Skip to main content

Microbiome Interpretable Temporal Rule Engine

Project description

# The Microbiome Interpretable Temporal Rule Engine

![MITRE schematic]( "MITRE schematic")

MITRE learns predictive models of patient outcomes from microbiome
time-series data in the form of short lists of interpretable rules.

See an [example of MITRE's interactive visualization output](

### Installation

#### From PyPI (recommended):
$ pip install mitre

#### From source:
$ git clone
$ pip install mitre/

To check that installation was successful, run

$ mitre --test

A series of status messages should be displayed, followed by 'Test problem completed successfully.'

Only Mac and Linux systems are supported at this time.

### Quick start

MITRE operation is controlled by a configuration file. To try it out,
copy the following into a file called 'demo.cfg':

verbose = True

load_example = bokulich

min_overall_abundance = 10
min_sample_reads = 5000
trim_start = 0
trim_stop = 375
density_filter_n_samples = 1
density_filter_n_intervals = 12
density_filter_n_consecutive = 2
take_relative_abundance = True
aggregate_on_phylogeny = True
temporal_abundance_threshold = 0.0001
temporal_abundance_consecutive_samples = 3
temporal_abundance_n_subjects = 10
discard_surplus_internal_nodes = True

n_intervals = 12
t_min = 1.0
t_max = 180.0

total_samples = 300

burnin_fraction = 0.05
bayes_factor_samples = 1000
quick_summary = True
full_summary = True
gui_output = True

(If you've downloaded the MITRE source code, you can copy demo.cfg
from the root directory.)

Then run

$ mitre demo.cfg

in the same directory. It should take 15-20 minutes to run. Here's what will happen:

MITRE will load data from Bokulich, N. A., *et al.*, ["Antibiotics,
birth mode, and diet shape microbiome maturation during early
life."]( *Science
Translational Medicine* **8**(343): 343ra82, which is packaged with

Then, it will apply a series of filters:

- excluding OTUs and samples with too few associated reads
- truncating the experiment at day of life 375,
- Dividing the 375-day study into 12 intervals and discarding subjects without at least 1 sample in any consecutive 2 intervals.

Next, counts data will be converted to relative abundance,
and new variables representing the aggregated abundance of
every subtree in a phylogenetic tree relating the OTUs will be created.
Variables not exceeding an abundance threshold will be dropped.

A MITRE model object will be set up and 300 samples will be drawn from the posterior
distribution over the space of valid *rule sets* relating the microbiome time series data to
the outcome of interest.

Three output files summarizing the samples will be written. In this
demo, we aren't drawing enough samples to get reliable statistics, so
the results may vary. But bokulich_diet_quick_summary.txt might look something like this:

Rule list with 1 rules (overall likelihood -7.14):

Rule 0 (coefficient 11.5 (5.28 -- 13.2)):
Odds of positive outcome INCREASE by factor of 9.57e+04 (196 - 5.61e+05), if:
Between time 93.750 and time 156.250, variable 13231 average is above 0.1309
This rule applies to 9/35 (0.257) subjects in dataset, 9/9 with positive outcomes (1.000).

Constant term (coefficient -2.81 (-4.1 -- -1.72)):
Positive outcome probability 0.0566 (0.0163 -- 0.152) if no other rules apply

This is the single best estimated rule set. It indicates that subjects
with a high abundance of group 13231 in the indicated time window are
likelier to have been fed predominantly a formula-based diet. In
bokulich_diet_variable_annotations.txt, we can look up group 13231 and
learn it's "a clade within phylum Firmicutes,including representatives
of class Clostridia, Bacilli"- which isn't too enlightening on its
own, but the same line lists the leaves of the tree that belong to
this group; looking them up in turn, we find this group includes
mostly OTUs from the genera *Clostridia* and *Blautia*. Ranges in parentheses
are 95% confidence intervals.

Looking farther down the file, we find a confusion matrix showing that
this rule set correctly identifies 9 of the 11 subjects in the group
with a formula-dominant diet, with no false positives.

For an interactive representation, open bokulich_diet_visualization.html and click on the
heat map to explore high-probability detectors. It might
look like a noiser version of the example linked above.

For more details, see the user guide (docs/manual.pdf) and the text
and supplementary note of the MITRE manuscript (reference below.)

### References

"MITRE: predicting host status from microbiota time-series data", Elijah Bogart and Georg K. Gerber (in preparation)

### License information

Copyright 2017 Eli Bogart, the Gerber Lab, and Brigham and Women's Hospital.
Released under the GNU General Public License version 3.0, see LICENSE.txt.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for mitre, version 0.9b4
Filename, size File type Python version Upload date Hashes
Filename, size mitre-0.9b4-cp27-cp27m-macosx_10_6_x86_64.whl (9.7 MB) File type Wheel Python version cp27 Upload date Hashes View
Filename, size mitre-0.9b4.tar.gz (9.5 MB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page