Improving phenotips' differential diagnosis tool
These scripts all take a directory of patient phenotype descriptions, and write out suggestions to a directory, running some sort of differential diagnosis algorithm on those patients.
diagnose_phenomizer_boring_pval.py: finds differential diagnoses for every patient in directory, uses ancestor overlap and information content $1 -> directory containing patients. Each patient is a comma-separated list of HPO terms. $2 -> file containing information content for each HPO term. Two tab-separated columns: HPO term on left, IC on right. $3 -> out directory. Files are 20 lines, the top disease suggestions. They are tab-separated: p-value, score, disease code. $4 -> directory containing p-values. Contains one subfolder for each disease. Each disease subfolder contains 10 files, for each query size from 1 to 10. Each file has one float per line, smallest number at the top. $5 -> normalization option. 'True' runs like simgic with p-values, 'half' will normalize by query information content, 'False' does not normalize.
diagnose_phenomizer_boring.py: same as above, without p-values, and no normalization. $1, $2, $3 -> in dir, ic file, out dir, same as above.
diagnose_phenomizer_simgic_pval.py: finds differential diagnoses for every patient in directory, uses simgic score - information content of ancestor overlap, intersection over union $1, $2, $3, $4 -> in dir, ic file, out dir, pval dir, same as above.
diagnose_phenomizer_simgic.py: same as above, without p-values. $1, $2, $3 -> in dir, ic file, out dir, same as above.
diagnose_phenomizer_mica.py: finds differential diagnoses for every patient in directory, uses most informative common ancestor algorithm (a la Phenomizer) $1 -> in directory, as above $2 -> out directory, as above
The following scripts are used to make the p-value distributions.
make_distribution.py: creates simgic distribution for a given disease. $1 -> disease code. $2 -> out dir. Writes 10 files, one for each query size from 1-10, with 100000 sorted random observations for each. $3 -> ic file, as above.
make_distribution_boring.py: creates unnormalized simgic distribution for given disease. $1, $2, $3 -> same as above.
dispatch_pval_simgic.sh, dispatch_pval_boring.sh: dispatches jobs across cluster to create above distributions in parallel. $1 -> determines which ic file to use.
The following scripts are used to get results from Phenomizer or Phenotips.
phenomizer_get.sh: uses wget queries to get diagnosis results from Phenomizer at compbio.charite.de. phenotips_get.sh: uses wget queries to get diagnosis results from Phenotips at playground.phenotips.org. phenotips_get_diff.sh: uses wget queries to get diff-diagnosis phenotype suggestions from Phenotips at playground.phenotips.org.
parse_phenotips.py, parse_phenotips_diff.py: parse the returns from their respective shell scripts. Take $1, $2 -> in dir, out dir.
The following scripts score a results directory from a disease suggestion script.
score_phenomizer.py: Creates score.log in results directory, containing info of how many top 1, top 5, and top 20 hits this directory contains. $1 -> in dir. Filenames are formatted 'orphanetnumber_indexnumber_hpo.txt'.
score_results.sh: Prints out distribution of hits from 1 to 20, and the number of misses. $1 -> in dir. There is a diagnosis file which maps patient names to correct diagnoses.
These files are not run to create any results, but they contain functions used by other scripts.
get_hp_ic.py hpo_lib.py: has many HPO manipulation functions. hpo.py: loads HPO, has many HPO manipulation functions. omim.py: does parsing of phenotype annotation file. orpha.py: does mapping between Orphanet and OMIM.