├── LICENSE ├── MATLAB version ├── Photometry_data_processing.m ├── Photometry_data_processing.mlx ├── airPLS.m ├── example.csv └── get_zdFF.m ├── Photometry_data_processing.ipynb ├── R version ├── Photometry_data_processing.R ├── Photometry_data_processing.Rmd ├── airPLS.R ├── example.csv ├── get_zdFF.R └── movavg.R ├── README.md ├── airPLS.py ├── example.csv ├── get_zdFF.py ├── photometry_functions.py └── smooth_signal.py /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /MATLAB version/Photometry_data_processing.m: -------------------------------------------------------------------------------- 1 | % Fiber photometry data processing 2 | % 3 | % If you want to use this code, please cite our Jove paper: 4 | % Martianova, E., Aronson, S., Proulx, C.D. Multi-Fiber Photometry to 5 | % Record Neural Activity in Freely Moving Animal. J. Vis. Exp. 6 | % (152), e60278, doi:10.3791/60278 (2019). 7 | 8 | % Run section by section (Ctrl+Enter) 9 | 10 | %% Your data 11 | 12 | df = readtable('example.csv'); % Change to your file 13 | head(df,5) 14 | 15 | % Change the next two lines depending on your data frame 16 | raw_reference = df.MeanInt_410nm(2:end)'; 17 | raw_signal = df.MeanInt_470nm(2:end)'; 18 | 19 | % Plot raw data 20 | figure 21 | subplot(2,1,1) 22 | plot(raw_reference,'m') 23 | subplot(2,1,2) 24 | plot(raw_signal,'b') 25 | 26 | %% Use function get_zdFF.m to analyze data 27 | zdFF = get_zdFF(raw_reference,raw_signal); 28 | 29 | % Plot z-score dF/F 30 | figure 31 | plot(zdFF,'k') 32 | 33 | %% Analysis step by step 34 | % Smooth data 35 | smooth_win = 10; 36 | smooth_reference = movmean(raw_reference,smooth_win); 37 | smooth_signal = movmean(raw_signal,smooth_win); 38 | 39 | % Plot smoothed signals 40 | figure 41 | subplot(2,1,1) 42 | plot(smooth_reference,'m') 43 | subplot(2,1,2) 44 | plot(smooth_signal,'b') 45 | 46 | %% Remove slope using airPLS algorithm (airPLS.m) 47 | lambda = 5e9; 48 | order = 2; 49 | wep = 0.1; 50 | p = 0.5; 51 | itermax = 50; 52 | [reference,base_r]= airPLS(smooth_reference,lambda,order,wep,p,itermax); 53 | [signal,base_s]= airPLS(smooth_signal,lambda,order,wep,p,itermax); 54 | 55 | % Plot slopes 56 | figure 57 | subplot(2,1,1) 58 | plot(smooth_reference,'m') 59 | hold on 60 | plot(base_r,'k') 61 | hold off 62 | subplot(2,1,2) 63 | plot(smooth_signal,'b') 64 | hold on 65 | plot(base_s,'k') 66 | hold off 67 | 68 | %% Remove the begining of recordings 69 | remove = 200; 70 | reference = reference(remove:end); 71 | signal = signal(remove:end); 72 | 73 | % Plot signals 74 | figure 75 | subplot(2,1,1) 76 | plot(reference,'m') 77 | subplot(2,1,2) 78 | plot(signal,'b') 79 | 80 | %% Standardize signals 81 | z_reference = (reference - median(reference)) / std(reference); 82 | z_signal = (signal - median(signal)) / std(signal); 83 | 84 | % Plot signals 85 | figure 86 | subplot(2,1,1) 87 | plot(z_reference,'m') 88 | subplot(2,1,2) 89 | plot(z_signal,'b') 90 | 91 | %% Fit reference signal to calcium signal 92 | % using non negative robust linear regression 93 | fitdata = fit(z_reference',z_signal',fittype('poly1'),'Robust','on'); 94 | 95 | % Plot fit 96 | figure 97 | hold on 98 | plot(z_reference,z_signal,'k.') 99 | plot(fitdata,'b') 100 | hold off 101 | 102 | %% Align reference to signal 103 | z_reference = fitdata(z_reference)'; 104 | 105 | % Plot aligned signals 106 | figure 107 | plot(z_reference,'m') 108 | hold on 109 | plot(z_signal,'b') 110 | hold off 111 | 112 | %% Calculate z-score dF/F 113 | zdFF = z_signal - z_reference; 114 | 115 | % Plot z-score dF/F 116 | figure 117 | plot(zdFF,'k') 118 | 119 | %% Contact us 120 | % If you have any questions please contact us: ekaterina.martianova.1@ulaval.ca 121 | -------------------------------------------------------------------------------- /MATLAB version/Photometry_data_processing.mlx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/katemartian/Photometry_data_processing/cd308ada5e3c1c9f7c3f60a0911615878d62920e/MATLAB version/Photometry_data_processing.mlx -------------------------------------------------------------------------------- /MATLAB version/airPLS.m: -------------------------------------------------------------------------------- 1 | function [Xc,Z]= airPLS(X,lambda,order,wep,p,itermax) 2 | % Baseline correction using adaptive iteratively reweighted Penalized Least Squares; 3 | % Input 4 | % X:row matrix of spectra or chromatogram (size m*n, m is sample and n is variable) 5 | % lambda: lambda is an adjustable parameter, it can be adjusted by user. The larger lambda is, the smoother z will be 6 | % order: an integer indicating the order of the difference of penalties 7 | % wep: weight exception proportion at both the start and end 8 | % p: asymmetry parameter for the start and end 9 | % itermax: maximum iteration times 10 | % Output 11 | % Xc: the corrected spectra or chromatogram vector (size m*n) 12 | % Z: the fitted vector (size m*n) 13 | % Examples: 14 | % Xc=airPLS(X); 15 | % [Xc,Z]=airPLS(X,10e5,2,0.1,0.5,20); 16 | % Reference: 17 | % (1) Eilers, P. H. C., A perfect smoother. Analytical Chemistry 75 (14), 3631 (2003). 18 | % (2) Eilers, P. H. C., Baseline Correction with Asymmetric Least 19 | % Squares Smoothing, http://www.science.uva.nl/~hboelens/publications/draftpub/Eilers_2005.pdf 20 | % (3) Gan, Feng, Ruan, Guihua, and Mo, Jinyuan, Baseline correction by improved iterative polynomial fitting with automatic threshold. Chemometrics and Intelligent Laboratory Systems 82 (1-2), 59 (2006). 21 | % 22 | % zhimin zhang @ central south university on Mar 30,2011 23 | 24 | if nargin < 6 25 | itermax=20; 26 | if nargin < 5 27 | p=0.05; 28 | if nargin < 4 29 | wep=0.1; 30 | if nargin < 3 31 | order=2; 32 | if nargin < 2 33 | lambda=10e7; 34 | if nargin < 1 35 | error('airPLS:NotEnoughInputs','Not enough input arguments. See airPLS.'); 36 | end 37 | end 38 | end 39 | end 40 | end 41 | end 42 | 43 | [m,n]=size(X); 44 | wi = [1:ceil(n*wep) floor(n-n*wep):n]; 45 | D = diff(speye(n), order); 46 | DD = lambda*D'*D; 47 | for i=1:m 48 | w=ones(n,1); 49 | x=X(i,:); 50 | for j=1:itermax 51 | W=spdiags(w, 0, n, n); 52 | C = chol(W + DD); 53 | z = (C\(C'\(w .* x')))'; 54 | d = x-z; 55 | dssn= abs(sum(d(d<0))); 56 | if(dssn<0.001*sum(abs(x))) 57 | break; 58 | end 59 | w(d>=0) = 0; 60 | w(wi) = p; 61 | w(d<0) = exp(j*abs(d(d<0))/dssn); 62 | end 63 | Z(i,:)=z; 64 | end 65 | Xc=X-Z; -------------------------------------------------------------------------------- /MATLAB version/get_zdFF.m: -------------------------------------------------------------------------------- 1 | function zdFF = get_zdFF(reference, signal, smooth_win, remove, lambda, itermax, order, wep, p) 2 | 3 | % Calculates z-score dF/F signal based on fiber photometry calcium-idependent 4 | % and calcium dependent signals. 5 | % 6 | % This program is a translation in MATLAB of the Python source code of 7 | % get_zdFF.py 8 | % 9 | % Input 10 | % reference: calcium-independent signal (usually 405-420 nm excitation) 11 | % signal: calcium-dependent signal (usually 465-490 nm excitation 12 | % for green fluorescent proteins, or ~560 nm for red) 13 | % smooth_win: window for moving average smooth 14 | % remove: the beginning of the traces with a steep slope one would like to remove 15 | % Inputs for airPLS: 16 | % lambda: lambda is an adjustable parameter, it can be adjusted by user. 17 | % The larger lambda is, the smoother baseline will be 18 | % itermax: maximum iteration times 19 | % order: an integer indicating the order of the difference of penalties 20 | % wep: weight exception proportion at both the start and end 21 | % p: asymmetry parameter for the start and end 22 | % 23 | % Output 24 | % zdFF - z-score dF/F, vector 25 | % 26 | % Examples: 27 | % zdFF = get_zdFF(reference, signal); 28 | % zdFF = get_zdFF(reference, signal, 10, 200, 5e9, 50, 2, 0.5, 0.5); 29 | % 30 | % Reference: 31 | % (1) Martianova, E., Aronson, S., Proulx, C.D. Multi-Fiber Photometry 32 | % to Record Neural Activity in Freely Moving Animal. J. Vis. Exp. 33 | % (152), e60278, doi:10.3791/60278 (2019) 34 | % https://www.jove.com/video/60278/multi-fiber-photometry-to-record-neural-activity-freely-moving 35 | % 36 | % March 2020 Ekaterina Martianova ekaterina.martianova.1@ulaval.ca 37 | 38 | 39 | % Preset some parameters 40 | if nargin<9 41 | p = 0.5; 42 | if nargin<8 43 | wep = 0.5; 44 | if nargin<7 45 | order = 2; 46 | if nargin<6 47 | itermax = 50; 48 | if nargin<5 49 | lambda=5e9; 50 | if nargin<4 51 | remove=200; 52 | if nargin<3 53 | smooth_win=10; 54 | end 55 | end 56 | end 57 | end 58 | end 59 | end 60 | end 61 | 62 | % Smooth signals using moving average 63 | reference = movmean(reference,smooth_win); 64 | signal = movmean(signal,smooth_win); 65 | 66 | % Find and remove slope in signals using airPLS 67 | [reference, ~]= airPLS(reference,lambda,order,wep,p,itermax); 68 | [signal, ~]= airPLS(signal,lambda,order,wep,p,itermax); 69 | 70 | % Remove the begining of recordings with fast drop 71 | reference = reference(remove:end); 72 | signal = signal(remove:end); 73 | 74 | % Standardize signals 75 | reference = (reference - median(reference)) / std(reference); 76 | signal = (signal - median(signal)) / std(signal); 77 | 78 | % Robust non negative linear regression 79 | fitdata = fit(reference',signal',fittype('poly1'),'Robust','on'); 80 | % Align reference trace to signal using the fit 81 | reference = fitdata(reference)'; 82 | 83 | % z-score dFF 84 | zdFF = signal - reference; 85 | end -------------------------------------------------------------------------------- /R version/Photometry_data_processing.R: -------------------------------------------------------------------------------- 1 | 2 | # If you would like to use this code, please cite our Jove paper: 3 | # Martianova, E., Aronson, S., Proulx, C.D. Multi-Fiber Photometry to Record 4 | # Neural Activity in Freely Moving Animal. J. Vis. Exp. (152), e60278, 5 | # doi:10.3791/60278 (2019). 6 | # https://www.jove.com/video/60278/multi-fiber-photometry-to-record-neural-activity-freely-moving 7 | 8 | 9 | ########### Your data ########################################################## 10 | 11 | df <- read.csv('example.csv') # Change to your file and directory 12 | head(df, n=5) 13 | 14 | raw_reference <- df$MeanInt_410nm[2:nrow(df)] 15 | raw_signal <- df$MeanInt_470nm[2:nrow(df)] 16 | 17 | # Plot raw data 18 | par(mfrow=c(2,1)) 19 | plot(raw_reference, type='l', col='purple') 20 | plot(raw_signal, type='l', col='blue') 21 | 22 | 23 | ########## Use function ######################################################## 24 | 25 | # Run function get_zdFF.R 26 | zdFF = get_zdFF(raw_reference,raw_signal) 27 | 28 | # Plot z-score dF/F 29 | par(mfrow=c(1,1)) 30 | plot(zdFF, type='l', col='black') 31 | 32 | ########## Analysis step by step ############################################### 33 | 34 | ### Smooth 35 | 36 | # Run function movavg.R 37 | smooth_win = 10 38 | smooth_reference <- movavg(raw_reference, smooth_win) 39 | smooth_signal <- movavg(raw_signal, smooth_win) 40 | 41 | # Plot smoothed signal 42 | par(mfrow=c(2,1)) 43 | plot(smooth_reference, type='l', col='purple') 44 | plot(smooth_signal, type='l', col='blue') 45 | 46 | 47 | ### Find slope baseline 48 | 49 | # Install package airPLS 50 | install.packages('devtools') 51 | library(devtools) 52 | httr::set_config( httr::config( ssl_verifypeer = 0L ) ) 53 | install_github("zmzhang/airPLS_R") 54 | 55 | # Call airPLS library 56 | library(airPLS) 57 | 58 | base_r <- airPLS(smooth_reference,5e4,1,50) 59 | base_s <- airPLS(smooth_signal,5e4,1,50) 60 | 61 | # Plot data and baselines 62 | par(mfrow=c(2,1)) 63 | plot(smooth_reference, type='l', col='purple') 64 | lines(base_r) 65 | plot(smooth_signal, type='l', col='blue') 66 | lines(base_s) 67 | 68 | 69 | ### Remove baseline and the begining 70 | 71 | remove = 200 72 | n = length(raw_reference) 73 | reference <- smooth_reference[remove:n] - base_r[remove:n] 74 | signal <- smooth_signal[remove:n] - base_s[remove:n] 75 | 76 | # Plot flatten data 77 | par(mfrow=c(2,1)) 78 | plot(reference, type='l', col='purple') 79 | plot(signal, type='l', col='blue') 80 | 81 | 82 | ### Standardize signals 83 | 84 | z_reference <- (reference - median(reference)) / sd(reference) 85 | z_signal <- (signal - median(signal)) / sd(signal) 86 | 87 | # Plot standardized data 88 | par(mfrow=c(2,1)) 89 | plot(z_reference, type='l', col='purple') 90 | plot(z_signal, type='l', col='blue') 91 | 92 | 93 | ### Linear robust fit 94 | require(MASS) 95 | fit <- rlm(z_signal ~ z_reference) 96 | 97 | # Plot fit 98 | par(mfrow=c(1,1)) 99 | plot(reference, signal) 100 | abline(fit, col="red") 101 | 102 | 103 | ### Align signals 104 | 105 | z_reference_fit <- predict(fit) 106 | 107 | # Plot aligned signals 108 | plot(signal, type='l', col='blue') 109 | lines(reference, type='l', col='purple') 110 | 111 | 112 | ### Calculate z-score dF/F 113 | 114 | zdFF <- z_signal - z_reference_fit 115 | 116 | # Plot z-score dF/F 117 | plot(zdFF, type='l', col='black') 118 | 119 | 120 | ########## Contuct us ########################################################## 121 | 122 | # If you have any questions, please contact us: ekaterina.martianova.1@ulaval.ca 123 | -------------------------------------------------------------------------------- /R version/Photometry_data_processing.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Photometry data processing" 3 | output: html_notebook 4 | --- 5 | If you want to use this code, please cite our Jove paper: 6 | 7 | __Martianova, E., Aronson, S., Proulx, C.D.__ [Multi-Fiber Photometry to Record Neural Activity in Freely Moving Animal.](https://www.jove.com/video/60278/multi-fiber-photometry-to-record-neural-activity-freely-moving). _J. Vis. Exp._ (152), e60278, doi:10.3791/60278 (2019). 8 | 9 | This R notebook is a translation in R of the Python Jupyter notebook Photometry_data_processing.ipynb 10 | 11 | 12 | # Your data 13 | ```{r} 14 | folder = 'C:/' # Change to your folder 15 | file = 'example.csv' # Change to your file or use our example.csv 16 | df <- read.csv(paste(folder,file,sep='')) # Change if the file is not *.csv 17 | head(df, n=5) 18 | ``` 19 | 20 | ```{r} 21 | # Change depending on your data frame 22 | raw_reference <- df$MeanInt_410nm[2:nrow(df)] 23 | raw_signal <- df$MeanInt_470nm[2:nrow(df)] 24 | ``` 25 | 26 | 27 | Plot raw traces 28 | ```{r} 29 | par(mfrow=c(2,1)) 30 | plot(raw_reference, type='l', col='purple') 31 | plot(raw_signal, type='l', col='blue') 32 | ``` 33 | 34 | # Use function get_zdFF.R to calculate z-score dF/F 35 | ```{r} 36 | zdFF = get_zdFF(raw_reference,raw_signal) 37 | ``` 38 | 39 | 40 | Plot z-dF/F 41 | ```{r} 42 | plot(zdFF, type='l', col='black') 43 | ``` 44 | 45 | # Analysis step-by-step 46 | 47 | ### Smooth traces 48 | 49 | 50 | Make sure to run movavg.R function. 51 | ```{r} 52 | smooth_win = 10 53 | smooth_reference <- movavg(raw_reference, smooth_win) 54 | smooth_signal <- movavg(raw_signal, smooth_win) 55 | ``` 56 | You can use other algorithms to smooth your data. 57 | 58 | 59 | Plot smoothed traces 60 | ```{r} 61 | par(mfrow=c(2,1)) 62 | plot(smooth_reference, type='l', col='purple') 63 | plot(smooth_signal, type='l', col='blue') 64 | ``` 65 | 66 | ### Find baseline slopes of the signals 67 | 68 | 69 | Make sure to run airPLS.R function 70 | ```{r} 71 | lambda = 5e4 72 | itermax = 50 73 | differences = 1 74 | 75 | base_r <- airPLS(smooth_reference, lambda, differences, itermax) 76 | base_s <- airPLS(smooth_signal, lambda, differences, itermax) 77 | ``` 78 | 79 | 80 | Plot baseline slopes 81 | ```{r} 82 | par(mfrow=c(2,1)) 83 | plot(smooth_reference, type='l', col='purple') 84 | lines(base_r) 85 | plot(smooth_signal, type='l', col='blue') 86 | lines(base_s) 87 | ``` 88 | 89 | ### Remove the baselines and the begining of the traces 90 | ```{r} 91 | remove = 200 92 | n = length(raw_reference) 93 | reference <- smooth_reference[remove:n] - base_r[remove:n] 94 | signal <- smooth_signal[remove:n] - base_s[remove:n] 95 | ``` 96 | 97 | 98 | Plot traces 99 | ```{r} 100 | par(mfrow=c(2,1)) 101 | plot(reference, type='l', col='purple') 102 | plot(signal, type='l', col='blue') 103 | ``` 104 | 105 | 106 | ### Standardize traces 107 | ```{r} 108 | z_reference <- (reference - median(reference)) / sd(reference) 109 | z_signal <- (signal - median(signal)) / sd(signal) 110 | ``` 111 | 112 | 113 | Plot traces 114 | ```{r} 115 | par(mfrow=c(2,1)) 116 | plot(z_reference, type='l', col='purple') 117 | plot(z_signal, type='l', col='blue') 118 | ``` 119 | 120 | ### Fit reference trace to signal 121 | 122 | ```{r} 123 | # Robust linear regression 124 | require(MASS) 125 | fit <- rlm(z_signal ~ z_reference) 126 | ``` 127 | 128 | 129 | Plot the fit 130 | ```{r} 131 | par(mfrow=c(1,1)) 132 | plot(z_reference, z_signal) 133 | abline(fit, col="red") 134 | ``` 135 | 136 | ### Align reference trace to signal 137 | ```{r} 138 | z_reference_fit <- predict(fit) 139 | ``` 140 | 141 | 142 | Plot aligned traces 143 | ```{r} 144 | plot(z_signal, type='l', col='blue') 145 | lines(z_reference_fit, type='l', col='purple') 146 | ``` 147 | 148 | ### Calculate z-score zdF/F 149 | ```{r} 150 | zdFF <- z_signal - z_reference_fit 151 | ``` 152 | 153 | 154 | Plot z-dF/F 155 | ```{r} 156 | plot(zdFF, type='l', col='black') 157 | ``` 158 | 159 | # Contact us 160 | If you have any questions, please contact us: ekaterina.martianova.1@ulaval.ca -------------------------------------------------------------------------------- /R version/airPLS.R: -------------------------------------------------------------------------------- 1 | WhittakerSmooth <- function(x,w,lambda,differences=1) { 2 | x=matrix(x,nrow = 1, ncol=length(x)) 3 | L=length(x) 4 | E=spMatrix(L,L,i=seq(1,L),j=seq(1,L),rep(1,L)) 5 | D=as(diff(E,1,differences),"dgCMatrix") 6 | W=as(spMatrix(L,L,i=seq(1,L),j=seq(1,L),w),"dgCMatrix") 7 | background=solve((W+lambda*t(D)%*%D),t((w*x))); 8 | return(as.vector(background)) 9 | } 10 | 11 | airPLS <- function(x,lambda=10,differences=1, itermax=20){ 12 | 13 | x = as.vector(x) 14 | m = length(x) 15 | w = rep(1,m) 16 | control = 1 17 | i = 1 18 | while(control==1){ 19 | z = WhittakerSmooth(x,w,lambda,differences) 20 | d = x-z 21 | sum_smaller = abs(sum(d[d<0])) 22 | if(sum_smaller<0.001*sum(abs(x))||i==itermax) 23 | { 24 | control = 0 25 | } 26 | w[d>=0] = 0 27 | w[d<0] = exp(i*abs(d[d<0])/sum_smaller) 28 | w[1] = exp(i*max(d[d<0])/sum_smaller) 29 | w[m] = exp(i*max(d[d<0])/sum_smaller) 30 | i=i+1 31 | } 32 | return(z) 33 | } -------------------------------------------------------------------------------- /R version/get_zdFF.R: -------------------------------------------------------------------------------- 1 | get_zdFF <- function(reference, signal, smooth_win=10, remove=200, lambda=5e4, itermax=50, differences=1) 2 | { 3 | # Calculates z-score dF/F signal based on fiber photometry calcium-idependent 4 | # and -dependent signals. 5 | # 6 | # This program is a translation in R of the Python source code of get_zdFF.py 7 | # 8 | # Input 9 | # reference: calcium-independent signal (usually 405-420 nm excitation) 10 | # signal: calcium-dependent signal (usually 465-490 nm excitation 11 | # for green fluorescent proteins, or ~560 nm for red) 12 | # smooth_win: window for moving average smooth 13 | # remove: the beginning of the traces with a steep slope one would like 14 | # to remove 15 | # Inputs for airPLS: 16 | # lambda: lambda is an adjustable parameter, it can be adjusted by user. 17 | # The larger lambda is, the smoother baseline will be 18 | # itermax: maximum iteration times 19 | # differences 20 | # 21 | # Output 22 | # zdFF - z-score dF/F, 23 | # 24 | # Examples: 25 | # zdFF = get_zdFF(reference, signal); 26 | # zdFF = get_zdFF(reference, signal, 10, 200, 5e4, 50, 1); 27 | # 28 | # Reference: 29 | # (1) Martianova, E., Aronson, S., Proulx, C.D. Multi-Fiber Photometry 30 | # to Record Neural Activity in Freely Moving Animal. J. Vis. Exp. 31 | # (152), e60278, doi:10.3791/60278 (2019) 32 | # https://www.jove.com/video/60278/multi-fiber-photometry-to-record-neural-activity-freely-moving 33 | # 34 | # March 2020 Ekaterina Martianova ekaterina.martianova.1@ulaval.ca 35 | 36 | 37 | # Smooth signals 38 | reference = movavg(reference, smooth_win) 39 | signal = movavg(signal, smooth_win) 40 | 41 | # Find slope using airPLS algorithm 42 | require(airPLS) 43 | base_r <- airPLS(reference, lambda, differences, itermax) 44 | base_s <- airPLS(signal, lambda, differences, itermax) 45 | 46 | # Remove slope and the begining of the recordings 47 | n = length(reference) 48 | reference = reference[remove:n] - base_r[remove:n] 49 | signal = signal[remove:n] - base_s[remove:n] 50 | 51 | # Standardize signals 52 | reference = (reference - median(reference)) / sd(reference) 53 | signal = (signal - median(signal)) / sd(signal) 54 | 55 | # Linear robust fit 56 | require(MASS) 57 | fit <- rlm(signal ~ reference) 58 | reference <- predict(fit) 59 | 60 | # Calculate z-score dF/F 61 | zdFF <- signal - reference 62 | 63 | return(zdFF) 64 | 65 | } -------------------------------------------------------------------------------- /R version/movavg.R: -------------------------------------------------------------------------------- 1 | movavg <- function(v, win) 2 | { 3 | # Calculates moving average 4 | # 5 | # Input 6 | # v: vector to smooth 7 | # win: window for moving average 8 | # Output 9 | # smoothed signal 10 | # 11 | 12 | f = rep(1, win) / win 13 | n = length(v) 14 | v1 = c(rep(v[1], win), v, rep(v[n],win)) 15 | v1 = stats::filter(v1, f) 16 | return(v1[win+1:n]) 17 | } 18 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Photometry data processing 2 | 3 | This processing pipeline of fiber photometry recordings was developed by Ekaterina Martianova in Christophe Proulx lab at CERVO Brain research center, Laval university, Quebec, QC, Canada, 2019. 4 | 5 | In order to use, please copy the notebook ___Photometry_data_processing.ipynb___ to your google drive by clicking on [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/katemartian/Photometry_data_processing/blob/master/Photometry_data_processing.ipynb) and the example file example.csv to your google drive and follow the instructions in the notebook. You can also run the codes from your local machine if you would like. 6 | For MATLAB version, open ___Photometry_data_processing.mlx___ in MATLAB Live Editor and follow the instructions in the notebook. 7 | For R version, open ___Photometry_data_processing.Rmd___ in RStudio and follow the instructions in the notebook. 8 | 9 | If you would like to use this code, please cite our Jove paper: __Martianova, E., Aronson, S., Proulx, C.D.__ [Multi-Fiber Photometry to Record Neural Activity in Freely Moving Animal.](https://www.jove.com/video/60278/multi-fiber-photometry-to-record-neural-activity-freely-moving). _J. Vis. Exp._ (152), e60278, doi: 10.3791/60278 (2019). 10 | 11 | If you have any questions, please contact at neudatsci@gmail.com 12 | 13 | 14 | __NEW!__ The pipeline is part of [danse](https://neuro.doriclenses.com/products/danse) software now. You might be interested to watch this [video](https://doriclenses.com/downloads/video/How_to_process_FP_data_V1.4.mp4) explaining the processing. If you prefer, please don't hesitate to contact to my work email: kate.martianova@doriclenses.com 15 | 16 | 17 | # License 18 | This project is licensed under GNU GPLv3. 19 | -------------------------------------------------------------------------------- /airPLS.py: -------------------------------------------------------------------------------- 1 | ''' 2 | airPLS.py Copyright 2014 Renato Lombardo - renato.lombardo@unipa.it 3 | Baseline correction using adaptive iteratively reweighted penalized least squares 4 | 5 | This program is a translation in python of the R source code of airPLS version 2.0 6 | by Yizeng Liang and Zhang Zhimin - https://code.google.com/p/airpls 7 | 8 | Reference: 9 | Z.-M. Zhang, S. Chen, and Y.-Z. Liang, Baseline correction using adaptive iteratively 10 | reweighted penalized least squares. Analyst 135 (5), 1138-1146 (2010). 11 | 12 | Description from the original documentation: 13 | Baseline drift always blurs or even swamps signals and deteriorates analytical 14 | results, particularly in multivariate analysis. It is necessary to correct baseline 15 | drift to perform further data analysis. Simple or modified polynomial fitting has 16 | been found to be effective in some extent. However, this method requires user 17 | intervention and prone to variability especially in low signal-to-noise ratio 18 | environments. The proposed adaptive iteratively reweighted Penalized Least Squares 19 | (airPLS) algorithm doesn't require any user intervention and prior information, 20 | such as detected peaks. It iteratively changes weights of sum squares errors (SSE) 21 | between the fitted baseline and original signals, and the weights of SSE are obtained 22 | adaptively using between previously fitted baseline and original signals. This 23 | baseline estimator is general, fast and flexible in fitting baseline. 24 | 25 | 26 | LICENCE 27 | This program is free software: you can redistribute it and/or modify 28 | it under the terms of the GNU Lesser General Public License as published by 29 | the Free Software Foundation, either version 3 of the License, or 30 | (at your option) any later version. 31 | 32 | This program is distributed in the hope that it will be useful, 33 | but WITHOUT ANY WARRANTY; without even the implied warranty of 34 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 35 | GNU Lesser General Public License for more details. 36 | 37 | You should have received a copy of the GNU Lesser General Public License 38 | along with this program. If not, see 39 | ''' 40 | 41 | import numpy as np 42 | from scipy.sparse import csc_matrix, eye, diags 43 | from scipy.sparse.linalg import spsolve 44 | 45 | def WhittakerSmooth(x,w,lambda_,differences=1): 46 | ''' 47 | Penalized least squares algorithm for background fitting 48 | 49 | input 50 | x: input data (i.e. chromatogram of spectrum) 51 | w: binary masks (value of the mask is zero if a point belongs to peaks and one otherwise) 52 | lambda_: parameter that can be adjusted by user. The larger lambda is, 53 | the smoother the resulting background 54 | differences: integer indicating the order of the difference of penalties 55 | 56 | output 57 | the fitted background vector 58 | ''' 59 | X=np.matrix(x) 60 | m=X.size 61 | i=np.arange(0,m) 62 | E=eye(m,format='csc') 63 | D=E[1:]-E[:-1] # numpy.diff() does not work with sparse matrix. This is a workaround. 64 | W=diags(w,0,shape=(m,m)) 65 | A=csc_matrix(W+(lambda_*D.T*D)) 66 | B=csc_matrix(W*X.T) 67 | background=spsolve(A,B) 68 | return np.array(background) 69 | 70 | def airPLS(x, lambda_=100, porder=1, itermax=15): 71 | ''' 72 | Adaptive iteratively reweighted penalized least squares for baseline fitting 73 | 74 | input 75 | x: input data (i.e. chromatogram of spectrum) 76 | lambda_: parameter that can be adjusted by user. The larger lambda is, 77 | the smoother the resulting background, z 78 | porder: adaptive iteratively reweighted penalized least squares for baseline fitting 79 | 80 | output 81 | the fitted background vector 82 | ''' 83 | m=x.shape[0] 84 | w=np.ones(m) 85 | for i in range(1,itermax+1): 86 | z=WhittakerSmooth(x,w,lambda_, porder) 87 | d=x-z 88 | dssn=np.abs(d[d<0].sum()) 89 | if(dssn<0.001*(abs(x)).sum() or i==itermax): 90 | if(i==itermax): print('WARING max iteration reached!') 91 | break 92 | w[d>=0]=0 # d>0 means that this point is part of a peak, so its weight is set to 0 in order to ignore it 93 | w[d<0]=np.exp(i*np.abs(d[d<0])/dssn) 94 | w[0]=np.exp(i*(d[d<0]).max()/dssn) 95 | w[-1]=w[0] 96 | return z 97 | -------------------------------------------------------------------------------- /get_zdFF.py: -------------------------------------------------------------------------------- 1 | ''' 2 | get_zdFF.py calculates standardized dF/F signal based on calcium-idependent 3 | and calcium-dependent signals commonly recorded using fiber photometry calcium imaging 4 | 5 | Ocober 2019 Ekaterina Martianova ekaterina.martianova.1@ulaval.ca 6 | 7 | Reference: 8 | (1) Martianova, E., Aronson, S., Proulx, C.D. Multi-Fiber Photometry 9 | to Record Neural Activity in Freely Moving Animal. J. Vis. Exp. 10 | (152), e60278, doi:10.3791/60278 (2019) 11 | https://www.jove.com/video/60278/multi-fiber-photometry-to-record-neural-activity-freely-moving 12 | 13 | ''' 14 | 15 | def get_zdFF(reference,signal,smooth_win=10,remove=200,lambd=5e4,porder=1,itermax=50): 16 | ''' 17 | Calculates z-score dF/F signal based on fiber photometry calcium-idependent 18 | and calcium-dependent signals 19 | 20 | Input 21 | reference: calcium-independent signal (usually 405-420 nm excitation), 1D array 22 | signal: calcium-dependent signal (usually 465-490 nm excitation for 23 | green fluorescent proteins, or ~560 nm for red), 1D array 24 | smooth_win: window for moving average smooth, integer 25 | remove: the beginning of the traces with a big slope one would like to remove, integer 26 | Inputs for airPLS: 27 | lambd: parameter that can be adjusted by user. The larger lambda is, 28 | the smoother the resulting background, z 29 | porder: adaptive iteratively reweighted penalized least squares for baseline fitting 30 | itermax: maximum iteration times 31 | Output 32 | zdFF - z-score dF/F, 1D numpy array 33 | ''' 34 | 35 | import numpy as np 36 | from sklearn.linear_model import Lasso 37 | 38 | # Smooth signal 39 | reference = smooth_signal(reference, smooth_win) 40 | signal = smooth_signal(signal, smooth_win) 41 | 42 | # Remove slope using airPLS algorithm 43 | r_base=airPLS(reference,lambda_=lambd,porder=porder,itermax=itermax) 44 | s_base=airPLS(signal,lambda_=lambd,porder=porder,itermax=itermax) 45 | 46 | # Remove baseline and the begining of recording 47 | reference = (reference[remove:] - r_base[remove:]) 48 | signal = (signal[remove:] - s_base[remove:]) 49 | 50 | # Standardize signals 51 | reference = (reference - np.median(reference)) / np.std(reference) 52 | signal = (signal - np.median(signal)) / np.std(signal) 53 | 54 | # Align reference signal to calcium signal using non-negative robust linear regression 55 | lin = Lasso(alpha=0.0001,precompute=True,max_iter=1000, 56 | positive=True, random_state=9999, selection='random') 57 | n = len(reference) 58 | lin.fit(reference.reshape(n,1), signal.reshape(n,1)) 59 | reference = lin.predict(reference.reshape(n,1)).reshape(n,) 60 | 61 | # z dFF 62 | zdFF = (signal - reference) 63 | 64 | return zdFF -------------------------------------------------------------------------------- /photometry_functions.py: -------------------------------------------------------------------------------- 1 | ''' 2 | get_zdFF.py calculates standardized dF/F signal based on calcium-idependent 3 | and calcium-dependent signals commonly recorded using fiber photometry calcium imaging 4 | 5 | Ocober 2019 Ekaterina Martianova ekaterina.martianova.1@ulaval.ca 6 | 7 | Reference: 8 | (1) Martianova, E., Aronson, S., Proulx, C.D. Multi-Fiber Photometry 9 | to Record Neural Activity in Freely Moving Animal. J. Vis. Exp. 10 | (152), e60278, doi:10.3791/60278 (2019) 11 | https://www.jove.com/video/60278/multi-fiber-photometry-to-record-neural-activity-freely-moving 12 | 13 | ''' 14 | 15 | def get_zdFF(reference,signal,smooth_win=10,remove=200,lambd=5e4,porder=1,itermax=50): 16 | ''' 17 | Calculates z-score dF/F signal based on fiber photometry calcium-idependent 18 | and calcium-dependent signals 19 | 20 | Input 21 | reference: calcium-independent signal (usually 405-420 nm excitation), 1D array 22 | signal: calcium-dependent signal (usually 465-490 nm excitation for 23 | green fluorescent proteins, or ~560 nm for red), 1D array 24 | smooth_win: window for moving average smooth, integer 25 | remove: the beginning of the traces with a big slope one would like to remove, integer 26 | Inputs for airPLS: 27 | lambd: parameter that can be adjusted by user. The larger lambda is, 28 | the smoother the resulting background, z 29 | porder: adaptive iteratively reweighted penalized least squares for baseline fitting 30 | itermax: maximum iteration times 31 | Output 32 | zdFF - z-score dF/F, 1D numpy array 33 | ''' 34 | 35 | import numpy as np 36 | from sklearn.linear_model import Lasso 37 | 38 | # Smooth signal 39 | reference = smooth_signal(reference, smooth_win) 40 | signal = smooth_signal(signal, smooth_win) 41 | 42 | # Remove slope using airPLS algorithm 43 | r_base=airPLS(reference,lambda_=lambd,porder=porder,itermax=itermax) 44 | s_base=airPLS(signal,lambda_=lambd,porder=porder,itermax=itermax) 45 | 46 | # Remove baseline and the begining of recording 47 | reference = (reference[remove:] - r_base[remove:]) 48 | signal = (signal[remove:] - s_base[remove:]) 49 | 50 | # Standardize signals 51 | reference = (reference - np.median(reference)) / np.std(reference) 52 | signal = (signal - np.median(signal)) / np.std(signal) 53 | 54 | # Align reference signal to calcium signal using non-negative robust linear regression 55 | lin = Lasso(alpha=0.0001,precompute=True,max_iter=1000, 56 | positive=True, random_state=9999, selection='random') 57 | n = len(reference) 58 | lin.fit(reference.reshape(n,1), signal.reshape(n,1)) 59 | reference = lin.predict(reference.reshape(n,1)).reshape(n,) 60 | 61 | # z dFF 62 | zdFF = (signal - reference) 63 | 64 | return zdFF 65 | 66 | 67 | def smooth_signal(x,window_len=10,window='flat'): 68 | 69 | """smooth the data using a window with requested size. 70 | 71 | This method is based on the convolution of a scaled window with the signal. 72 | The signal is prepared by introducing reflected copies of the signal 73 | (with the window size) in both ends so that transient parts are minimized 74 | in the begining and end part of the output signal. 75 | The code taken from: https://scipy-cookbook.readthedocs.io/items/SignalSmooth.html 76 | 77 | input: 78 | x: the input signal 79 | window_len: the dimension of the smoothing window; should be an odd integer 80 | window: the type of window from 'flat', 'hanning', 'hamming', 'bartlett', 'blackman' 81 | 'flat' window will produce a moving average smoothing. 82 | 83 | output: 84 | the smoothed signal 85 | """ 86 | 87 | import numpy as np 88 | 89 | if x.ndim != 1: 90 | raise(ValueError, "smooth only accepts 1 dimension arrays.") 91 | 92 | if x.size < window_len: 93 | raise(ValueError, "Input vector needs to be bigger than window size.") 94 | 95 | if window_len<3: 96 | return x 97 | 98 | if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']: 99 | raise(ValueError, "Window is one of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'") 100 | 101 | s=np.r_[x[window_len-1:0:-1],x,x[-2:-window_len-1:-1]] 102 | 103 | if window == 'flat': # Moving average 104 | w=np.ones(window_len,'d') 105 | else: 106 | w=eval('np.'+window+'(window_len)') 107 | 108 | y=np.convolve(w/w.sum(),s,mode='valid') 109 | 110 | return y[(int(window_len/2)-1):-int(window_len/2)] 111 | 112 | 113 | ''' 114 | airPLS.py Copyright 2014 Renato Lombardo - renato.lombardo@unipa.it 115 | Baseline correction using adaptive iteratively reweighted penalized least squares 116 | 117 | This program is a translation in python of the R source code of airPLS version 2.0 118 | by Yizeng Liang and Zhang Zhimin - https://code.google.com/p/airpls 119 | 120 | Reference: 121 | Z.-M. Zhang, S. Chen, and Y.-Z. Liang, Baseline correction using adaptive iteratively 122 | reweighted penalized least squares. Analyst 135 (5), 1138-1146 (2010). 123 | 124 | Description from the original documentation: 125 | Baseline drift always blurs or even swamps signals and deteriorates analytical 126 | results, particularly in multivariate analysis. It is necessary to correct baseline 127 | drift to perform further data analysis. Simple or modified polynomial fitting has 128 | been found to be effective in some extent. However, this method requires user 129 | intervention and prone to variability especially in low signal-to-noise ratio 130 | environments. The proposed adaptive iteratively reweighted Penalized Least Squares 131 | (airPLS) algorithm doesn't require any user intervention and prior information, 132 | such as detected peaks. It iteratively changes weights of sum squares errors (SSE) 133 | between the fitted baseline and original signals, and the weights of SSE are obtained 134 | adaptively using between previously fitted baseline and original signals. This 135 | baseline estimator is general, fast and flexible in fitting baseline. 136 | 137 | 138 | LICENCE 139 | This program is free software: you can redistribute it and/or modify 140 | it under the terms of the GNU Lesser General Public License as published by 141 | the Free Software Foundation, either version 3 of the License, or 142 | (at your option) any later version. 143 | 144 | This program is distributed in the hope that it will be useful, 145 | but WITHOUT ANY WARRANTY; without even the implied warranty of 146 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 147 | GNU Lesser General Public License for more details. 148 | 149 | You should have received a copy of the GNU Lesser General Public License 150 | along with this program. If not, see 151 | ''' 152 | 153 | import numpy as np 154 | from scipy.sparse import csc_matrix, eye, diags 155 | from scipy.sparse.linalg import spsolve 156 | 157 | def WhittakerSmooth(x,w,lambda_,differences=1): 158 | ''' 159 | Penalized least squares algorithm for background fitting 160 | 161 | input 162 | x: input data (i.e. chromatogram of spectrum) 163 | w: binary masks (value of the mask is zero if a point belongs to peaks and one otherwise) 164 | lambda_: parameter that can be adjusted by user. The larger lambda is, 165 | the smoother the resulting background 166 | differences: integer indicating the order of the difference of penalties 167 | 168 | output 169 | the fitted background vector 170 | ''' 171 | X=np.matrix(x) 172 | m=X.size 173 | i=np.arange(0,m) 174 | E=eye(m,format='csc') 175 | D=E[1:]-E[:-1] # numpy.diff() does not work with sparse matrix. This is a workaround. 176 | W=diags(w,0,shape=(m,m)) 177 | A=csc_matrix(W+(lambda_*D.T*D)) 178 | B=csc_matrix(W*X.T) 179 | background=spsolve(A,B) 180 | return np.array(background) 181 | 182 | def airPLS(x, lambda_=100, porder=1, itermax=15): 183 | ''' 184 | Adaptive iteratively reweighted penalized least squares for baseline fitting 185 | 186 | input 187 | x: input data (i.e. chromatogram of spectrum) 188 | lambda_: parameter that can be adjusted by user. The larger lambda is, 189 | the smoother the resulting background, z 190 | porder: adaptive iteratively reweighted penalized least squares for baseline fitting 191 | 192 | output 193 | the fitted background vector 194 | ''' 195 | m=x.shape[0] 196 | w=np.ones(m) 197 | for i in range(1,itermax+1): 198 | z=WhittakerSmooth(x,w,lambda_, porder) 199 | d=x-z 200 | dssn=np.abs(d[d<0].sum()) 201 | if(dssn<0.001*(abs(x)).sum() or i==itermax): 202 | if(i==itermax): print('WARING max iteration reached!') 203 | break 204 | w[d>=0]=0 # d>0 means that this point is part of a peak, so its weight is set to 0 in order to ignore it 205 | w[d<0]=np.exp(i*np.abs(d[d<0])/dssn) 206 | w[0]=np.exp(i*(d[d<0]).max()/dssn) 207 | w[-1]=w[0] 208 | return z 209 | -------------------------------------------------------------------------------- /smooth_signal.py: -------------------------------------------------------------------------------- 1 | def smooth_signal(x,window_len=10,window='flat'): 2 | 3 | """smooth the data using a window with requested size. 4 | 5 | This method is based on the convolution of a scaled window with the signal. 6 | The signal is prepared by introducing reflected copies of the signal 7 | (with the window size) in both ends so that transient parts are minimized 8 | in the begining and end part of the output signal. 9 | The code taken from: https://scipy-cookbook.readthedocs.io/items/SignalSmooth.html 10 | 11 | input: 12 | x: the input signal 13 | window_len: the dimension of the smoothing window; should be an odd integer 14 | window: the type of window from 'flat', 'hanning', 'hamming', 'bartlett', 'blackman' 15 | 'flat' window will produce a moving average smoothing. 16 | 17 | output: 18 | the smoothed signal 19 | """ 20 | 21 | import numpy as np 22 | 23 | if x.ndim != 1: 24 | raise(ValueError, "smooth only accepts 1 dimension arrays.") 25 | 26 | if x.size < window_len: 27 | raise(ValueError, "Input vector needs to be bigger than window size.") 28 | 29 | if window_len<3: 30 | return x 31 | 32 | if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']: 33 | raise(ValueError, "Window is one of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'") 34 | 35 | s=np.r_[x[window_len-1:0:-1],x,x[-2:-window_len-1:-1]] 36 | 37 | if window == 'flat': # Moving average 38 | w=np.ones(window_len,'d') 39 | else: 40 | w=eval('np.'+window+'(window_len)') 41 | 42 | y=np.convolve(w/w.sum(),s,mode='valid') 43 | 44 | return y[(int(window_len/2)-1):-int(window_len/2)] --------------------------------------------------------------------------------