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This repository contains code and relevant files for the Latent Structure
modeling project described in:
Poldrack RA, Mumford JA, Schonberg T, Kalar D, Barman B, Yarkoni T (2012). Discovering relations between mind, brain, and mental disorders using topic mapping. PLOS Computational Biology, in press.
Preprint available at:
http://talyarkoni.org/papers/Poldrack_et_al_in_press_PLoS_CompBio.pdf
Guide to subdirectories:
CCA - contains results from CCA analysis
NIF-Disorders - contains files used for disorders topic modeling
clustering - contains files used for clustering of disorders
cogatlas - contains files used for cogatlas topic modeling
src: contains the source files used for all analyses. these require the following
external libraries:
http://numpy.scipy.org/
http://scikit-learn.org/stable/
http://rpy.sourceforge.net/rpy2.html
http://nipy.sourceforge.net/nibabel/
also note that src/utils needs to be in the python path
1_db_foci_to_image.py - obtains coordinates from neurosynth database and creates images
- uses utils/tal_to_mni.py
1.1_merge_peakfiles.sh - creates merged image and computes mask of voxels with activation
on at least 1% of papers
1.2_extract_nidag_text.py - extract full text of each article from database
2_get_cogat_concepts.py - read cognitive atlas concepts from RDF and get loading for each document
3_create_disorder_list.py - load NIF dysfunction ontology, grab synonyms, and add
additional missing terms
4_get_disorders_NIF.py - get loading for each disorder term from corpus
5_mk_cogatlas_docs.py - make documents based on cogatlas loadings
6_mk_disorders_docs.py - make documents based on disorder loadings
7_mk_pickled_data.py - created a pickle with the full dataset to make loading easier
8_mk_8fold_cogatlas.py - make files for 8-fold CV
9_mk_8fold_disorders.py - make files for 8-fold CV
10_mk_8fold_cogatlas_mallet_data.py - make mallet data from 8-fold files
11_mk_8fold_disorders_mallet_data.py - make mallet data from 8-fold files
12_mk_8fold_cogatlas_topic_models.py - create scripts to run mallet jobs
13_mk_8fold_disorders_topic_models.py - create scripts to run mallet jobs
14_get_best_topic_likelihood.py - check likelihoods to get get dimenstionality
15.1_get_disorders_dimensionality.py - generate additional topic models to get disorder dimensionality
15.3_get_best_disorder_dimensionality.py - get dimensionality that has unique topic dists
15_run_topic_models.py - generate scripts to run final topic models
16_mk_cogatlas_loadingdata.py - load topic data and save loadingdata.txt
17_mk_disorders_loadingdata.py - load topic data and save loadingdata.txt
18_mk_all_chisq_maps.py - make all chisquare maps - uses utils/mk_chisq_maps_filter.py
18.2_mk_6mm_chisq_maps.sh - make 6mm versions of topic
19_mk_slice_images_cogatlas.py - make slice images using p values
20_mk_slice_images_disorders.py - make slice images using p values
22_mk_latexreport_cogatlas.py - create latex report
23_mk_latexreport_disorders.py - create latex report
24_run_CCA_nonneg.py - run CCA analysis
25_cluster_disorders.R - run clustering on disorders data
26_make_cognitive_topic_figure.py - generate figure 2 from initial submission
27_make_disorders_topic_figure.py - generate figure 3 from initial submission
28_mk_8fold_fulltext_topic_models.py - create scripts to run fulltext topic models
28.1_mk_8fold_fulltext_mallet_data.py - make 8-fold data for full text analysis
28.2_run_all_fulltext_nfold.sh - run mallet jobs on 8-fold full text
28.3_get_best_dimensionality.py - get best dimensionality for full text
30_get_wordhist.py - get histograms of # of docs for each filtered paper
31_count_locations.py - count # of locations reported across all papers
32_doc_topic_hist.py - make histograms of docs/topic and topics/doc (for Figure 3)
33_plot_likelihood.py - plot empirical likelihood as function of # of topics (for Figure 2)
34_run_CCA_on_topics.py - run CCA directly on topic distributions
35_mk_fold1_loadingdata.py - get loadingdata for fold1 for each ntopics
36.1_mk_slice_corr_images.disorders.py - make images using correlation rather than p value
36_mk_slice_corr_images.cogatlas.py - make images using correlation rather than p value (for figure
37_make_cognitive_topic_corr_figure.py - make Figure 4
38_make_disorder_topic_corr_figure.py - make Figure 6
39_get_topic_hierarchy.py - create graphviz .dot file for topic hierarchy (Figure 5)
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