learn_model

Contents

• learn_model
  • Overview
  • Command-line usage
  • Example Input and Output

Overview

learn_model is a program within the sortseq_tools package which generates linear energy matrix models for sections of a sorted library.

After you install `sortseq_tools`_, this program will be available to run at the command line.

Command-line usage

usage: sortseq learn_model [-h] [-s START] [-e END] [-t {dna,rna,protein}]
                           [-et {sortseq,mpra,selection}]
                           [-lm {least_squares,lasso,MImax}]
                           [--initialize {Rand,LeastSquares}] [-rn RUNNUM]
                           [-db DB_FILENAME] [-iter NUMITERATIONS] [-b BURNIN]
                           [-th THIN] [-i I] [-o OUT]

Options:

-s=0, --start=0
	Position to start your analyzed region
-e, --end	Position to end your analyzed region
-t=dna, --type=dna
	Undocumented Possible choices: dna, rna, protein
-et=sortseq, --exptype=sortseq
	Type of experiment. Possible choices: sortseq, mpra, selection
-lm=least_squares, --learningmethod=least_squares
	Algorithm for determining matrix parameters. Possible choices: least_squares, lasso, MImax
--initialize=Rand
	How to choose starting point for MCMC Possible choices: Rand, LeastSquares
-rn=0, --runnum=0
	For multiple runs this will change output data base file name
-db, --db_filename
	For MImax, If you wish to save the trace in a database, put the name of the sqlite data base
-iter=30000, --numiterations=30000
	For MImax, Number of MCMC iterations
-b=1000, --burnin=1000
	For MImax, Number of burn in iterations
-th=10, --thin=10
	For MImax, this option will set the number of iterations during which only 1 iteration will be saved.
-i=False, --i=False
	Read input from file instead of stdin
-o, --out	Undocumented

Example Input and Output

The input table to this program must contain a sequences column and counts columns for each bin. For a sort seq experiment, this can be any number of bins. For MPRA and selection experiments this must be ct_0 and ct_1.

Example Input Table:

seq    ct_0     ct_1     ct_2    ...
ACG    1        5        7
GGT    8        5        5
...

Example Output Table:

pos    val_A    val_C    val_G     val_T
0      .04      -.3      -.2       .15
1      .2       .1       -.44      .05
...