DeBias decouples global bias from direct interactions in co-orientation and co-localization data.
Matlab Java
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February 1st, 2017

This repository includes DeBias' Matlab source code and test data

Apply DeBias:

  1. Calculate GI and LI with DeBias for a set of observations x, y, output plots to outDname. The only required parameters are x,y,isCoOrientation, see documentation in the code regarding default values

    • [GI,LI] = executeDebias(x,y,isCoOrientation,outDname,name,k,nSimulations)
  2. Execute DeBias on all files in a directory. Supports input file formats .xls, .txt (two columns with the matched x,y observations) and .mat (variables x,y with the matched observations).

    • executeDebiasDirectory(dname,isCoOrientation,k)


  • Generate X,Y values through simulations and use executeDebias
    1. Co-orientation simulation (see Figure 1B):
      • [X,Y] = generateCoOrientSimulationData(muX,sX,muY,sY,alpha,N);
    2. Colocalization simulation (see Appendix 3 - figure 1A):
      • [X,Y] = generateColocSimulationData(sX,sZ,N,fracColoc)

Example data:

  • Avaialble at the testData folder. Three experimental instances for co-orientation, one for co-localization and a simulated orientation dataset that include 4 instances from 2 different scenarios (Fig. 1C; data11-14: 𝜎X = 𝜎Y = 21, α = 0.2; data21-24: 𝜎X = 𝜎Y = 29, α = 0.4).

  • The most recommended way to start working with the source code is to generate simulated data and try DeBias on it. For example:

    • [X,Y] = generateCoOrientSimulationData(0,30,0,30,0.3,1000);
    • [GI,LI] = executeDebias(X,Y,true);

DeBias Analyst:

  • Compares multiple GI,LI values derived from two different experimental conditions.
  • Outputs (GI,LI) plots (such as in Fig. 3D in the manuscript) and statistical significance of the GI and LI values across these conditions.
  • See documentation in the code for details
    • [GIpval,LIpval] = DeBiasAnalyst(GIs1,LIs1,GIs2,LIs2,str1,str2,outdir);

Automatic estimation of K:

  • Estimate K (number of histogram bins for EMD calculations) using the Freedman Diaconis rule.
  • DeBias' ability to discriminate between different experiemtnal conditions are not sensitive to the choice of K
  • (In the manuscript: Figure 3 - figure supplement 1D-E, Figure 6 - figure supplement 1I-J, Appendix 2 - figure 2, Appendix 3 - figure 1F).
  • This function is provided for the curious user ;-)
  • data - is the alignment data
    • K = DeBiasGetK(data,isCoOrientation)


Please contact Assaf Zaritsky,, for any questions / suggestions.

For more work from the Danuser Lab:

(and software: )