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Data-driven aggregation of ranked and unranked lists
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README.md
baseline.py
cross_validation.py
entity.py
entitylist.py
entitylist_builder.py
file_reader.py
genescores_dumper.py
maic.py
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README.md

Version 0.2

Authors

A Law D Farr B Wang JK Baillie

Meta-analysis by information content (MAIC)

Data-driven aggregation of ranked and unranked lists

https://baillielab.net/maic

basic usage

python maic.py -f

Input file format

Input is a series of lists of named entities, which may belong to categories. Each line of the input file is a list of entities, separated by tab The first four columns (tab-separated text strings) in each line specify features of the list in this line: <list_label> RANKED entity1 entity2 entity3 ... <list_label> RANKED entity1 entity2 entity3 ... <list_label> UNRANKED entity1 entity2 entity3 ... <list_label> UNRANKED entity1 entity2 entity3 ...

Options

-f FILENAME, --filename FILENAME path to the file containing data to be analysed -z, --z default=False. Use a z-score from permuted lists -t, Transform method -b [BASELINE], --baseline [BASELINE] Correct scores using a baseline calculated from random data matching the input data set. Optionally specify the path to a file in which pre-calculated baselines are stored (default is ${HOME}/.maic-baselines.txt. -g [GENE_SCORE_OUTPUT_FILE], --gene-score-output-file [GENE_SCORE_OUTPUT_FILE] -c {0.0..1.0}, --chop-threshold {0.0..1.0} 0.0 < chop-threshold < 1.0 -m MAX_INPUT_LEN, --max-input-len MAX_INPUT_LEN maximum list length to include -n NUM_PERMS, --num-perms NUM_PERMS number of permutations (to generate z score) -e, --exclude-current-gene default=False. Less biased but much much slower. Does not significantly affect results. -w {none,knn,polynomial,exponential,svr}, --weight-function {none,knn,polynomial,exponential,svr} weighting function to use for ranked lists -v, --verbose increase the detail of logging messages. -q, --quiet decrease the detail of logging messages (overrides the -v/--verbose flag)

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