├── 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
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--------------------------------------------------------------------------------
/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 [](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)]
--------------------------------------------------------------------------------