Merge pull request #15 from gardner-lab/Liberti

Liberti

Analysis Pipeline/FS_AV_Parse.m

@@ -81,7 +81,7 @@ function FS_AV_Parse(DIR,varargin)
     clear k V1;
 
 else
-    
+
   disp('moving file- to large for batch processing... use FS_AV_Parse(pwd,large)');
   %LargeDir = strcat(path,'/','LargeFiles');
   movefile(FILE, error_dir)
@@ -103,6 +103,7 @@ function FS_AV_Parse(DIR,varargin)
 end
 
 %
+try
 disp('Performing Gain correction')
 noise = squeeze(est(:,:,3,:)); % blue channel
 noise = (((squeeze(mean(mean(noise(:,1:80,:),1))))));%+(squeeze(mean(mean(noise(1:10,:,:),2)))))/2);
@@ -116,6 +117,10 @@ function FS_AV_Parse(DIR,varargin)
 end
 clear noise;
 clear est;
+catch
+  video.gain = [];
+end
+
 
 
 % Format AUDIO DATA
@@ -134,8 +139,8 @@ function FS_AV_Parse(DIR,varargin)
 
         try
 		[s,f,t]=fb_pretty_sonogram(filtfilt(b,a,mic_data./abs(max(mic_data))),fs,'low',2.5,'zeropad',0);
-      
-        
+
+
 		minpt=1;
 		maxpt=min(find(f>=10e3));
 

README.md

@@ -11,7 +11,7 @@
 ## Overview
 The FinchScope project aims to provide a modular in-vivo  optophysiology rig, with a transparent acquisition and analysis pipeline. The goal is to produce a customizable and scaleable single-photon fluorescent imaging microscope system that takes advantage of developing open-source analysis platforms. You can read more about the project [here.](http://iopscience.iop.org/1741-2552/14/4/045001/)
 
-The project was developed by [Will Liberti](https://github.com/WALIII) in the [Gardner Lab](http://people.bu.edu/timothyg/Home.html), for multi-month imaging of neural activity in songbirds [[1]](https://doi.org/10.1371/journal.pbio.1002158) [[2]](https://www.nature.com/articles/nn.4405) [[3]](https://ccneuro.org/2018/proceedings/1133.pdf) and  rodents. This effort would not have been possible without the critical advice and support at the outset of the project by Daniel Aharoni and Peyman Golshani, of the [UCLA Miniscope project](http://miniscope.org/index.php?title=Main_Page)
+The project was developed by [Will Liberti](https://github.com/WALIII) in the [Gardner Lab](http://people.bu.edu/timothyg/Home.html), for multi-month imaging of neural activity in songbirds [[1]](https://doi.org/10.1371/journal.pbio.1002158) [[2]](https://www.nature.com/articles/nn.4405) [[3]](https://ccneuro.org/2018/proceedings/1133.pdf) and rodents. This effort would not have been possible without the critical advice and support at the outset of the project by Daniel Aharoni and Peyman Golshani, of the [UCLA Miniscope project](http://miniscope.org/index.php?title=Main_Page)
 
 
 
@@ -43,4 +43,3 @@ If you use any part of this project in your work, please cite out Journal of Neu
 * [Nathan Perkins]([email protected])
 * [Daniel Leman]([email protected])
 * [Ian Davison]([email protected])        
-

fdr_bh.m

@@ -0,0 +1,226 @@
+% fdr_bh() - Executes the Benjamini & Hochberg (1995) and the Benjamini &
+%            Yekutieli (2001) procedure for controlling the false discovery 
+%            rate (FDR) of a family of hypothesis tests. FDR is the expected
+%            proportion of rejected hypotheses that are mistakenly rejected 
+%            (i.e., the null hypothesis is actually true for those tests). 
+%            FDR is a somewhat less conservative/more powerful method for 
+%            correcting for multiple comparisons than procedures like Bonferroni
+%            correction that provide strong control of the family-wise
+%            error rate (i.e., the probability that one or more null
+%            hypotheses are mistakenly rejected).
+%
+%            This function also returns the false coverage-statement rate 
+%            (FCR)-adjusted selected confidence interval coverage (i.e.,
+%            the coverage needed to construct multiple comparison corrected
+%            confidence intervals that correspond to the FDR-adjusted p-values).
+%
+%
+% Usage:
+%  >> [h, crit_p, adj_ci_cvrg, adj_p]=fdr_bh(pvals,q,method,report);
+%
+% Required Input:
+%   pvals - A vector or matrix (two dimensions or more) containing the
+%           p-value of each individual test in a family of tests.
+%
+% Optional Inputs:
+%   q       - The desired false discovery rate. {default: 0.05}
+%   method  - ['pdep' or 'dep'] If 'pdep,' the original Bejnamini & Hochberg
+%             FDR procedure is used, which is guaranteed to be accurate if
+%             the individual tests are independent or positively dependent
+%             (e.g., Gaussian variables that are positively correlated or
+%             independent).  If 'dep,' the FDR procedure
+%             described in Benjamini & Yekutieli (2001) that is guaranteed
+%             to be accurate for any test dependency structure (e.g.,
+%             Gaussian variables with any covariance matrix) is used. 'dep'
+%             is always appropriate to use but is less powerful than 'pdep.'
+%             {default: 'pdep'}
+%   report  - ['yes' or 'no'] If 'yes', a brief summary of FDR results are
+%             output to the MATLAB command line {default: 'no'}
+%
+%
+% Outputs:
+%   h       - A binary vector or matrix of the same size as the input "pvals."
+%             If the ith element of h is 1, then the test that produced the 
+%             ith p-value in pvals is significant (i.e., the null hypothesis
+%             of the test is rejected).
+%   crit_p  - All uncorrected p-values less than or equal to crit_p are 
+%             significant (i.e., their null hypotheses are rejected).  If 
+%             no p-values are significant, crit_p=0.
+%   adj_ci_cvrg - The FCR-adjusted BH- or BY-selected 
+%             confidence interval coverage. For any p-values that 
+%             are significant after FDR adjustment, this gives you the
+%             proportion of coverage (e.g., 0.99) you should use when generating
+%             confidence intervals for those parameters. In other words,
+%             this allows you to correct your confidence intervals for
+%             multiple comparisons. You can NOT obtain confidence intervals 
+%             for non-significant p-values. The adjusted confidence intervals
+%             guarantee that the expected FCR is less than or equal to q
+%             if using the appropriate FDR control algorithm for the  
+%             dependency structure of your data (Benjamini & Yekutieli, 2005).
+%             FCR (i.e., false coverage-statement rate) is the proportion 
+%             of confidence intervals you construct
+%             that miss the true value of the parameter. adj_ci=NaN if no
+%             p-values are significant after adjustment.
+%   adj_p   - All adjusted p-values less than or equal to q are significant
+%             (i.e., their null hypotheses are rejected). Note, adjusted 
+%             p-values can be greater than 1.
+%
+%
+% References:
+%   Benjamini, Y. & Hochberg, Y. (1995) Controlling the false discovery
+%     rate: A practical and powerful approach to multiple testing. Journal
+%     of the Royal Statistical Society, Series B (Methodological). 57(1),
+%     289-300.
+%
+%   Benjamini, Y. & Yekutieli, D. (2001) The control of the false discovery
+%     rate in multiple testing under dependency. The Annals of Statistics.
+%     29(4), 1165-1188.
+%
+%   Benjamini, Y., & Yekutieli, D. (2005). False discovery rate?adjusted 
+%     multiple confidence intervals for selected parameters. Journal of the 
+%     American Statistical Association, 100(469), 71?81. doi:10.1198/016214504000001907
+%
+%
+% Example:
+%  nullVars=randn(12,15);
+%  [~, p_null]=ttest(nullVars); %15 tests where the null hypothesis
+%  %is true
+%  effectVars=randn(12,5)+1;
+%  [~, p_effect]=ttest(effectVars); %5 tests where the null
+%  %hypothesis is false
+%  [h, crit_p, adj_ci_cvrg, adj_p]=fdr_bh([p_null p_effect],.05,'pdep','yes');
+%  data=[nullVars effectVars];
+%  fcr_adj_cis=NaN*zeros(2,20); %initialize confidence interval bounds to NaN
+%  if ~isnan(adj_ci_cvrg),
+%     sigIds=find(h);
+%     fcr_adj_cis(:,sigIds)=tCIs(data(:,sigIds),adj_ci_cvrg); % tCIs.m is available on the
+%     %Mathworks File Exchagne
+%  end
+%
+%
+% For a review of false discovery rate control and other contemporary
+% techniques for correcting for multiple comparisons see:
+%
+%   Groppe, D.M., Urbach, T.P., & Kutas, M. (2011) Mass univariate analysis 
+% of event-related brain potentials/fields I: A critical tutorial review. 
+% Psychophysiology, 48(12) pp. 1711-1725, DOI: 10.1111/j.1469-8986.2011.01273.x 
+% http://www.cogsci.ucsd.edu/~dgroppe/PUBLICATIONS/mass_uni_preprint1.pdf
+%
+%
+% For a review of FCR-adjusted confidence intervals (CIs) and other techniques 
+% for adjusting CIs for multiple comparisons see:
+%
+%   Groppe, D.M. (in press) Combating the scientific decline effect with 
+% confidence (intervals). Psychophysiology.
+% http://biorxiv.org/content/biorxiv/early/2015/12/10/034074.full.pdf
+%
+%
+% Author:
+% David M. Groppe
+% Kutaslab
+% Dept. of Cognitive Science
+% University of California, San Diego
+% March 24, 2010
+
+%%%%%%%%%%%%%%%% REVISION LOG %%%%%%%%%%%%%%%%%
+%
+% 5/7/2010-Added FDR adjusted p-values
+% 5/14/2013- D.H.J. Poot, Erasmus MC, improved run-time complexity
+% 10/2015- Now returns FCR adjusted confidence intervals
+
+function [h, crit_p, adj_ci_cvrg, adj_p]=fdr_bh(pvals,q,method,report)
+
+if nargin<1,
+    error('You need to provide a vector or matrix of p-values.');
+else
+    if ~isempty(find(pvals<0,1)),
+        error('Some p-values are less than 0.');
+    elseif ~isempty(find(pvals>1,1)),
+        error('Some p-values are greater than 1.');
+    end
+end
+
+if nargin<2,
+    q=.05;
+end
+
+if nargin<3,
+    method='pdep';
+end
+
+if nargin<4,
+    report='no';
+end
+
+s=size(pvals);
+if (length(s)>2) || s(1)>1,
+    [p_sorted, sort_ids]=sort(reshape(pvals,1,prod(s)));
+else
+    %p-values are already a row vector
+    [p_sorted, sort_ids]=sort(pvals);
+end
+[dummy, unsort_ids]=sort(sort_ids); %indexes to return p_sorted to pvals order
+m=length(p_sorted); %number of tests
+
+if strcmpi(method,'pdep'),
+    %BH procedure for independence or positive dependence
+    thresh=(1:m)*q/m;
+    wtd_p=m*p_sorted./(1:m);
+
+elseif strcmpi(method,'dep')
+    %BH procedure for any dependency structure
+    denom=m*sum(1./(1:m));
+    thresh=(1:m)*q/denom;
+    wtd_p=denom*p_sorted./[1:m];
+    %Note, it can produce adjusted p-values greater than 1!
+    %compute adjusted p-values
+else
+    error('Argument ''method'' needs to be ''pdep'' or ''dep''.');
+end
+
+if nargout>3,
+    %compute adjusted p-values; This can be a bit computationally intensive
+    adj_p=zeros(1,m)*NaN;
+    [wtd_p_sorted, wtd_p_sindex] = sort( wtd_p );
+    nextfill = 1;
+    for k = 1 : m
+        if wtd_p_sindex(k)>=nextfill
+            adj_p(nextfill:wtd_p_sindex(k)) = wtd_p_sorted(k);
+            nextfill = wtd_p_sindex(k)+1;
+            if nextfill>m
+                break;
+            end;
+        end;
+    end;
+    adj_p=reshape(adj_p(unsort_ids),s);
+end
+
+rej=p_sorted<=thresh;
+max_id=find(rej,1,'last'); %find greatest significant pvalue
+if isempty(max_id),
+    crit_p=0;
+    h=pvals*0;
+    adj_ci_cvrg=NaN;
+else
+    crit_p=p_sorted(max_id);
+    h=pvals<=crit_p;
+    adj_ci_cvrg=1-thresh(max_id);
+end
+
+if strcmpi(report,'yes'),
+    n_sig=sum(p_sorted<=crit_p);
+    if n_sig==1,
+        fprintf('Out of %d tests, %d is significant using a false discovery rate of %f.\n',m,n_sig,q);
+    else
+        fprintf('Out of %d tests, %d are significant using a false discovery rate of %f.\n',m,n_sig,q);
+    end
+    if strcmpi(method,'pdep'),
+        fprintf('FDR/FCR procedure used is guaranteed valid for independent or positively dependent tests.\n');
+    else
+        fprintf('FDR/FCR procedure used is guaranteed valid for independent or dependent tests.\n');
+    end
+end
+
+
+
+