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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Statistical Analysis of Recent Go Around Flight Data to Study and Evaluate Skilled Pilot Monitoring

A statistical analysis was performed on a batch of recent go-around flight data of a few major US airlines spanning a one year period. The statistical analysis investigates a few select relevant aspects of these go arounds, such as initiation altitude and altitude change, aircraft configuration, deviation from the flightpath, and energy state. These data show that only one third of the landings are aborted below 400ft, but the majority of the go arounds occur at significantly higher altitudes. Current go-around training is specifically focused on the final approach and landing phase at low altitudes and low speeds. The results of this study will support the development of a broader set of relevant scenarios for an FAA simulator experiment to study and evaluate the skills of the pilots who monitor this wide spectrum of diverse go-around conditions, with the ultimate goal to help improve pilot go-around training and technologies.

go around↗
NASA NTRS2024

SUPIN: A Tool for the Aerodynamic Design and Analysis of Supersonic Inlets

The Supersonic Inlet Design and Analysis Tool (SUPIN) is a computational program that performs geometric modeling and aerodynamic design and analysis of a set of supersonic inlets whose types include the axisymmetric pitot, three-dimensional pitot, axisymmetric, two-dimensional, two-dimensional bifurcated-duct, and streamline-traced inlets. The inlets are modeled by dividing the inlet flowpath into components that start at the freestream and ends at the engine face. The primary components of the inlet include the external supersonic diffuser, throat section, and subsonic diffuser. Each component is characterized by a set of inputs that include geometric and aerodynamic factors. Examples of the geometric factors are angles, lengths, area ratios, and coordinates that describe the geometry of the components. Examples of the aerodynamic factors are Mach numbers, flow ratios, and pressure ratios that set constraints or desired aerodynamic properties of the component. These input factors are specified within a text-based input data file that is read into SUPIN. SUPIN is a Fortran 95 program that performs the inlet design and analysis as a batch or non-interactive process. SUPIN writes output data files describing the inlet geometry and aerodynamic performance. SUPIN uses analytic, empirical, and numerical methods to perform the inlet design and analysis. The inlet geometry is constructed using planar and three-dimensional geometry constructs and is represented by coordinates, angles, areas, profiles, and surfaces. Surfaces are output in the form of a Plot3D surface grid file. SUPIN does have some capability to automatically generate planar and three-dimensional, multi-block, structured grids for computational fluid dynamics (CFD) analysis. The aerodynamic analysis performed by SUPIN computes quasi-one-dimensional properties, such as Mach numbers, pressures, and temperatures, through the inlet flowpath. Planar aerodynamic properties, such as those for shock and Mach waves, are computed for the supersonic compression of the external supersonic diffuser and throat section. The inlet performance is characterized by the inlet flow rates, total pressure recovery, and inlet drag. The primary intent of this document is to describe the usage of SUPIN. The methods used within SUPIN are documented and may serve as a useful reference for understanding the aerodynamic design and analysis of supersonic inlets.

supersonic inlets↗
NASA NTRSDate not supplied

The BioSensor Instrument Beyond LEIA: a Versatile Platform for Lunar Biology

Introduction: The BioSensor is a deep-space-compatible automated microfluidic culturing instrument. While originally designed to measure the effects of deep space radiation on yeast growth for the BioSentinel mission, it has the potential to host a diverse range of life science experiments with single- and/or multi-celled organisms and can be adapted to interface with a diverse range of platforms in both crewed and uncrewed settings. It is therefore a leading candidate for hosting life sciences experimentation associated with a lunar surface habitat. BioSensor design: The function of the BioSensor is to monitor the growth and metabolic activity of samples in batch fluidic culture, without the need for crew involvement. The current configuration houses organisms in 16 wells within 16 microfluidic cards, accommodating a total of 256 samples, replicates, and controls. Each well has an optical system including three LEDs and a photodiode detector to measure absorbance at three wavelengths, enabling measurement of optical density, color change in dyes such as alamarBlue, and bioproduction of pigmented compounds. Organisms are loaded into fluidic wells and air-dried for storage during integration, launch, and transit, then activated by the introduction of culture medium from storage bags via manifolds that fill one card at a time. Temperature is controlled by individual card heaters, and timing of all activities (fluidics fills, optical measurements, temperatures) is directed by an experiment script. The self-contained BioSensor payload is roughly 4U in volume; with electrical/mechanical/thermal interface, e.g. for operation on ISS or a lunar lander, as well as a linear energy-transfer (LET) charged-particle radiation spectrometer, the entire system is closer to 6U. BioSentinel and LEIA: Flown on the ISS and in a deep-space free flyer for BioSentinel, the BioSensor has been modified for use in the LEIA mission, including improvements to reduce the sensitivity to lengthy launch delays. LEIA will monitor yeast growth in the radiation and reduced-gravity environment of the lunar surface no earlier than 2026, on a CLPS lander [4]. Changes include accommodating additional culture media and an additional LED color for a new biological assay (bioproduction of carotenoids-- dietary antioxidants), as well as modifications to the housing to allow late-load biology changeout and improved isolation between fluidics and electronics. Future prospects: Future work with the BioSensor, beyond LEIA, will include expanding the range and diversity of organisms and assays accommodated. Preliminary work has demonstrated the growth of Arabidopsis seedlings in BioSensor fluidic cards, including optical measurements of growth rate over time. Minor modifications could allow measurement of phenotypes related to photosynthetic capacity in both plants and cyanobacteria. The experimental capabilities of the BioSensor could be dramatically increased by introducing the capability for fluorescence measurements, and/or the design of novel biological assays using luminescence. The BioSensor can also be adapted for new platforms and experiment settings; in addition to free-flyer, ISS, and CLPS lander, a preliminary design concept has been developed for crewed deployment directly to the lunar surface. The instrument could be accommodated inside a lunar habitat, where its automated operation would make it an excellent candidate for experiments from fundamental investigations into the response of organisms to lunar surface conditions to applied-science purposes such as screening engineered strains of various organisms for bioproduction capability.

J A Lee↗
NASA NTRSDate not supplied

Volumetric Assessment of UPRITE Exercises From Marker-Based Motion Capture

BACKGROUND Lack of volumetric data on full-body movement of exercises presents a challenge to ensuring the fit of crew member’s full range of motion on the International Space Station (ISS). The Upright Proprioception Retention via In-flight Training and Evaluation (UPRITE) is a sensorimotor countermeasure device designed for maintaining crew members’ proprioception in a microgravity environment. A footplate—attached to a static base—rotates in two degrees of freedom (pitch and roll) up to a 20 deg angle. An initial volumetric assessment assuming an upright standing posture produced a cone-like shape with a narrow bottom and wide top. Such general volumetric assessments risk creating an overly conservative volume estimate, taking up more space than is necessary on the already limited interior space of the ISS, and neglecting necessary volume due to oversimplifying assumptions. Rather, higher-fidelity volumetric assessments offer more comprehensive insights in an environment where every area counts. The main objective of this work is to provide the spatial parameters of exercises on the UPRITE such that it is placed on the ISS according to its volumetric demands or that usage is adjusted to fit the available space. METHODS In 2023, a data collection was performed originally to inform loads and dynamics of system use and was recently leveraged for volumetric assessment. Three human subjects representing different body types (~63-76 inches in stature) performed a variety of board manipulations using UPRITE with body weight offload. The test collected the 3D positional data of a modified full-body Plug-in Gait marker set [1] via a 16-camera OptiTrack MoCap system. After processing – filling marker gaps and trimming data – in OptiTrack Motive, the recorded marker location data, which included device markers, was exported to a readable trajectory file. To accurately represent the full volume defining landmarks, additional markers were digitally added to an unscaled Modified Full Body Model [2]. The model was then scaled according to its subject parameters upon which an inverse kinematics analysis was performed. A custom plugin yielded model marker location data files. Volumetric analyses were performed on the recorded trajectory and model trajectory files using a custom Python-built tool that extracted the marker location data and plotted it in a 3D space. Concerned with only the maximum volume of the motion, a 3D convex hull analysis was applied to the plot, extracting the vertices or external points of the eventual 3D CAD output, dubbed aptly as a “volume shell”. This overall approach was based on guidance in a NASA-STD-3001 Technical Brief [3]. RESULTS AND DISCUSSION Batch volumetric assessment on the exercises for each subject was performed, producing high-fidelity volume shells in minimal time. Preliminary results highlighted the value in higher-fidelity volumes based on collected data when possible. For example, revolving a single posture in the cone assessment would not have sufficiently captured a single leg stance; rather, it would need to involve swinging the leg both forward and back. Additional observations and the maximal dimensions of the volumes, including those based on scaled data for ISS anthropometric requirements, will be presented at the Human Research Program Investigator’s Workshop. CONCLUSIONS While this work’s primary objective was for the UPRITE-to-ISS integration, the tool built to conduct this analysis has wide applications for future exercise systems as an informational tool for optimal device placement. The tool and its findings also have implications for exercise device design and spacecraft interior considerations on Gateway, the Lunar Pressurized Rover, and beyond. REFERENCES [1] Bell, C. A., et al. (2023) Recent Improvements and Verification of a Full Body Model in OpenSim. NASA Human Research Program Investigator’s Workshop. https://ntrs.nasa.gov/citations/20230001080 [2] Lostroscio, K., et al (2023) The Digital Astronaut Simulation. AHFE International Conference on Human Factors in Design, Engineering, and Computing for All. [3] Exercise Overview. (2023) NASA-STD-3001 Technical Brief. https://www.nasa.gov/wp-content/uploads/2023/12/ochmo-tb-031-exercise-overview.pdf?emrc=9d454c?emrc=9d454c

L D Quinto↗
NASA NTRSDate not supplied

Monocular Horizon Navigation

The Artemis program advances towards operations on the lunar surface, where precise surface localization is a driving need for safety and science. Using the observable horizon as an image landmark allows for estimating the photographer’s position. This work analyzes the application of Perspective-n-Point (PnP) algorithms to this lunar localization problem. Batch simulations using lunar topography display the accuracy and drawbacks of these methods. Monte Carlo techniques show the pipeline’s solutions as measurements provided to a navigation filter. When filtered, these solutions have position errors of 50 meters or better, which is similar to or better than the performance of other methods of surface localization.

optical navigation↗
NASA NTRSDate not supplied

A Heritage BioSensor for Lunar Biology Experiments

Introduction: Automated biological experiments on small spacecraft missions have gained prominence over the past decade due to their simplicity, accessibility, and small mass, volume, and power needs. Most recently, the BioSensor microfluidic CubeSat payload aboard BioSentinel used an automated microfluidic cell culture system to study the effects of environmental stressors like deep space radiation and microgravity on yeast growth and metabolism. BioSentinel’s successor, the Lunar Explorer Instrument for space biology Applications (LEIA), will study the effects of lunar gravity and radiation using an improved version of the BioSensor microfluidic platform. The BioSensor payload has great adaptability to host a diverse range of biological experiments with single- and multi-celled organisms in both crewed and uncrewed missions, making it a compelling candidate for future space biology studies in a lunar surface environment. BioSensor Instrumentation on BioSentinel: The first spaceflight mission with the BioSensor, BioSentinel’s biology experiments occurred at three locations -- deep space, ISS and ground. The payload contained 18 microfluidic cards, each featuring 16 growth wells (a total of 288 growth wells). Each well was loaded before launch with desiccated yeast. In space, liquid culture medium (nutrients) was automatically introduced to batches of wells at a time to initiate a series of biology experiments. Temperature was maintained by thin film heaters on both sides of each card. Each well was equipped with three LEDs emitting at 570 nm, 630 nm, and 850 nm, paired with photodetectors to measure cell concentration and the alamarBlue (metabolic indicator dye) color transition from blue to pink. Phenotypic parameters like cell viability, metabolic rate, and generation time can be derived from these measurements. The sequence and timing of fluid fills, optical measurements, and thermal control were stored onboard, but could be updated asynchronously via ground communication. LEIA: LEIA is slated for launch no earlier than 2026 on a CLPS lander. BioSentinel’s BioSensor has been modified for use in LEIA. These improvements include: (a) storage for multiple culture medium types, (b) additional LED color (465 nm) for a new biological assay for antioxidant (carotenoid) production, (c) housing modifications for later biology load before launch, (d) improved isolation between electronic and fluidic components, and (e) improved humidity control for prolonged organism viability in case of post-load launch delay. Future Prospects: The consistent and successful demonstration of complex fluidics platforms alongside reliable instrument operations in a space environment is poised to create strong momentum for BioSensor-based biological experiment payloads. Planned future developments with the BioSensor include extending compatibility to a broader range of organisms and assays. Preliminary work has already demonstrated successful growth of Arabidopsis seedlings in fluidic cards. With a few modifications to the optical assembly, the setup could easily measure photosynthetic traits in plants and cyanobacteria. The addition of fluorescence measurements and generation of novel luminescent assays will elevate BioSensor’s functionality further. Beyond the BioSensor’s potential uses on free-flyer missions, ISS and Gateway, and CLPS landers, deploying the BioSensor to the lunar surface or in an artificial habitat on crewed missions could enable pioneering research on both how life responds to lunar conditions and future bioproduction capabilities making the BioSensor an indispensable tool for future space biology research.

Chinmayee Govinda Raj↗
NASA NTRSDate not supplied

Lunar Explorer Instrument for space biology Applications (LEIA): An overview of planned science concept of operations

Radiation and reduced gravity pose biological risks to crewed deep space exploration. To better understand deep space radiation biology, the LEIA mission will measure charged particles and fast neutrons as well as yeast growth, metabolic rate, and bioengineered production of carotenoids at the lunar surface. LEIA will be delivered to the south polar region of the Moon by the Commercial Lunar Payload Services (CLPS) program. The LEIA science concept of operations was developed to isolate radiation and partial gravity from other environmental conditions experienced by the payload. Due to CLPS integration requirements, there will be at least eight months from loading yeast into the payload until activation on the lunar surface. Replicate yeast strains will be loaded in a randomized complete block design to minimize batch differences in desiccation tolerance and positional effects in the microfluidics culture system. Temperature and relative humidity will be controlled throughout integration and flight to maintain yeast viability and to enable measurement of yeast growth parameters within the lunar surface operations timeframe, with the ground control matching environmental conditions where possible. LEIA is co-manifested with the European Space Agency’s PROSPECT mission, which will be operating a drill during yeast growth. Vibration translated through the lunar lander will be mitigated and quantified to account for potential impacts on LEIA optical measurements and yeast growth rate. Total space radiation dose, including the transit exposure from Earth to the Moon, will be measured to obtain more accurate charged particle and fast neutron dose rates on the lunar surface. These radiation data will also yield ground truth radiation dose experienced by the yeast during the mission. Quantifying these environmental factors impacting the LEIA payload will allow more accurate ground control experiments to better isolate the biological responses to radiation and reduced gravity at the lunar surface.

Lunar Surface Mission↗
NASA NTRSDate not supplied

Remote Guidance of a Mothership-Daughtership Formation for Asteroid Exploration

This work presents an application of Model Predictive Control to maintain long-term stability for a three-satellite formation around the asteroid Apophis via remote guidance. The mothership and two daughterships perform tomographic characterization with a bistatic radar. The mothership estimates the states of all three spacecraft and determines the necessary control to maintain the desired antipodal daughtership states. An Extended Kalman Filter estimates the mothership states. A Batch Least Squares algorithm estimates the daughtership states and performs maneuver reconstruction for the mothership-administered thrust commands under thruster and gravitational uncertainty. The formulation maintains the antipodal angle with growing instability amidst these uncertainties.

Guidance↗
NASA NTRS1992

SNS programming environment user's guide

This document briefly describes the computing environment for the Supercomputing Network Subsystem (SNS) of the Central Scientific Computing Complex of the Langley Research Center. The major SNS computers are a CRAY-2, a CRAY Y-MP, a CONVEX C-210, and a CONVEX C-220. It describes the software that is common to all of these computers, including; the UNIX operating system, graphics, networking utilities, mass storage, and mathematical libraries. It also describes file management, validation, SNS configuration, documentation and customer services. The document is intended for all SNS users as a ready reference to frequently asked questions and to more detailed information contained within the vendor manuals. It is appropriate for both the novice and the experienced user.

Supercomputing↗
NASA NTRS2013

Einstein@Home All-Sky Search for Periodic Gravitational Waves in LIGO S5 Data

This paper presents results of an all-sky search for periodic gravitational waves in the frequency range [50, 1 190] Hzand and with frequency derivative range of approx. [-20,1.1] x 10(exp-10) H(sub 0)z s(exp-1) for the fifth LIGO science run (S5). The search uses a noncoherent Hough-transform method to combine the information from coherent searches on time scales of about one day. Because these searches are very computationally intensive, they have been carried out with the Einstein@Home volunteer distributed computing project. Postprocessing identifies eight candidate signals; deeper follow-up studies rule them out. Hence, since no gravitational wave signals have been found, we report upper limits on the intrinsic gravitational wave strain amplitude ho. For example, in the 0.5 Hz-wide band at 152.5 Hz, we can exclude the presence of signals with h(exp 0) greater than 7.6 x 10(exp-25) at a 90% confidence level. This search is about a factor 3 more sensitive than the previous Einstein@Home search of early S5 LIGO data.

J Aasi↗
NASA NTRS2016

Supplement: The Rate of Binary Black Hole Mergers Inferred from Advanced LIGO Observations Surrounding GW150914

This article provides supplemental information for a Letter reporting the rate of (BBH) coalescences inferred from 16 days of coincident Advanced LIGO observations surrounding the transient (GW) signal GW150914. In that work wereported various rate estimates whose 90% confidence intervals fell in the range 2600 Gpc(exp -3) yr(exp -1). Here we givedetails on our method and computations, including information about our search pipelines, a derivation of ourlikelihood function for the analysis, a description of the astrophysical search trigger distribution expected frommerging BBHs, details on our computational methods, a description of the effects and our model for calibrationuncertainty, and an analytic method for estimating our detector sensitivity, which is calibrated to our measurements.

gravitational waves – stars: black holes↗
NASA NTRS2017

Gravitational Waves and Gamma-Rays from a Binary Neutron Star Merger: GW170817 and GRB 170817A

On 2017 August 17, the gravitational-wave event GW170817 was observed by the Advanced LIGO and Virgo detectors, and the gamma-ray burst (GRB) GRB170817A was observed independently by the Fermi Gamma-ray Burst Monitor, and the Anti-Coincidence Shield for the Spectrometer for the International Gamma-Ray Astrophysics Laboratory. The probability of the near-simultaneous temporal and spatial observation of GRB 170817A and GW170817 occurring by chance is 5.0 x 10(exp -8). We therefore confirm binary neutron star mergers as a progenitor of short GRBs. The association of GW170817 and GRB 170817A provides new insight into fundamental physics and the origin of short GRBs. We use the observed time delay of (+1.74 +/- 0.05) s between GRB170817A and GW170817 to: (i) constrain the difference between the speed of gravity and the speed of light to be between -3 x 10(exp-16) times the speed of light, (ii) place new bounds on the violation of Lorentz invariance, (iii) present a new test of the equivalence principle by constraining the Shapiro delay between gravitational and electromagnetic radiation. We also use the time delay to constrain the size and bulk Lorentz factor of the region emitting the gamma-rays. GRB170817A is the closest short GRB with a known distance, but is between 2 and 6 orders of magnitude less energetic than other bursts with measured redshift. A new generation of gamma-ray detectors, and subthreshold searches in existing detectors, will be essential to detect similar short bursts at greater distances. Finally, we predict a joint detection rate for the Fermi Gamma-ray Burst Monitor and the Advanced LIGO and Virgo detectors of 0.1 - 1.4 per year during the 2018--2019 observing run and 0.3 - 1.7 per year at design sensitivity.

Gravitational wave↗
NASA NTRS2014

First All-Sky Search for Continuous Gravitational Waves from Unknown Sources in Binary Systems

We present the first results of an all-sky search for continuous gravitational waves from unknown spinning neutron stars in binary systems using LIGO and Virgo data. Using a specially developed analysis program, the TwoSpect algorithm, the search was carried out on data from the sixth LIGO science run and the second and third Virgo science runs. The search covers a range of frequencies from 20 Hz to 520 Hz, a range of orbital periods from 2 to ∼2,254 h and a frequency- and period-dependent range of frequency modulation depths from 0.277 to 100 mHz. This corresponds to a range of projected semimajor axes of the orbit from ∼0.6 × 10(exp −3) ls to ∼6,500 ls assuming the orbit of the binary is circular. While no plausible candidate gravitational wave events survive the pipeline, upper limits are set on the analyzed data. The most sensitive 95% confidence upper limit obtained on gravitational wave strain is 2.3 × 10(exp −24) at 217 Hz, assuming the source waves are circularly polarized. Although this search has been optimized for circular binary orbits, the upper limits obtained remain valid for orbital eccentricities as large as 0.9. In addition, upper limits are placed on continuous gravitational wave emission from the low-mass x-ray binary Scorpius X-1 between 20 Hz and 57.25 Hz.

systems↗
NASA NTRS2013

Parameter Estimation for Compact Binary Coalescence Signals with the First Generation Gravitational-Wave Detector Network

Compact binary systems with neutron stars or black holes are one of the most promising sources for ground-based gravitational-wave detectors. Gravitational radiation encodes rich information about source physics; thus parameter estimation and model selection are crucial analysis steps for any detection candidate events. Detailed models of the anticipated waveforms enable inference on several parameters, such as component masses, spins, sky location and distance, that are essential for new astrophysical studies of these sources. However, accurate measurements of these parameters and discrimination of models describing the underlying physics are complicated by artifacts in the data, uncertainties in the waveform models and in the calibration of the detectors. Here we report such measurements on a selection of simulated signals added either in hardware or software to the data collected by the two LIGO instruments and the Virgo detector during their most recent joint science run, including a blind injection where the signal was not initially revealed to the collaboration. We exemplify the ability to extract information about the source physics on signals that cover the neutron-star and black-hole binary parameter space over the component mass range 1M25M and the full range of spin parameters. The cases reported in this study provide a snapshot of the status of parameter estimation in preparation for the operation of advanced detectors.

Aasi, J.↗
NASA NTRSDate not supplied

Evaluation of Correction Methods for NASA GeneLab Transcriptomic Datasets

Conducting space biology experiments aboard the International Space Station, particularly those utilizing complex model organisms like mice, is expensive and difficult due to limited crew availability, hardware, and space. As a result, sample numbers from these studies are low, reducing the statistical power of any one experiment. Aggregating spaceflight datasets serves as a method to increase sample numbers, allowing for novel insights through bioinformatic analysis of ‘omics data from merged datasets. However, aggregating datasets can introduce unwanted variation including 1) differences in sample handling, processing, and sequencing platforms between datasets (technical variation) as well as 2) differences in experimental design between datasets such as sex or age of the model organism used. In the present study, NASA GeneLab-hosted RNAseq datasets from rodent liver tissues were used to evaluate several statistical methods to correct for this unwanted variation through two approaches, reference-based and standard. The following correction algorithms were applied with (reference-based) and/or without (standard) considering Universal Mouse RNA Reference samples: ComBat and ComBat_seq from the SVA package, median polish, empirical Bayes, and ANOVA-based algorithms from the MBatch package, and negative binomial regression normalization in the DESeq2 package. For each approach, after the correction algorithm was applied, differential gene expression (DGE) analysis of flight and ground control samples was performed with the combined data. The robustness of each tool was evaluated using BatchQC, to determine statistical differences between datasets before and after correction, Principal Component Analysis, to evaluate global gene expression in samples before and after correction, and by comparing DGE analysis of individual datasets and combined datasets before and after correction. The results showed that the reference-based approach introduced several additional (and likely artificial) DEGs when compared with the standard approach. Thus, the most robust standard correction will be implemented in the GeneLab Visualization 2.0 platform when datasets are combined.

GeneLab, RNA-seq, Batch Correction↗
NASA NTRS2020

Design and Implementation of a Scenario Development Process for a 2040 Trajectory-Based Operations Simulation

The National Aeronautics and Space Administration (NASA) is supporting research to transition from a legacy, air traffic management (ATM) system to Trajectory-Based Operations (TBO) environment targeting the 2035-2045 timeframe by creating simulation scenarios for current and future studies. TBO in the National Airspace System (NAS) focuses on modernizing the current operating paradigm to increase efficiency, predictability, resilience, and flexibility while migrating toward greater operational autonomy across the airspace. These simulation scenarios offer a wide dissemination and use of system-level constraints and aircraft state/intent data, routine use of data communications to request complex trajectory modifications and to receive clearances, and aircraft that are flying on 4-D trajectories with flexibility when desired, but structure where required. This report describes the end-to-end development of scenarios for the NASA Langley Research Center simulation environment to represent a 2040 Trajectory-Based Operations airspace for air transport operations research.

Trajectory-Based Operations↗
NASA NTRSDate not supplied

Evaluation of Correction Methods for NASA GeneLab Transcriptomic Datasets

Conducting space biology experiments aboard the International Space Station, particularly those utilizing complex model organisms like mice, is expensive and difficult due to limited crew availability, hardware, and space. As a result, sample numbers from these studies are low, reducing the statistical power of any one experiment. Aggregating spaceflight datasets serves as a method to increase sample numbers, allowing for novel insights through bioinformatic analysis of ‘omics data from merged datasets. However, aggregating datasets can introduce unwanted variation including 1) differences in sample handling, processing, and sequencing platforms between datasets (technical variation) as well as 2) differences in experimental design between datasets. In the present study, NASA GeneLab-hosted RNAseq datasets from mouse liver tissues were used to evaluate several statistical methods to correct for this unwanted variation through two approaches, reference-based and standard. The following correction algorithms were applied with (reference-based) and/or without (standard) considering Universal Mouse RNA Reference samples: ComBat and ComBat_seq from the SVA package, median polish, empirical Bayes, and ANOVA-based algorithms from the MBatch package, and negative binomial regression normalization in the DESeq2 package. For each approach, after the correction algorithm was applied, differential gene expression (DGE) analysis of flight and ground control samples was performed with the combined data. The robustness of each tool was evaluated using BatchQC to determine statistical differences between datasets before and after correction, Principal Component Analysis to evaluate global gene expression in samples before and after correction, and by comparing DGE analysis of individual datasets and combined datasets before and after correction. The results showed that the reference-based approach introduced several additional (and likely artificial) DEGs when compared with the respective standard approach. Of the methods tested, standard ComBat and DESeq2 were identified as the most robust correction methods for combining spaceflight mouse liver RNAseq datasets hosted on GeneLab.

GeneLab↗