[DOI] 10.5524/100819

[Title] Supporting data for "Trajectories, bifurcations and pseudotime in large
clinical datasets: applications to myocardial infarction and diabetes data"

[Release Date] 2020-10-20

[Citation] Golovenkin, S; Bac, J; Chervov, A; Mirkes, E; Orlova, Y; Barillot, E;
Gorban, A; Zinovyev, A (2020): Supporting data for "Trajectories, bifurcations
and pseudotime in large clinical datasets: applications to myocardial infarction
and diabetes data" GigaScience Database. https://dx.doi.org/10.5524/100819

[Data Type] Software,Metadata

[Data Summary] Large observational clinical datasets become increasingly
available for mining associations between various disease traits and
administered therapy. These datasets can be considered as representations of the
landscape of all possible disease conditions, in which a concrete pathology
develops through a number of stereotypical routes, characterized by `points of
no return' and `final states' (such as lethal or recovery states). Extracting
this information directly from the data remains challenging, especially in the
case of synchronic (with a short-term follow up) observations. Here we suggest a
semi-supervised methodology for the analysis of large clinical datasets,
characterized by mixed data types and missing values, through modeling the
geometrical data structure as a bouquet of bifurcating clinical trajectories.
The methodology is based on application of elastic principal graphs which can
address simultaneously the tasks of dimensionality reduction, data
visualization, clustering, feature selection and quantifying the geodesic
distances (pseudotime) in partially ordered sequences of observations. The
methodology allows positioning a patient on a particular clinical trajectory
(pathological scenario) and characterizing the degree of progression along it
with a qualitative estimate of the uncertainty of the prognosis. Overall, our
pseudotime quantification-based approach gives a possibility to apply the
methods developed for dynamical disease phenotyping and illness trajectory
analysis (diachronic data analysis) to synchronic observational data. We
developed a tool <a href="https://github.com/sysbio-curie/ClinTrajan">
ClinTrajan</a> for clinical trajectory analysis implemented in Python
programming language. We test the  methodology in two large publicly available
datasets: myocardial infarction complications and readmission of diabetic
patients data.

[File Location] https://s3.ap-northeast-1.wasabisys.com/gigadb-datasets/live/pub/10.5524/100001_101000/100819/

[File name] - [File Description]
scikit-dimension-0.1.zip  -  Archival copy of the GitHub repository https://github.com/sysbio-curie/scikit-dimension/releases/tag/v0.1 downloaded 20-Oct-2020. scikit-dimension. This project is licensed under the BSD 3-Clause license. Please refer to the GitHub repo for most recent updates.
ClinTrajan-1.0.zip  -  Archival copy of the GitHub repository https://github.com/sysbio-curie/ClinTrajan/releases/tag/v1.0 downloaded 20-Oct-2020. ClinTrajan - Methodology and software for quantifying pseudotemporal trajectories in clinical datasets. This project is licensed under the LGPL license. Please refer to the GitHub repo for most recent updates.
readme_100819.txt  -  

[License]
All files and data are distributed under the CC0 1.0 Universal (CC0 1.0) Public
Domain Dedication (https://creativecommons.org/publicdomain/zero/1.0/), unless
specifically stated otherwise, see http://gigadb.org/site/term for more details.

[Comments]

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