Data Analysis Toolbox Research Articles

Begonia-A Two-Photon Imaging Analysis Pipeline for Astrocytic Ca2+ Signals.

Imaging the intact brain of awake behaving mice without the dampening effects of anesthesia, has revealed an exceedingly rich repertoire of astrocytic Ca2+ signals. Analyzing and interpreting such complex signals pose many challenges. Traditional analyses of fluorescent changes typically rely on manually outlined static region-of-interests, but such analyses fail to capture the intricate spatiotemporal patterns of astrocytic Ca2+ dynamics. Moreover, all astrocytic Ca2+ imaging data obtained from awake behaving mice need to be interpreted in light of the complex behavioral patterns of the animal. Hence processing multimodal data, including animal behavior metrics, stimulation timings, and electrophysiological signals is needed to interpret astrocytic Ca2+ signals. Managing and incorporating these data types into a coherent analysis pipeline is challenging and time-consuming, especially if research protocols change or new data types are added. Here, we introduce Begonia, a MATLAB-based data management and analysis toolbox tailored for the analyses of astrocytic Ca2+ signals in conjunction with behavioral data. The analysis suite includes an automatic, event-based algorithm with few input parameters that can capture a high level of spatiotemporal complexity of astrocytic Ca2+ signals. The toolbox enables the experimentalist to quantify astrocytic Ca2+ signals in a precise and unbiased way and combine them with other types of time series data.

Giotto: a toolbox for integrative analysis and visualization of spatial expression data

Spatial transcriptomic and proteomic technologies have provided new opportunities to investigate cells in their native microenvironment. Here we present Giotto, a comprehensive and open-source toolbox for spatial data analysis and visualization. The analysis module provides end-to-end analysis by implementing a wide range of algorithms for characterizing tissue composition, spatial expression patterns, and cellular interactions. Furthermore, single-cell RNAseq data can be integrated for spatial cell-type enrichment analysis. The visualization module allows users to interactively visualize analysis outputs and imaging features. To demonstrate its general applicability, we apply Giotto to a wide range of datasets encompassing diverse technologies and platforms.

NIRS-KIT: a MATLAB toolbox for both resting-state and task fNIRS data analysis.

.Significance: Functional near-infrared spectroscopy (fNIRS) has been widely used to probe human brain function during task state and resting state. However, the existing analysis toolboxes mainly focus on task activation analysis, few software packages can assist resting-state fNIRS studies.Aim: We aimed to provide a versatile and easy-to-use toolbox to perform analysis for both resting state and task fNIRS.Approach: We developed a MATLAB toolbox called NIRS-KIT that works for both resting-state analysis and task activation detection.Results: NIRS-KIT implements common and necessary processing steps for performing fNIRS data analysis, including data preparation, quality control, preprocessing, individual-level analysis, group-level statistics with several popular statistical models, and multiple comparison correction methods, and finally results visualization. For resting-state fNIRS analysis, functional connectivity analysis, graph theory-based network analysis, and amplitude of low-frequency fluctuations analysis are provided. Additionally, NIRS-KIT also supports activation analysis for task fNIRS.Conclusions: NIRS-KIT offers an open source tool for researchers to analyze resting-state and/or task fNIRS data in one suite. It contains several key features: (1) good compatibility, supporting multiple fNIRS recording systems, data formats of NIRS-SPM and Homer2, and the shared near-infrared spectroscopy format data format recommended by the fNIRS society; (2) flexibility, supporting customized preprocessing scripts; (3) ease-to-use, allowing processing fNIRS signals in batch manner with user-friendly graphical user interfaces; and (4) feature-packed data viewing and result visualization. We anticipate that this NIRS-KIT will facilitate the development of the fNIRS field.

4DNvestigator: time series genomic data analysis toolbox

ABSTRACT Data on genome organization and output over time, or the 4D Nucleome (4DN), require synthesis for meaningful interpretation. Development of tools for the efficient integration of these data is needed, especially for the time dimension. We present the ‘4DNvestigator’, a user-friendly network-based toolbox for the analysis of time series genome-wide genome structure (Hi-C) and gene expression (RNA-seq) data. Additionally, we provide methods to quantify network entropy, tensor entropy, and statistically significant changes in time series Hi-C data at different genomic scales.

Multi-contact 4C: long-molecule sequencing of complex proximity ligation products to uncover local cooperative and competitive chromatin topologies.

We present the experimental protocol and data analysis toolbox for multi-contact 4C (MC-4C), a new proximity ligation method tailored to study the higher-order chromatin contact patterns of selected genomic sites. Conventional chromatin conformation capture (3C) methods fragment proximity ligation products for efficient analysis of pairwise DNA contacts. By contrast, MC-4C is designed to preserve and collect large concatemers of proximity ligated fragments for long-molecule sequencing on an Oxford Nanopore or Pacific Biosciences platform. Each concatemer of proximity ligation products represents a snapshot topology of a different individual allele, revealing its multi-way chromatin interactions. By inverse PCR with primers specific for a fragment of interest (the viewpoint) and DNA size selection, sequencing is selectively targeted to thousands of different complex interactions containing this viewpoint. A tailored statistical analysis toolbox is able to generate background models and three-way interaction profiles from the same dataset. These profiles can be used to distinguish whether contacts between more than two regulatory sequences are mutually exclusive or, conversely, simultaneously occurring at chromatin hubs. The entire procedure can be completed in 2 w, and requires standard molecular biology and data analysis skills and equipment, plus access to a third-generation sequencing platform.

Multinomial, Poisson and Gaussian statistics in count data analysis

It is generally known that counting statistics is not correctly described by a Gaussian approximation. Nevertheless, in neutron scattering, it is common practice to apply this approximation to the counting statistics; also at low counting numbers. We show that the application of this approximation leads to skewed results not only for low-count features, such as background level estimation, but also for its estimation at double-digit count numbers. In effect, this approximation is shown to be imprecise on all levels of count. Instead, a Multinomial approach is introduced as well as a more standard Poisson method, which we compare with the Gaussian case. These two methods originate from a proper analysis of a multi-detector setup and a standard triple axis instrument. We devise a simple mathematical procedure to produce unbiased fits using the Multinomial distribution and demonstrate this method on synthetic and actual inelastic scattering data. We find that the Multinomial method provide almost unbiased results, and in some cases outperforms the Poisson statistics. Although significantly biased, the Gaussian approach is in general more robust in cases where the fitted model is not a true representation of reality. For this reason, a proper data analysis toolbox for low-count neutron scattering should therefore contain more than one model for counting statistics.

SRS-GDA: A spatial random sampling toolbox for grid-based hydro-climatic data analysis in environmental change studies

We present in this paper the development of a new, open-source MATLAB toolbox SRS-GDA that aims to provide random spatial sampling of grid-based hydro-climatic datasets for environmental change studies. This toolbox addresses the needs of quantifying how hydro-climatic responses, which are often driven by grid-based forcing datasets such as climate model projections, vary with location and scale. The toolbox can be used to carry out random spatial sampling of grid-based quantities with various constraints: shape, size, location, dominant orientation and resolution. A case study of a large dataset, the GEAR rainfall dataset is supplied to demonstrate the typical uses case of this toolbox. The provision of the toolbox for downloading together with the sample data is also presented.

Computational methods for ribosome profiling data analysis.

Since the introduction of the ribosome profiling technique in 2009 its popularity has greatly increased. It is widely used for the comprehensive assessment of gene expression and for studying the mechanisms of regulation at the translational level. As the number of ribosome profiling datasets being produced continues to grow, so too does the need for reliable software that can provide answers to the biological questions it can address. This review describes the computational methods and tools that have been developed to analyze ribosome profiling data at the different stages of the process. It starts with initial routine processing of raw data and follows with more specific tasks such as the identification of translated open reading frames, differential gene expression analysis, or evaluation of local or global codon decoding rates. The review pinpoints challenges associated with each step and explains the ways in which they are currently addressed. In addition it provides a comprehensive, albeit incomplete, list of publicly available software applicable to each step, which may be a beneficial starting point to those unexposed to ribosome profiling analysis. The outline of current challenges in ribosome profiling data analysis may inspire computational biologists to search for novel, potentially superior, solutions that will improve and expand the bioinformatician's toolbox for ribosome profiling data analysis. This article is characterized under: Translation > Ribosome Structure/Function RNA Evolution and Genomics > Computational Analyses of RNA Translation > Translation Mechanisms Translation > Translation Regulation.

ScGEAToolbox: a Matlab toolbox for single-cell RNA sequencing data analysis.

Single-cell RNA sequencing (scRNA-seq) technology has revolutionized the way research is done in biomedical sciences. It provides an unprecedented level of resolution across individual cells for studying cell heterogeneity and gene expression variability. Analyzing scRNA-seq data is challenging though, due to the sparsity and high dimensionality of the data. I developed scGEAToolbox-a Matlab toolbox for scRNA-seq data analysis. It contains a comprehensive set of functions for data normalization, feature selection, batch correction, imputation, cell clustering, trajectory/pseudotime analysis, and network construction, which can be combined and integrated to building custom workflow. While most of the functions are implemented in native Matlab, wrapper functions are provided to allow users to call the "third-party" tools developed in Matlab or other languages. Furthermore, scGEAToolbox is equipped with sophisticated graphical user interfaces (GUIs) generated with App Designer, making it an easy-to-use application for quick data processing. https://github.com/jamesjcai/scGEAToolbox. Supplementary data are available at Bioinformatics online.

SPECTRUM \u2013 A MATLAB Toolbox for Proteoform Identification from Top-Down Proteomics Data

Top-Down Proteomics (TDP) is an emerging proteomics protocol that involves identification, characterization, and quantitation of intact proteins using high-resolution mass spectrometry. TDP has an edge over other proteomics protocols in that it allows for: (i) accurate measurement of intact protein mass, (ii) high sequence coverage, and (iii) enhanced identification of post-translational modifications (PTMs). However, the complexity of TDP spectra poses a significant impediment to protein search and PTM characterization. Furthermore, limited software support is currently available in the form of search algorithms and pipelines. To address this need, we propose ‘SPECTRUM’, an open-architecture and open-source toolbox for TDP data analysis. Its salient features include: (i) MS2-based intact protein mass tuning, (ii) de novo peptide sequence tag analysis, (iii) propensity-driven PTM characterization, (iv) blind PTM search, (v) spectral comparison, (vi) identification of truncated proteins, (vii) multifactorial coefficient-weighted scoring, and (viii) intuitive graphical user interfaces to access the aforementioned functionalities and visualization of results. We have validated SPECTRUM using published datasets and benchmarked it against salient TDP tools. SPECTRUM provides significantly enhanced protein identification rates (91% to 177%) over its contemporaries. SPECTRUM has been implemented in MATLAB, and is freely available along with its source code and documentation at https://github.com/BIRL/SPECTRUM/.

ScOrange-a tool for hands-on training of concepts from single-cell data analytics.

MotivationSingle-cell RNA sequencing allows us to simultaneously profile the transcriptomes of thousands of cells and to indulge in exploring cell diversity, development and discovery of new molecular mechanisms. Analysis of scRNA data involves a combination of non-trivial steps from statistics, data visualization, bioinformatics and machine learning. Training molecular biologists in single-cell data analysis and empowering them to review and analyze their data can be challenging, both because of the complexity of the methods and the steep learning curve.ResultsWe propose a workshop-style training in single-cell data analytics that relies on an explorative data analysis toolbox and a hands-on teaching style. The training relies on scOrange, a newly developed extension of a data mining framework that features workflow design through visual programming and interactive visualizations. Workshops with scOrange can proceed much faster than similar training methods that rely on computer programming and analysis through scripting in R or Python, allowing the trainer to cover more ground in the same time-frame. We here review the design principles of the scOrange toolbox that support such workshops and propose a syllabus for the course. We also provide examples of data analysis workflows that instructors can use during the training.Availability and implementationscOrange is an open-source software. The software, documentation and an emerging set of educational videos are available at http://singlecell.biolab.si.

Effect of Flurbiprofen Axetil on Low-frequency Fluctuation Amplitude of Resting-state Functional Magnetic Resonance Imaging in Trigeminal Neuralgia

Objective To observe the changes of brain function in patients with trigeminal neuralgia after administration of flurbiprofen axetil by using the resting-state functional magnetic resonance imaging(fMRI)and based on the amplitude of low-frequency fluctuation(ALFF). Methods Resting fMRI data of 20 patients with trigeminal neuralgia before and after treatment with flurbiprofen axetil were collected by 1.5T magnetic resonance imaging system.The resting fMRI data were pretreated by Statistical Parametric Mapping and DPABI(a toolbox for Data Processing and Analysis for Brain Imaging)software,and the difference of low-frequency oscillation amplitude of brain spontaneous activity before and after treatment with flurbiprofen axetil was analyzed by ALFF. Results The Visual Analogue Scale of pain intensity after flurbiprofen axetil injection was significantly lower than that before administration,and the pain relieved significantly(P=0.000).The ALFF values of right dorsolateral prefrontal lobe,bilateral medial prefrontal lobe,and right middle cingulate gyrus in patients treated with flurbiprofen axetil at rest were significantly lower than those before administration(P=0.000). Conclusions The analgesic effect of flurbiprofen axetil is exerted on the central system.This agent can inhibit the abnormal brain function caused by chronic pain stimulation and thus reduce pain.However,the specific mechanism needs further investigations.

Breathe Easy EDA: A MATLAB toolbox for psychophysiology data management, cleaning, and analysis.

Electrodermal activity (EDA) recordings are widely used in experimental psychology to measure skin conductance responses (SCRs) that reflect sympathetic nervous system arousal. However, irregular respiration patterns and deep breaths can cause EDA fluctuations that are difficult to distinguish from genuine arousal-related SCRs, presenting a methodological challenge that increases the likelihood of false positives in SCR analyses. Thus, it is crucial to identify respiration-related artifacts in EDA data. Here we developed a novel and freely distributed MATLAB toolbox, Breathe Easy EDA (BEEDA). BEEDA is a flexible toolbox that facilitates EDA visual inspection, allowing users to identify and eliminate respiration artifacts. BEEDA further includes functionality for EDA data analyses (measuring tonic and phasic EDA components) and reliability analyses for artifact identification. The toolbox is suitable for any experiment recording both EDA and respiration data, and flexibly adjusts to experiment-specific parameters (e.g., trial structure and analysis parameters).

Static microplate fermentation and automated growth analysis approaches identified a highly-aldehyde resistant Saccharomyces cerevisiae strain

The need to replace fossil fuels has motivated research to develop alternative technologies for renewable energy production. For second generation ethanol, this includes the high-throughput screening and selection of yeast strains resistant to inhibitory byproducts of biomass pretreatment. In this work, we evaluated the use of static growth in 96-well microplates as a fermentative assay for Saccharomyces cerevisiae high-throughput screening, together with robust OD measurement correlations that overcome the low absorbance limit found in conventional plate spectrophotometers. This resulted in a method of yeast cultivation in microplates that requires no agitation and results in accurate kinetic parameters that can be used for the screening of S. cerevisiae. In order to automate calculation of these physiological parameters, we created OCHT®, an open-source high-throughput phenotype profiling software for processing, analyzing and visualizing S. cerevisiae growth data. Together, the cultivation standardization and data analysis toolbox allowed the evaluation and comparison of four S. cerevisiae strains in media containing aldehydes growth inhibitors, typically found in hydrolysates produced from sugarcane biomass. We identified FMY001 (SA1-derived) as a tolerant strain at high concentrations of HMF, furfural and vanillin. The individual analysis of segregants derived from FMY001 demonstrated their potential application in emerging biotechnological processes and gave us preliminary insights about the genetic mechanisms related to its high HMF concentration tolerance.

GHWR, a multi-method global heatwave and warm-spell record and toolbox

Heatwaves are extended periods of unusually high temperatures with significant societal and environmental impacts. Despite their significance, there is not a generalized definition for heatwaves. In this paper, we introduce a multi-method global heatwave and warm-spell data record and analysis toolbox (named GHWR). In addition to a comprehensive long-term global data record of heatwaves, GHWR allows processing and extracting heatwave records for any location efficiently. We use traditional constant temperature threshold methods, as well as spatially and temporally localized threshold approaches to identify heatwaves. GHWR includes binary (0/1) occurrence records of heatwaves/warm-spells, and annual summary files with detailed information on their frequency, duration, magnitude and amplitude. GHWR also introduces the standardized heat index (SHI) as a generalized statistical metric to identify heatwave/warm-spells. SHI has direct association with the probability distribution function of long-term daily temperatures for any given calendar day and spatial grid. Finally, GHWR offers a unique opportunity for users to select the type of heatwave/warm-spell information from a plethora of methods based on their needs and applications.

Integrated analysis of anatomical and electrophysiological human intracranial data.

The exquisite spatiotemporal precision of human intracranial EEG recordings (iEEG) permits characterizing neural processing with a level of detail that is inaccessible to scalp-EEG, MEG, or fMRI. However, the same qualities that make iEEG an exceptionally powerful tool also present unique challenges. Until now, the fusion of anatomical data (MRI and CT images) with the electrophysiological data and its subsequent analysis has relied on technologically and conceptually challenging combinations of software. Here, we describe a comprehensive protocol that addresses the complexities associated with human iEEG, providing complete transparency and flexibility in the evolution of raw data into illustrative representations. The protocol is directly integrated with an open source toolbox for electrophysiological data analysis (FieldTrip). This allows iEEG researchers to build on a continuously growing body of scriptable and reproducible analysis methods that, over the past decade, have been developed and employed by a large research community. We demonstrate the protocol for an example complex iEEG data set to provide an intuitive and rapid approach to dealing with both neuroanatomical information and large electrophysiological data sets. We explain how the protocol can be largely automated, taking under an hour to complete, and readily adjusted to iEEG data sets with other characteristics.

Breathe Easy EDA: A MATLAB toolbox for psychophysiology data management, cleaning, and analysis

Electrodermal activity (EDA) recordings are widely used in experimental psychology to measure skin conductance responses (SCRs) that reflect sympathetic nervous system arousal. However, irregular respiration patterns and deep breaths can cause EDA fluctuations that are difficult to distinguish from genuine arousal-related SCRs, presenting a methodological challenge that increases the likelihood of false positives in SCR analyses. Thus, it is crucial to identify respiration-related artifacts in EDA data. Here we developed a novel and freely distributed MATLAB toolbox, Breathe Easy EDA (BEEDA). BEEDA is a flexible toolbox that facilitates EDA visual inspection, allowing users to identify and eliminate respiration artifacts. BEEDA further includes functionality for EDA data analyses (measuring tonic and phasic EDA components) and reliability analyses for artifact identification. The toolbox is suitable for any experiment recording both EDA and respiration data, and flexibly adjusts to experiment-specific parameters (e.g., trial structure and analysis parameters).

Breathe Easy EDA: A MATLAB toolbox for psychophysiology data management, cleaning, and analysis.

Electrodermal activity (EDA) recordings are widely used in experimental psychology to measure skin conductance responses (SCRs) that reflect sympathetic nervous system arousal. However, irregular respiration patterns and deep breaths can cause EDA fluctuations that are difficult to distinguish from genuine arousal-related SCRs, presenting a methodological challenge that increases the likelihood of false positives in SCR analyses. Thus, it is crucial to identify respiration-related artifacts in EDA data. Here we developed a novel and freely distributed MATLAB toolbox, Breathe Easy EDA (BEEDA). BEEDA is a flexible toolbox that facilitates EDA visual inspection, allowing users to identify and eliminate respiration artifacts. BEEDA further includes functionality for EDA data analyses (measuring tonic and phasic EDA components) and reliability analyses for artifact identification. The toolbox is suitable for any experiment recording both EDA and respiration data, and flexibly adjusts to experiment-specific parameters (e.g., trial structure and analysis parameters).

Detection and Analysis of Small-Amplitude Seasonal Variation in a Short Time Series

We discuss a procedure for seasonality analyses of short time-series data with small amplitude. Such analyses are often performed in medical research. In economics, however, time series are typically long and of appreciable amplitude; therefore, economists are used to analyzing such data. Our procedure provides one more tool for the economists’ data-analysis toolbox. We illustrate the procedure’s application with three examples of real economics data. The examples demonstrate that the procedure can be profitably applied to short economics time series. JEL Classifications: C12, C22, C23, C49, F31

NeoAnalysis: a Python-based toolbox for quick electrophysiological data processing and analysis

BackgroundIn a typical electrophysiological experiment, especially one that includes studying animal behavior, the data collected normally contain spikes, local field potentials, behavioral responses and other associated data. In order to obtain informative results, the data must be analyzed simultaneously with the experimental settings. However, most open-source toolboxes currently available for data analysis were developed to handle only a portion of the data and did not take into account the sorting of experimental conditions. Additionally, these toolboxes require that the input data be in a specific format, which can be inconvenient to users. Therefore, the development of a highly integrated toolbox that can process multiple types of data regardless of input data format and perform basic analysis for general electrophysiological experiments is incredibly useful.ResultsHere, we report the development of a Python based open-source toolbox, referred to as NeoAnalysis, to be used for quick electrophysiological data processing and analysis. The toolbox can import data from different data acquisition systems regardless of their formats and automatically combine different types of data into a single file with a standardized format. In cases where additional spike sorting is needed, NeoAnalysis provides a module to perform efficient offline sorting with a user-friendly interface. Then, NeoAnalysis can perform regular analog signal processing, spike train, and local field potentials analysis, behavioral response (e.g. saccade) detection and extraction, with several options available for data plotting and statistics. Particularly, it can automatically generate sorted results without requiring users to manually sort data beforehand. In addition, NeoAnalysis can organize all of the relevant data into an informative table on a trial-by-trial basis for data visualization. Finally, NeoAnalysis supports analysis at the population level.ConclusionsWith the multitude of general-purpose functions provided by NeoAnalysis, users can easily obtain publication-quality figures without writing complex codes. NeoAnalysis is a powerful and valuable toolbox for users doing electrophysiological experiments.