LI Detector

A framework for sensitive colony-based screens regardless of the distribution of fitness effects

Pre-print available on bioRxiv.

1. Initialize

Generating tables necessary for the effective use of the LI Detector

initialize.m

1. Information to keep in hand before proceeding:
   • MySQL credentials - username, password, database name
   • Upscale patterns from the experiment - ie in what combinations were the lower density plates condensed to form the higher density plates
   • Name of reference strain being used
   • Plate maps of the starting density plate
     • One plate per sheet in excel
     • Cells contain strain-id
     • File should be in .xlsx format
     • Example
   • Excel table specifying strain-id to orf-name relationships
     • First column is strain_id
     • Second column is orf_name (should include the reference strain)
     • Unique strain-ids for each orf (mutant strain)
     • Each strain-id from the platemaps should have an associated orf-name
     • File should be in .xlsx format
     • Example
2. Successful run will create the following tables:
   • _borderpos = border positions of all plates in the experiment
     • 1 border for 384 density, 2 for 1536 and 4 for 6144
   • _pos2coor = position ids and their corresponding plate coordinate
     • unique position ids for all possible colony positions in the experiment and thei correspoing plate coordinates ie colony density, plate number, row number and column number
   • _pos2orf_name = position ids and the corresponding orf-name (or mutant name)
   • _pos2rep = position ids of lowest density plates to their replicates at higher density plates based on the upscale pattern
     • for internal use
   • _pos2strain_id = position ids and their corresponding strain ids
   • _strainid2orf_name = same as excel table from above
3. Example files can be found in Data.zip.

2. Analyze Images

Pixel count estimation from pictures using the MATLAB Colony Analyzer Toolkit

imageanalyzer.m

1. Information to keep in handy before proceeding:
   • Location of any smudges on the plates ie the colonies you want to remove from the analysis because of any technical issues
     • plate number, row number, column number
2. User will be asked to verify binary files before uploading raw pixel count data
   • Each image will now have 3 additional files - .binary, .cs.txt and .info.mat
   • View the .binary file (using Preview in Mac) to verify if the colonies have been correctly identified
3. Successful run will create the following tables:
   • _RAW = raw colony size estimations per hour per position id of all the images
     • image1, image2 and image3 columns correspond to the three images per plate
     • average column is the mean of the pixel count estimation from the three images
       • image1 = image2 = image3 = average if there is a single image per plate
   • _smudgebox = position ids corresponding to the user defined coordinates
   • _JPEG = similar to _RAW with
     • pixel count estimations for borders and smudgebox NULL'd
     • and any pixel count estimation < 10 is also NULL'd - likely to be a light artifact
4. If the images are already analyzed using a different software then make sure the colony sizes in the _JPEG table are arranged in ascending order of hours, plate number, column number, row number.
5. Example files can be found in Data.zip.

3. Spatial Bias Correction

Relative fitness measurements and p-value estimation from colony-size data

lid.m

1. Successful run will create the following tables:
   • _NORM = position ids and their corresponding relative fitness measurements
     • also includes the background pixel count measurement based on references
   • _FITNESS = similar to _NORM but with strain ids and orf-names included
   • _FITNESS_STAT = strain-id-wise mean, median and standard deviation of relative fitness
   • _PVALUE = strain-id-wise empirical p-values
     • stat = (strain mean fitness - reference mean fitness)/reference fitness standard deviation
     • es = (strain mean fitness - reference mean fitness)reference mean fitness
2. Example files can be found in Data.zip.