hzPostLightBySweep; hzPostLight];
allBaselineIsi = [allBaselineIsi; isiBaseline];
isiCvBySweep = [isiCvBySweep, isiCv];
isiMeanBySweep = [isiMeanBySweep, isiMean];
isiStdBySweep = [isiStdBySweep, isiStd];
% create list of sweepNumber with the same size as list of timestamps
% to organize raster plot
sweepNumberArray = sweepNumber.* ones(length(locs),1);
% storing sweep by sweep data in a cell array
% to export later
% fyi access sweep 1 data using cell2mat(tsBySweep(1))
tsBySweep = [tsBySweep, locs];
isiBySweep = [isiBySweep, isiBaseline];
sweepNumberArrayBySweep = [sweepNumberArrayBySweep, sweepNumberArray];
%----------------------------------------------------------------
% checking if cell is irregular (ISI CV > 0.2)
% ASSUMPTION ALERT, MIGHT NEED UPDATING
if isiCv > 0.2
isIrregular = 1;
elseif isiCv <= 0.2
isIrregular = 0;
else
isIrregular = NaN;
end
isIrregularBySweep = [isIrregularBySweep, isIrregular];
%----------------------------------------------------------------
% Collecting AP shape data
% Store total number of APs found in complete baseline period
nAP = size(locsBaseline,1);
% Store data surrounding each AP
% Exclude first and last AP to avoid error - MATLAB will complain if
% the required time window around each AP is beyond window of data
% available.
for iAP = 2:(nAP-1)
% convert time points to data points
xminForAPshape = round(samplingFrequency * (locsBaseline(iAP) - preAPinSeconds));
xmaxForAPshape = round(samplingFrequency * (locsBaseline(iAP) + postAPinSeconds));
xbaselineForAPshape = round(xminForAPshape + samplingFrequency*preAPbaselineDurationSeconds);
% make data rows
ySubsetForAPshape = transpose(y(xminForAPshape:xmaxForAPshape));
% calculate mean y during user-defined baseline
ySubsetBaseline = mean(y(xminForAPshape:xbaselineForAPshape));
% calculate baseline subtracted y
ySubsetBaselineSubtracted = ySubsetForAPshape - ySubsetBaseline;
% Store all data
ySubsetAll = [ySubsetAll; ySubsetBaselineSubtracted];
end
nAPtotal = nAPtotal + nAP;
%----------------------------------------------------------------
% Data that will be exported
% Storing a subset of sweep by sweep data that will be exported
data = [data; ...
mouseNumber, ...
experimentDate, ...
sweepNumber, ...
isDiscarded, ...
gridColumns, ...
gridRows, ...
sweepsPerSquare, ...
discardedSweepsFromEnd, ...
peaksOrValleysAsNum, ...
highpassThreshold, ...
lowpassThreshold, ...
minPeakHeight, ...
minPeakDistance, ...
lightExtensionFactor, ...
lightChannel, ...
singleLightPulse, ...
stimDur, ...
stimFreq, ...
lightDur, ...
hzBaseline, ...
isiMean, ...
isiStd, ...
isiCv, ...
isIrregular, ...
length(locsPreLight), ...
length(locsDuringLight), ...
length(locsPostLight), ...
hzPreLight, ...
hzDuringLight, ...
hzPostLight, ...
];
%----------------------------------------------------------------
end
%% CELL ANALYSIS - firing (all sweeps, irrespective of square) ===============================================
% Mean and Std for pre-light baseline firing rate
hzPreLightMean = mean(hzPreLightBySweep, 'omitnan');
hzPreLightStd = std(hzPreLightBySweep, 'omitnan');
% counting APs accross ALL SWEEPS
edges = [0:30];
[N, ~] = histcounts(allTimeStamps,edges);
firingHz = N/length(allSweeps);
% is firing modulated by light?
% if cell is inhibited, lightEffect = -1
% if cell is excited, lightEffect = 1
% if cell is indifferent, lightEffect = 0
lightEffectBySweep = [];
sdFromPreLightHzBySweep = [];
for sweepNumber = [1:length(allSweeps)]
% calculate change in firing as standard deviations from pre-light
% baseline mean
sdFromPreLightHz = (hzDuringLightBySweep(sweepNumber) - hzPreLightMean) / hzPreLightStd;
% store sweep-by-sweep change in firing
sdFromPreLightHzBySweep = [sdFromPreLightHzBySweep; sdFromPreLightHz];
% check if this is a discarded sweep. If so, assign NaN to stored
% variables
if isDiscardedBySweep(sweepNumber) == 1
lightEffectBySweep = [lightEffectBySweep; NaN];
% if this is not a discarded sweep, assign a "boolean" value to the
% variable lightEffect. -1 means that the cell was supressed. +1 means
% that the cell was excited. 0 means that the cell did not change its
% firing rate
elseif sdFromPreLightHz < -2
lightEffectBySweep = [lightEffectBySweep; -1];
elseif sdFromPreLightHz > 2
lightEffectBySweep = [lightEffectBySweep; +1];
else
lightEffectBySweep = [lightEffectBySweep; 0];
end
end
% add lightEffect and sdFromPreLightHz as the last columns of the sweep by sweep data
data = [data, lightEffectBySweep, sdFromPreLightHzBySweep];
% Check if cell is irregular
isIrregularCell = median(isIrregularBySweep, 'omitnan');
%% CELL ANALYSIS - AP shape (all sweeps, irrespective of square)==============================================
% calculate average AP shape/trace
avgAP = mean(ySubsetAll);
%--------------------------------------------------------------------------
% create x axis for plotting AP shape
xSubset = 1000*linspace(0,(preAPinSeconds + postAPinSeconds), length(ySubsetForAPshape));
%--------------------------------------------------------------------------
% find AP peak and valley
avgAPpeakInDataPoints = find(avgAP==max(avgAP));
avgAPvalleyInDataPoints = find(avgAP==min(avgAP));
avgAPpeakInMilliSeconds = xSubset(avgAPpeakInDataPoints);
avgAPvalleyInMilliSeconds = xSubset(avgAPvalleyInDataPoints);
%--------------------------------------------------------------------------
% calculating derivatives and creating xAxis to plot derivatives.
% for each derivative, the xAxis length decreases by 1 point.
xForDy = xSubset;
xForDy(end) = []; % remove last point from xAxis
dy = diff(avgAP)./diff(xSubset);
ddy = diff(dy)./diff(xForDy);
xForDdy = xForDy;
xForDdy(end) = []; % remove last point from xAxis
%--------------------------------------------------------------------------
% Find AP ONset based on 2nd derivative - ddy == min of first valley
[pks1,locs1,w,p] = findpeaks(-ddy,xForDdy,'MinPeakHeight',ddyValleyThreshold);
ddyBasedOnsetInMilliSeconds = locs1(1);
ddyBasedOnsetInDataPoints = round(ddyBasedOnsetInMilliSeconds*(samplingFrequency/1000));
%--------------------------------------------------------------------------
% Find AP offset based on avgAP==0 after peak
% Assumes that AP valley precedes the AP peak
avgAPafterPeak = avgAP;
avgAPafterPeak(1:avgAPpeakInDataPoints) = [];
avgAPoffsetInDataPoints = avgAPpeakInDataPoints + find(round(avgAPafterPeak)==0, 1);
avgAPoffsetInMilliSeconds = xSubset(avgAPoffsetInDataPoints);
% To avoid errors in the CSV in case matlab fails to find the offset:
if isempty(find(round(avgAPafterPeak)==0, 1))
avgAPoffsetInMilliSeconds = xSubset(end);
avgAPoffsetInDataPoints = length(xSubset);
end
%--------------------------------------------------------------------------
% Calculate AP width and duration based on multiple criteria
halfWidth = avgAPpeakInMilliSeconds - avgAPvalleyInMilliSeconds;
biphasicDuration = avgAPpeakInMilliSeconds - ddyBasedOnsetInMilliSeconds;
% I am commenting this duration calculation out because the
% ddyBasedOffsetInMilliSeconds is often incorrect and it takes one extra
% uneccessary user input.
% totalDurationDdyBased = ddyBasedOffsetInMilliSeconds - ddyBasedOnsetInMilliSeconds;
totalDurationAvgBased = avgAPoffsetInMilliSeconds - ddyBasedOnsetInMilliSeconds;
%--------------------------------------------------------------------------
% Check if AP duration is consistent with DA cell
% DA cells have total duration > 2 ms
% To be conservative, I'm taking the average between my two duration
% metrics: if (totalDurationDdyBased + totalDurationAvgBased)/2 > 2
% UPDATE Aug 30 2022: don't take the average. Just ignore totalDurationDdyBased.
if totalDurationAvgBased > 2
isDA = 1;
else
isDA = 0;
end
%--------------------------------------------------------------------------
% store AP shape data
% cannot add to sweep by sweep data cuz it's just 1 row
dataAPshape = [mouseNumber, ...
experimentDate, ...
firstSweepNumber, ...
lastSweepNumber, ...
discardedSweepsFromEnd, ...
peaksOrValleysAsNum, ...
highpassThreshold, ...
lowpassThreshold, ...
minPeakHeight, ...
minPeakDistance, ...
preAPinSeconds, ...
postAPinSeconds, ...
preAPbaselineDurationSeconds, ...
ddyValleyThreshold, ...
nAPtotal, ...
ddyBasedOnsetInMilliSeconds, ...
avgAPvalleyInMilliSeconds, ...
avgAPpeakInMilliSeconds, ...
avgAPoffsetInMilliSeconds, ...
halfWidth, ...
biphasicDuration, ...
totalDurationAvgBased, ...
isDA];
%% CELL ANALYSIS - POLYGON SPECIFIC CODE - data for each square ==============================================
% ALERT: I will have to adjust this code to account for removed sweeps!
% Store cell-specific data
dataCell = [mouseNumber, ...
experimentDate, ...
firstSweepNumber, ...
lastSweepNumber, ...
length(allSweeps) - length(discardedSweeps), ...
gridColumns, ...
gridRows, ...
peaksOrValleysAsNum, ...
highpassThreshold, ...
lowpassThreshold, ...
minPeakHeight, ...
minPeakDistance, ...
lightExtensionFactor, ...
lightChannel, ...
singleLightPulse, ...
heatmapMin, ...
heatmapMax, ...
stimDur, ...
stimFreq, ...
lightDur, ...
halfWidth, ...
totalDurationAvgBased, ...
mean(hzBaselineBySweep, 'omitnan'), ...
std(hzBaselineBySweep, 'omitnan'), ...
std(hzBaselineBySweep, 'omitnan')/mean(hzBaselineBySweep, 'omitnan'), ...
mean(allBaselineIsi, 'omitnan'), ...
std(allBaselineIsi, 'omitnan'), ...
std(allBaselineIsi, 'omitnan')/mean(allBaselineIsi, 'omitnan'), ...
isDA, ...
isIrregularCell];
% Assigning sweeps to each o-stim area (square)
% Each row in sweepIDperSquare correspond to a square
% Each column in sweepIDperSquare corresponds to a repeat sweep in that
% square.
totalSquares = gridColumns * gridRows;
relativeSweepNumberPerSquare = []; % from 1 to totalSquares
absoluteSweepNumberPerSquare = []; % from 1st sweep to last sweep number
dataSquare = [];
dataCellSingleRow = dataCell;
% to properly plot histograms later, I need to keep track of sweeps per
% square taking into consideration any discarded sweeps. I will call this
% variable sweepsPerSquarePerSquare
sweepsPerSquarePerSquareAll = [];
% each row is a square
for row=[1:totalSquares]
% create/erase arrays that will be filled for each square
dataSubsetForMeanAndSD = [];
dataSubsetForMode = [];
dataSubsetForMedian = [];
% each column is a sweep
for column=[1:sweepsPerSquare]
% store relative sweepID (1 to total # of sweeps)
relativeSweepNumber = row + totalSquares * (column - 1);
relativeSweepNumberPerSquare(row,column) = relativeSweepNumber;
% store absolute sweepID (firstSweepNumber to lastSweepNumber)
absoluteSweepNumber = relativeSweepNumber + firstSweepNumber - 1;
absoluteSweepNumberPerSquare(row,column) = absoluteSweepNumber;
% ignore data from discarded sweeps
if ismember(absoluteSweepNumber, discardedSweeps)
disp('heyo I blocked trash sweep from polygon data');
% save data from other sweeps
else
dataSubsetForMeanAndSD = [dataSubsetForMeanAndSD; ...
hzPreLightBySweep(relativeSweepNumber), ...
hzDuringLightBySweep(relativeSweepNumber), ...
hzPostLightBySweep(relativeSweepNumber)];
dataSubsetForMode = [dataSubsetForMode; ...
lightEffectBySweep(relativeSweepNumber)];
dataSubsetForMedian = [dataSubsetForMedian; ...
sdFromPreLightHzBySweep(relativeSweepNumber)];
end
end
% store number of sweeps analyzed for each square
sweepsPerSquarePerSquare = size(dataSubsetForMeanAndSD,1);
sweepsPerSquarePerSquareAll = [sweepsPerSquarePerSquareAll; sweepsPerSquarePerSquare];
% store data for each square
dataSquare = [dataSquare; ...
sweepsPerSquarePerSquare, ...
mean(dataSubsetForMeanAndSD), ...
std(dataSubsetForMeanAndSD), ...
mode(dataSubsetForMode), ...
median(dataSubsetForMedian)];
% store square-by-square data together with cell data
dataCellSingleRow = [dataCellSingleRow, ...
sweepsPerSquarePerSquare, ...
mean(dataSubsetForMeanAndSD), ...
std(dataSubsetForMeanAndSD), ...
mode(dataSubsetForMode), ...
median(dataSubsetForMedian)];
end
% concatenate dataSquare with absoluteSweepNumberPerSquare and a column
% vector with square ID numbers
dataSquareWithSweeps = [[1:totalSquares]', absoluteSweepNumberPerSquare, dataSquare];
% concatenate dataSquareWithSweeps with dataCell
% use repmat to copy dataCell into multiple rows - as many rows as there
% are squares
dataCellMultipleRows = [repmat(dataCell,totalSquares,1), dataSquareWithSweeps];
%% PLOT 1 - cropped cell image ===============================================================================
% make sure you're getting the image taken with zoom = 1
% concatenate strings from user input to get full path to figure file
cellImageFileDir = strcat(cellImageDir,'\',cellImageFileName);
% import image
cellImage = imread(cellImageFileDir);
% crop image according to the polygon's mirror calibration
% I verified this in PPT lol
% the original image is 1376 x 1024 pixels
% ASSUMPTION ALERT: the calibration of the polygon will not change over time
% I need to crop the top 100 pixels and the bottom 51 pixels
% imcrop determines the rectangle to keep in the following form: [XMIN YMIN WIDTH HEIGHT]
% note that y increases from top to bottom, so ymin should match my
% required "top crop".
% I do not need to crop along the x axis, so xmin = 1 and width = 1376
% the height is 1024 - 100 - 51 = 873
croppedImage = imcrop(cellImage, [1,100,1376,873]);
figure('name', strcat(fileName, '_', analysisDate, ' - firing_vs_light_polygon - cell image'));
imshow(croppedImage, 'Border', 'tight');
% get inner figure size and store half of those values
pos = get(gcf, 'InnerPosition'); %// gives x left, y bottom, width, height
innerWidth = pos(3)/2;
innerHeight = pos(4)/2;
% get outer figure size and store half of those values
pos = get(gcf, 'OuterPosition'); %// gives x left, y bottom, width, height
outerWidth = pos(3)/2;
outerHeight = pos(4)/2;
% set figure size to the stored values
set(gcf,'InnerPosition',[0 maxHeight-innerHeight innerWidth innerHeight]);
%% PLOT 2 - Polygon Heatmap 1 ================================================================================
% re-organize data as a grid for heatmap
% the " .' " at the end makes sure that the sweeps are placed in the
% correct place in the grid, given an ordered polygon design - aka in each
% sweep the light moves one square to the right, or to the first square in
% the next row when it reached the end of the current row.
dataForHeatmap = reshape(dataSquare(:,end),gridColumns,[]).';
% plot heatmap as an actual heatmap
figure('name', strcat(fileName, '_', analysisDate, ' - firing_vs_light_polygon - heatmap 1'));
% set the ColorLimits for consistent color-coding accross cells/mice
% set the ColorMap to a flipped map with "flipud", so that more negative
% values will be "hotter" and values closer to zero will be "colder".
% remove labels from each square with CellLabelColor = 'none'.
h = heatmap(dataForHeatmap, 'ColorLimits', [heatmapMin,heatmapMax], 'ColorMap', flipud(parula), 'CellLabelColor', 'none');
% removing labels from x axis
cdl = h.XDisplayLabels; % Current Display Labels
h.XDisplayLabels = repmat(' ',size(cdl,1), size(cdl,2)); % Blank Display Labels
% removing labels from y axis
cdl = h.YDisplayLabels; % Current Display Labels
h.YDisplayLabels = repmat(' ',size(cdl,1), size(cdl,2)); % Blank Display Labels
% re-size
set(gcf,'InnerPosition',[2*innerWidth maxHeight-innerHeight innerWidth innerHeight]);
%% PLOT 3 - Polygon Heatmap 2 ================================================================================
% Made to be overlayed on top of cell image from rig
% resize the heatmap matrix to match the size of the cropped image from the rig
% the original heatmap matrix will only be as big as the number of squares
% used in the polygon design. But the image from the rig will be a lot
% bigger (the size of the matrix is the size of image in pixels)
resizedHeatmap = imresize(dataForHeatmap, [size(croppedImage,1) size(croppedImage,2)], 'nearest');
% make heatmap without the heatmap function
figure('name', strcat(fileName, '_', analysisDate, ' - firing_vs_light_polygon - heatmap 2'));
imshow(resizedHeatmap,'Colormap',flipud(parula),'DisplayRange', [heatmapMin,heatmapMax], 'Border', 'tight');
set(gcf,'InnerPosition',[innerWidth maxHeight-innerHeight innerWidth innerHeight]);
c = colorbar;
c.Label.String = 'SDs from baseline firing rate';
%% PLOT 4 - Make a blended image with transparency ===========================================================
% White where there was no inhibition of firing
% Transparent where there was inhibition of firing beyond significance
% criteria (firing decreased by -2 SDs from mean baseline firing rate)
% Note that actual criteria is determined by user input "heatmapMin"
% If heatmapMin = -2, then the criteria is "firing decreased by -2 SDs from
% mean baseline firing rate"
% pilot code that is now obsolete:
% figure('Name', '1')
% background = im2double(croppedImage);
% blendedImage = 0.5*resizedHeatmap + background;
% blendedImage = im2uint8(blendedImage);
% imshow(blendedImage)
% creating a transparency mask
% trasform all negative values into positive ones: -resizedHeatmap
transparencyMask = -resizedHeatmap;
% default values are heatmapMax=0 and heatmapMin=-2
% so default results are transparencyMaskMin=0 and transparencyMaskMax=2
transparencyMaskMin = -heatmapMax;
transparencyMaskMax = -heatmapMin;
% anything between 0 and 2 --> normalized between 1 and 0 --> transparency gradient
transparencyMask(transparencyMask>transparencyMaskMin & transparencyMask<transparencyMaskMax) = 1 - transparencyMask(transparencyMask>transparencyMaskMin & transparencyMask<transparencyMaskMax)/transparencyMaskMax;
% anything below 0 --> 1 --> min transparency --> white
transparencyMask(transparencyMask<=transparencyMaskMin) = 1;
% anything above 2 --> 0 --> max transparency --> transparent
transparencyMask(transparencyMask>=transparencyMaskMax) = 0;
% sanity check that transparencyMask is between 0 and 1
max(max(transparencyMask));
min(min(transparencyMask));
figure('Name', strcat(fileName, '_', analysisDate, ' - firing_vs_light_polygon - transparent heatmap'))
background = imshow(croppedImage, 'Border', 'tight');
% make a white image the same size as the croppedImage
whiteImage = cat(3, ones(size(croppedImage)), ones(size(croppedImage)), ones(size(croppedImage)));
% fyi the placement of hold on and hold off in this code matter a lot for
% whatever reason. Any other placement leads to image overwritting instead
% of overlaying.
hold on;
% overlay white mask
whiteMask = imshow(whiteImage);
hold off;
% apply transparency mask to white mask
set(whiteMask, 'AlphaData', transparencyMask);
% set figure size to the same as the cropped cell image
% FYI this only adjusts the outer figure size, not the inner figure size...
set(gcf,'InnerPosition',[0 maxHeight-2.5*innerHeight innerWidth innerHeight]);
% set(gcf,'OuterPosition',[1 1 outerWidth outerHeight]);
%% PLOT 5 - ISI ==============================================================================================
% ISI CV pre-light across all sweeps
figure('name', strcat(fileName, '_', analysisDate, ' - firing_vs_light_polygon - baseline ISI counts'));
% edges: from 0-1s in 1ms steps
edges = [0:0.001:1];
histogram(allBaselineIsi, edges);
title([strcat(fileName, ' baseline ISI counts')],'Interpreter','none');
xlabel('ISI (s)');
ylabel('Counts per bin');
axis([0 1 0 ymaxIsiCV])
xticks([0 1]);
set(gcf,'Position',[maxWidth-400 maxHeight-400 400 400]);
yticks([0 ymaxIsiCV]);
%% PLOT 6 - ISI normalized ===================================================================================
% ISI CV pre-light across all sweeps
figure('name', strcat(fileName, '_', analysisDate, ' - firing_vs_light_polygon - baseline ISI prob'));
% edges: from 0-1s in 1ms steps
edges = [0:0.001:1];
histogram(allBaselineIsi, edges, 'Normalization', 'probability', 'FaceColor', [0 0 0],'FaceAlpha', 1, 'EdgeColor', 'none');
title([strcat(fileName, ' baseline ISI prob')],'Interpreter','none');
xlabel('ISI (s)');
ylabel('Counts ber Bin / Total Counts');
axis([0 0.5 0 0.1])
xticks([0 0.5]);
yticks([0 0.1]);
set(gcf,'Position',[maxWidth-400 maxHeight-400-500 400 400]);
%% PLOT 7 - Raster plot (tiled) ==============================================================================
% Zoomed in: mean +- 2SD Hz is from short pre-light baseline
% create figure & name it
figure('name', strcat(fileName, '_', analysisDate, ' - firing_vs_light_polygon - tiled raster'));
t = tiledlayout(gridRows, gridColumns);
% plotting raster plot
for square = [1:totalSquares]
nexttile
hold on;
for sweep = [relativeSweepNumberPerSquare(square,:)]
plot(cell2mat(tsBySweep(sweep)), ones(size(cell2mat(sweepNumberArrayBySweep(sweep))))+sweep, '|', 'Color', 'k')
end
% zooming in and beautifying raster plot
axis([lightOnsetTime-lightDur lightOnsetTime+2*lightDur relativeSweepNumberPerSquare(square,1)-20 relativeSweepNumberPerSquare(square,end)+20])
% ylabel(strcat('Sweeps (', num2str(sweepsPerSquare), ')'));
% xlabel('Time (s)');
yticks([]);
xticks(0:1:10);