Tn-seq data processing scripts
calc_bottleneck_fr.py takes an input directory, containing fitness files, and for each file, computes a bottleneck value. The bottleneck is defined as the proportion of TA insertion sites that had positive counts at time 1, but 0 counts at time 2. For robustness, only positions with >50 counts at time 1 are considered.
This script will generate a tab separated table which contains the bottleneck value for each input file.
Example usage:
python calc_bottleneck_fr.py -i [inputdirectory] -o [outputfile]
Where [inputdirectory] is a directory containing Fitness files, and [outputfile] is the name of a tab separated file to be generated. This file will have the bottleneck value corresponding to each of the files in the [inputdirectory].
process_single_expt.R is a function that takes in an aggregate file, and performs a 2-sample t-test with B-H correction on each gene. The observed fitness of each gene is compared to an expected fitness for the experiment (which is the median fitness across all genes). In the output, dW (difference in fitness) is real-expected fitness. This script will produce a [name]_discovery.csv table that contains the dW and associated statistical comparison, as well as a volcano plot.
Example usage:
process_single_expt(inputfilename, essentialsfile, N, outputdirectory)
inputfilename is the input aggregate file
essentialsfile is a csv file that contains gene essentiality data for the strain that is being processed. In the TvO lab, we use the Essentials.csv file, which contains gene essentiality data for 2 Streptococcus pneumoniae strains
N is a threshold for the number of insertions in a given gene. Genes with fewer than N insertions (after bottleneck correction) will be filtered out and excluded from the analysis. Higher N would be more conservative. For in vitro experiments, N=3 can be suitable, but experiments with high variability might require N=6 or higher.
outputdirectory is the name of the output directory where output tables and figures will be saved. If the directory doesn't already exist, it will be created.
compare_two_expts.R takes in two aggregate files, and performs a 2-sample t-test with B-H correction on each gene. In the output, dW (difference in fitness) is the difference in fitness between experiment 1 and 2 (fitness1-fitness2). This script will produce a [name1]_[name2]_discovery.csv table that contains the dW and associated statistical comparison, a volcano plot, and a scatter plot showing fitness values for each gene in both experiments.
Example usage:
compare_two_expts(inputfilename1, inputfilename2, essentialsfile, N, outputdirectory)
inputfilename1 is the input aggregate file for experiment 1
inputfilename2 is the input aggregate file for experiment 2
essentialsfile is a csv file that contains gene essentiality data for the strain that is being processed. In the TvO lab, we use the Essentials.csv file, which contains gene essentiality data for 2 Streptococcus pneumoniae strains
N is a threshold for the number of insertions in a given gene. Genes with fewer than N insertions (after bottleneck correction) will be filtered out and excluded from the analysis.
outputdirectory is the name of the output directory where output tables and figures will be saved. If the directory doesn't already exist, it will be created.