PyPop: a mature open-source software pipeline for population genomics

Python for Population Genomics (PyPop) is a software package that processes genotype and allele data and performs large-scale population genetic analyses on highly polymorphic multi-locus genotype data. In particular, PyPop tests data conformity to Hardy-Weinberg equilibrium expectations, performs Ewens-Watterson tests for selection, estimates haplotype frequencies, measures linkage disequilibrium, and tests significance. Standardized means of performing these tests is key for contemporary studies of evolutionary biology and population genetics, and these tests are central to genetic studies of disease association as well. Here, we present PyPop 1.0.0, a new major release of the package, which implements new features using the more robust infrastructure of GitHub, and is distributed via the industry-standard Python Package Index. New features include implementation of the asymmetric linkage disequilibrium measures and, of particular interest to the immunogenetics research communities, support for modern nomenclature, including colon-delimited allele names, and improvements to meta-analysis features for aggregating outputs for multiple populations. Code available at: https://zenodo.org/records/10080668 and https://github.com/alexlancaster/pypop.

The PyMVPA BIDS-App: a robust multivariate pattern analysis pipeline for fMRI data

With the advent of multivariate pattern analysis (MVPA) as an important analytic approach to fMRI, new insights into the functional organization of the brain have emerged. Several software packages have been developed to perform MVPA analysis, but deploying them comes with the cost of adjusting data to individual idiosyncrasies associated with each package. Here we describe PyMVPA BIDS-App, a fast and robust pipeline based on the data organization of the BIDS standard that performs multivariate analyses using powerful functionality of PyMVPA. The app runs flexibly with blocked and event-related fMRI experimental designs, is capable of performing classification as well as representational similarity analysis, and works both within regions of interest or on the whole brain through searchlights. In addition, the app accepts as input both volumetric and surface-based data. Inspections into the intermediate stages of the analyses are available and the readability of final results are facilitated through visualizations. The PyMVPA BIDS-App is designed to be accessible to novice users, while also offering more control to experts through command-line arguments in a highly reproducible environment.

Global analysis of the yeast knockout phenome

Genome-wide phenotypic screens in the budding yeast Saccharomyces cerevisiae, enabled by its knockout collection, have produced the largest, richest, and most systematic phenotypic description of any organism. However, integrative analyses of this rich data source have been virtually impossible because of the lack of a central data repository and consistent metadata annotations. Here, we describe the aggregation, harmonization, and analysis of ~14,500 yeast knockout screens, which we call Yeast Phenome. Using this unique dataset, we characterized two unknown genes (YHR045W and YGL117W) and showed that tryptophan starvation is a by-product of many chemical treatments. Furthermore, we uncovered an exponential relationship between phenotypic similarity and intergenic distance, which suggests that gene positions in both yeast and human genomes are optimized for function.

Sustainable data analysis with Snakemake

Data analysis often entails a multitude of heterogeneous steps, from the application of various command line tools to the usage of scripting languages like R or Python for the generation of plots and tables. It is widely recognized that data analyses should ideally be conducted in a reproducible way. Reproducibility enables technical validation and regeneration of results on the original or even new data. However, reproducibility alone is by no means sufficient to deliver an analysis that is of lasting impact (i.e., sustainable) for the field, or even just one research group. We postulate that it is equally important to ensure adaptability and transparency. The former describes the ability to modify the analysis to answer extended or slightly different research questions. The latter describes the ability to understand the analysis in order to judge whether it is not only technically, but methodologically valid. Here, we analyze the properties needed for a data analysis to become reproducible, adaptable, and transparent. We show how the popular workflow management system Snakemake can be used to guarantee this, and how it enables an ergonomic, combined, unified representation of all steps involved in data analysis, ranging from raw data processing, to quality control and fine-grained, interactive exploration and plotting of final results.

Sustainable data analysis with Snakemake

Data analysis often entails a multitude of heterogeneous steps, from the application of various command line tools to the usage of scripting languages like R or Python for the generation of plots and tables. It is widely recognized that data analyses should ideally be conducted in a reproducible way. Reproducibility enables technical validation and regeneration of results on the original or even new data. However, reproducibility alone is by no means sufficient to deliver an analysis that is of lasting impact (i.e., sustainable) for the field, or even just one research group. We postulate that it is equally important to ensure adaptability and transparency. The former describes the ability to modify the analysis to answer extended or slightly different research questions. The latter describes the ability to understand the analysis in order to judge whether it is not only technically, but methodologically valid. Here, we analyze the properties needed for a data analysis to become reproducible, adaptable, and transparent. We show how the popular workflow management system Snakemake can be used to guarantee this, and how it enables an ergonomic, combined, unified representation of all steps involved in data analysis, ranging from raw data processing, to quality control and fine-grained, interactive exploration and plotting of final results.

Ten simple rules for writing Dockerfiles for reproducible data science

Computational science has been greatly improved by the use of containers for packaging software and data dependencies. In a scholarly context, the main drivers for using these containers are transparency and support of reproducibility; in turn, a workflow's reproducibility can be greatly affected by the choices that are made with respect to building containers. In many cases, the build process for the container's image is created from instructions provided in a Dockerfile format. In support of this approach, we present a set of rules to help researchers write understandable Dockerfiles for typical data science workflows. By following the rules in this article, researchers can create containers suitable for sharing with fellow scientists, for including in scholarly communication such as education or scientific papers, and for effective and sustainable personal workflows.

The Scientific Filesystem

Background

Here, we present the Scientific Filesystem (SCIF), an organizational format that supports exposure of executables and metadata for discoverability of scientific applications. The format includes a known filesystem structure, a definition for a set of environment variables describing it, and functions for generation of the variables and interaction with the libraries, metadata, and executables located within. SCIF makes it easy to expose metadata, multiple environments, installation steps, files, and entry points to render scientific applications consistent, modular, and discoverable. A SCIF can be installed on a traditional host or in a container technology such as Docker or Singularity. We start by reviewing the background and rationale for the SCIF, followed by an overview of the specification and the different levels of internal modules (“apps”) that the organizational format affords. Finally, we demonstrate that SCIF is useful by implementing and discussing several use cases that improve user interaction and understanding of scientific applications. SCIF is released along with a client and integration in the Singularity 2.4 software to quickly install and interact with SCIF. When used inside of a reproducible container, a SCIF is a recipe for reproducibility and introspection of the functions and users that it serves.

Results

We use SCIF to evaluate container software, provide metrics, serve scientific workflows, and execute a primary function under different contexts. To encourage collaboration and sharing of applications, we developed tools along with an open source, version-controlled, tested, and programmatically accessible web infrastructure. SCIF and associated resources are available at https://sci-f.github.io. The ease of using SCIF, especially in the context of containers, offers promise for scientists’ work to be self-documenting and programatically parseable for maximum reproducibility. SCIF opens up an abstraction from underlying programming languages and packaging logic to work with scientific applications, opening up new opportunities for scientific software development.

Singularity: Scientific containers for mobility of compute

Here we present Singularity, software developed to bring containers and reproducibility to scientific computing. Using Singularity containers, developers can work in reproducible environments of their choosing and design, and these complete environments can easily be copied and executed on other platforms. Singularity is an open source initiative that harnesses the expertise of system and software engineers and researchers alike, and integrates seamlessly into common workflows for both of these groups. As its primary use case, Singularity brings mobility of computing to both users and HPC centers, providing a secure means to capture and distribute software and compute environments. This ability to create and deploy reproducible environments across these centers, a previously unmet need, makes Singularity a game changing development for computational science.

Predicting Prostate Cancer Recurrence After Radical Prostatectomy

BACKGROUND

Prostate cancer prognosis is variable, and management decisions involve balancing patients' risks of recurrence and recurrence-free death. Moreover, the roles of body mass index (BMI) and race in risk of recurrence are controversial [1,2]. To address these issues, we developed and cross-validated RAPS (Risks After Prostate Surgery), a personal prediction model for biochemical recurrence (BCR) within 10 years of radical prostatectomy (RP) that includes BMI and race as possible predictors, and recurrence-free death as a competing risk.

METHODS

RAPS uses a patient's risk factors at surgery to assign him a recurrence probability based on statistical learning methods applied to a cohort of 1,276 patients undergoing RP at the University of Pennsylvania. We compared the performance of RAPS to that of an existing model with respect to calibration (by comparing observed and predicted outcomes), and discrimination (using the area under the receiver operating characteristic curve (AUC)).

RESULTS

RAPS' cross-validated BCR predictions provided better calibration than those of an existing model that underestimated patients' risks. Discrimination was similar for the two models, with BCR AUCs of 0.793, 95% confidence interval (0.766-0.820) for RAPS, and 0.780 (0.745-0.815) for the existing model. RAPS' most important BCR predictors were tumor grade, preoperative prostate-specific antigen (PSA) level and BMI; race was less important [3]. RAPS' predictions can be obtained online at https://predict.shinyapps.io/raps.

CONCLUSION

RAPS' cross-validated BCR predictions were better calibrated than those of an existing model, and BMI information contributed substantially to these predictions. RAPS predictions for recurrence-free death were limited by lack of co-morbidity data; however the model provides a simple framework for extension to include such data. Its use and extension should facilitate decision strategies for post-RP prostate cancer management. Prostate 77:291-298, 2017. © 2016 Wiley Periodicals, Inc.

Sharing brain mapping statistical results with the neuroimaging data model

Only a tiny fraction of the data and metadata produced by an fMRI study is finally conveyed to the community. This lack of transparency not only hinders the reproducibility of neuroimaging results but also impairs future meta-analyses. In this work we introduce NIDM-Results, a format specification providing a machine-readable description of neuroimaging statistical results along with key image data summarising the experiment. NIDM-Results provides a unified representation of mass univariate analyses including a level of detail consistent with available best practices. This standardized representation allows authors to relay methods and results in a platform-independent regularized format that is not tied to a particular neuroimaging software package. Tools are available to export NIDM-Result graphs and associated files from the widely used SPM and FSL software packages, and the NeuroVault repository can import NIDM-Results archives. The specification is publically available at: http://nidm.nidash.org/specs/nidm-results.html.

2015 Brainhack Proceedings

I1 Introduction to the 2015 Brainhack Proceedings

R. Cameron Craddock, Pierre Bellec, Daniel S. Margules, B. Nolan Nichols, Jörg P. Pfannmöller

A1 Distributed collaboration: the case for the enhancement of Brainspell’s interface

AmanPreet Badhwar, David Kennedy, Jean-Baptiste Poline, Roberto Toro

A2 Advancing open science through NiData

Ben Cipollini, Ariel Rokem

A3 Integrating the Brain Imaging Data Structure (BIDS) standard into C-PAC

Daniel Clark, Krzysztof J. Gorgolewski, R. Cameron Craddock

A4 Optimized implementations of voxel-wise degree centrality and local functional connectivity density mapping in AFNI

R. Cameron Craddock, Daniel J. Clark

A5 LORIS: DICOM anonymizer

Samir Das, Cécile Madjar, Ayan Sengupta, Zia Mohades

A6 Automatic extraction of academic collaborations in neuroimaging

Sebastien Dery

A7 NiftyView: a zero-footprint web application for viewing DICOM and NIfTI files

Weiran Deng

A8 Human Connectome Project Minimal Preprocessing Pipelines to Nipype

Eric Earl, Damion V. Demeter, Kate Mills, Glad Mihai, Luka Ruzic, Nick Ketz, Andrew Reineberg, Marianne C. Reddan, Anne-Lise Goddings, Javier Gonzalez-Castillo, Krzysztof J. Gorgolewski

A9 Generating music with resting-state fMRI data

Caroline Froehlich, Gil Dekel, Daniel S. Margulies, R. Cameron Craddock

A10 Highly comparable time-series analysis in Nitime

Ben D. Fulcher

A11 Nipype interfaces in CBRAIN

Tristan Glatard, Samir Das, Reza Adalat, Natacha Beck, Rémi Bernard, Najmeh Khalili-Mahani, Pierre Rioux, Marc-Étienne Rousseau, Alan C. Evans

A12 DueCredit: automated collection of citations for software, methods, and data

Yaroslav O. Halchenko, Matteo Visconti di Oleggio Castello

A13 Open source low-cost device to register dog’s heart rate and tail movement

Raúl Hernández-Pérez, Edgar A. Morales, Laura V. Cuaya

A14 Calculating the Laterality Index Using FSL for Stroke Neuroimaging Data

Kaori L. Ito, Sook-Lei Liew

A15 Wrapping FreeSurfer 6 for use in high-performance computing environments

Hans J. Johnson

A16 Facilitating big data meta-analyses for clinical neuroimaging through ENIGMA wrapper scripts

Erik Kan, Julia Anglin, Michael Borich, Neda Jahanshad, Paul Thompson, Sook-Lei Liew

A17 A cortical surface-based geodesic distance package for Python

Daniel S Margulies, Marcel Falkiewicz, Julia M Huntenburg

A18 Sharing data in the cloud

David O’Connor, Daniel J. Clark, Michael P. Milham, R. Cameron Craddock

A19 Detecting task-based fMRI compliance using plan abandonment techniques

Ramon Fraga Pereira, Anibal Sólon Heinsfeld, Alexandre Rosa Franco, Augusto Buchweitz, Felipe Meneguzzi

A20 Self-organization and brain function

Jörg P. Pfannmöller, Rickson Mesquita, Luis C.T. Herrera, Daniela Dentico

A21 The Neuroimaging Data Model (NIDM) API

Vanessa Sochat, B Nolan Nichols

A22 NeuroView: a customizable browser-base utility

Anibal Sólon Heinsfeld, Alexandre Rosa Franco, Augusto Buchweitz, Felipe Meneguzzi

A23 DIPY: Brain tissue classification

Julio E. Villalon-Reina, Eleftherios Garyfallidis

Translational Meta-analytical Methods to Localize the Regulatory Patterns of Neurological Disorders in the Human Brain

The task of mapping neurological disorders in the human brain must be informed by multiple measurements of an individual's phenotype - neuroimaging, genomics, and behavior. We developed a novel meta-analytical approach to integrate disparate resources and generated transcriptional maps of neurological disorders in the human brain yielding a purely computational procedure to pinpoint the brain location of transcribed genes likely to be involved in either onset or maintenance of the neurological condition.

A robust classifier to distinguish noise from fMRI independent components

Analyzing Functional Magnetic Resonance Imaging (fMRI) of resting brains to determine the spatial location and activity of intrinsic brain networks--a novel and burgeoning research field--is limited by the lack of ground truth and the tendency of analyses to overfit the data. Independent Component Analysis (ICA) is commonly used to separate the data into signal and Gaussian noise components, and then map these components on to spatial networks. Identifying noise from this data, however, is a tedious process that has proven hard to automate, particularly when data from different institutions, subjects, and scanners is used. Here we present an automated method to delineate noisy independent components in ICA using a data-driven infrastructure that queries a database of 246 spatial and temporal features to discover a computational signature of different types of noise. We evaluated the performance of our method to detect noisy components from healthy control fMRI (sensitivity = 0.91, specificity = 0.82, cross validation accuracy (CVA) = 0.87, area under the curve (AUC) = 0.93), and demonstrate its generalizability by showing equivalent performance on (1) an age- and scanner-matched cohort of schizophrenia patients from the same institution (sensitivity = 0.89, specificity = 0.83, CVA = 0.86), (2) an age-matched cohort on an equivalent scanner from a different institution (sensitivity = 0.88, specificity = 0.88, CVA = 0.88), and (3) an age-matched cohort on a different scanner from a different institution (sensitivity = 0.72, specificity = 0.92, CVA = 0.79). We additionally compare our approach with a recently published method. Our results suggest that our method is robust to noise variations due to population as well as scanner differences, thereby making it well suited to the goal of automatically distinguishing noise from functional networks to enable investigation of human brain function.