Real Human Datasets Research Articles

MarsGT: Multi-omics analysis for rare population inference using single-cell graph transformer

Rare cell populations are key in neoplastic progression and therapeutic response, offering potential intervention targets. However, their computational identification and analysis often lag behind major cell types. To fill this gap, we introduce MarsGT: Multi-omics Analysis for Rare population inference using a Single-cell Graph Transformer. It identifies rare cell populations using a probability-based heterogeneous graph transformer on single-cell multi-omics data. MarsGT outperforms existing tools in identifying rare cells across 550 simulated and four real human datasets. In mouse retina data, it reveals unique subpopulations of rare bipolar cells and a Müller glia cell subpopulation. In human lymph node data, MarsGT detects an intermediate B cell population potentially acting as lymphoma precursors. In human melanoma data, it identifies a rare MAIT-like population impacted by a high IFN-I response and reveals the mechanism of immunotherapy. Hence, MarsGT offers biological insights and suggests potential strategies for early detection and therapeutic intervention of disease.

Limitations of principal components in quantitative genetic association models for human studies.

Principal Component Analysis (PCA) and the Linear Mixed-effects Model (LMM), sometimes in combination, are the most common genetic association models. Previous PCA-LMM comparisons give mixed results, unclear guidance, and have several limitations, including not varying the number of principal components (PCs), simulating simple population structures, and inconsistent use of real data and power evaluations. We evaluate PCA and LMM both varying number of PCs in realistic genotype and complex trait simulations including admixed families, subpopulation trees, and real multiethnic human datasets with simulated traits. We find that LMM without PCs usually performs best, with the largest effects in family simulations and real human datasets and traits without environment effects. Poor PCA performance on human datasets is driven by large numbers of distant relatives more than the smaller number of closer relatives. While PCA was known to fail on family data, we report strong effects of family relatedness in genetically diverse human datasets, not avoided by pruning close relatives. Environment effects driven by geography and ethnicity are better modeled with LMM including those labels instead of PCs. This work better characterizes the severe limitations of PCA compared to LMM in modeling the complex relatedness structures of multiethnic human data for association studies.

Adaptive data-driven motion detection and optimized correction for brain PET

Head motion during PET scans causes image quality degradation, decreased concentration in regions with high uptake and incorrect outcome measures from kinetic analysis of dynamic datasets. Previously, we proposed a data-driven method, center of tracer distribution (COD), to detect head motion without an external motion tracking device. There, motion was detected using one dimension of the COD trace with a semiautomatic detection algorithm, requiring multiple user defined parameters and manual intervention. In this study, we developed a new data-driven motion detection algorithm, which is automatic, self-adaptive to noise level, does not require user-defined parameters and uses all three dimensions of the COD trace (3DCOD). 3DCOD was first validated and tested using 30 simulation studies (18F-FDG, N = 15; 11C-raclopride (RAC), N = 15) with large motion. The proposed motion correction method was tested on 22 real human datasets, with 20 acquired from a high resolution research tomograph (HRRT) scanner (18F-FDG, N = 10; 11C-RAC, N = 10) and 2 acquired from the Siemens Biograph mCT scanner. Real-time hardware-based motion tracking information (Vicra) was available for all real studies and was used as the gold standard. 3DCOD was compared to Vicra, no motion correction (NMC), one-direction COD (our previous method called 1DCOD) and two conventional frame-based image registration (FIR) algorithms, i.e., FIR1 (based on predefined frames reconstructed with attenuation correction) and FIR2 (without attenuation correction) for both simulation and real studies. For the simulation studies, 3DCOD yielded -2.3 ± 1.4% (mean ± standard deviation across all subjects and 11 brain regions) error in region of interest (ROI) uptake for 18F-FDG (-3.4 ± 1.7% for 11C-RAC across all subjects and 2 regions) as compared to Vicra (perfect correction) while NMC, FIR1, FIR2 and 1DCOD yielded -25.4 ± 11.1% (-34.5 ± 16.1% for 11C- RAC), -13.4 ± 3.5% (-16.1 ± 4.6%), -5.7 ± 3.6% (-8.0 ± 4.5%) and -2.6 ± 1.5% (-5.1 ± 2.7%), respectively. For real HRRT studies, 3DCOD yielded -0.3 ± 2.8% difference for 18F-FDG (-0.4 ± 3.2% for 11C-RAC) as compared to Vicra while NMC, FIR1, FIR2 and 1DCOD yielded -14.9 ± 9.0% (-24.5 ± 14.6%), -3.6 ± 4.9% (-13.4 ± 14.3%), -0.6 ± 3.4% (-6.7 ± 5.3%) and -1.5 ± 4.2% (-2.2 ± 4.1%), respectively. In summary, the proposed motion correction method yielded comparable performance to the hardware-based motion tracking method for multiple tracers, including very challenging cases with large frequent head motion, in studies performed on a non-TOF scanner.

U-Net-based approach for automatic lung segmentation in electrical impedance tomography

Objective. Electrical impedance tomography (EIT) is a non-invasive technique that constitutes a promising tool for real-time imaging and long-term monitoring of the ventilation distribution at bedside. However, clinical monitoring and diagnostic evaluations depend on various methods to assess ventilation-dependent parameters useful for ventilation therapy. This study develops an automatic, robust, and rapidly accessible method for lung segmentation that can be used to define appropriate regions-of-interest (ROIs) within EIT images. Approach. To date, available methods for patients with defected lungs have the disadvantage of not being able to identify lung regions because of their poor ventilation responses. Furthermore, the challenges related to the identification of lung areas in EIT images are attributed to the low spatial resolution of EIT. In this study, a U-Net-based automatic lung segmentation model is used as a postprocessor to transform the original EIT image to a lung ROI image and refine the inherent conductivity distribution of the original EIT image. The trained U-Net network is capable of performing an automatic segmentation of conductivity changes in EIT images without requiring prior information. Main results. The experimental design of this study was based on a finite element method (FEM) phantom used to assess the feasibility and effectiveness of the proposed method, and evaluation of the trained models on the test dataset was performed using the Dice similarity coefficient (DSC) and the mean absolute error (MAE). The FEM experimental results yielded values of 0.0065 for MAE, and values >0.99 for DSC in simulations. Significance. The use of a deep-learning-based approach attained automatic and convenient segmentation of lung ROIs into distinguishable images, which represents a direct benefit for regional lung ventilation-dependent parameter extraction and analysis. However, further investigations and validation are warranted in real human datasets with different physiology conditions with CT cross-section dataset to refine the suggested model.

SVJedi: genotyping structural variations with long reads.

Studies on structural variants (SVs) are expanding rapidly. As a result, and thanks to third generation sequencing technologies, the number of discovered SVs is increasing, especially in the human genome. At the same time, for several applications such as clinical diagnoses, it is important to genotype newly sequenced individuals on well-defined and characterized SVs. Whereas several SV genotypers have been developed for short read data, there is a lack of such dedicated tool to assess whether known SVs are present or not in a new long read sequenced sample, such as the one produced by Pacific Biosciences or Oxford Nanopore Technologies. We present a novel method to genotype known SVs from long read sequencing data. The method is based on the generation of a set of representative allele sequences that represent the two alleles of each structural variant. Long reads are aligned to these allele sequences. Alignments are then analyzed and filtered out to keep only informative ones, to quantify and estimate the presence of each SV allele and the allele frequencies. We provide an implementation of the method, SVJedi, to genotype SVs with long reads. The tool has been applied to both simulated and real human datasets and achieves high genotyping accuracy. We show that SVJedi obtains better performances than other existing long read genotyping tools and we also demonstrate that SV genotyping is considerably improved with SVJedi compared to other approaches, namely SV discovery and short read SV genotyping approaches. https://github.com/llecompte/SVJedi.git. Supplementary data are available at Bioinformatics online.

MUM&Co: accurate detection of all SV types through whole-genome alignment.

MUM&Co is a single bash script to detect structural variations (SVs) utilizing whole-genome alignment (WGA). Using MUMmer's nucmer alignment, MUM&Co can detect insertions, deletions, tandem duplications, inversions and translocations greater than 50 bp. Its versatility depends upon the WGA and therefore benefits from contiguous de-novo assemblies generated by third generation sequencing technologies. Benchmarked against five WGA SV-calling tools, MUM&Co outperforms all tools on simulated SVs in yeast, plant and human genomes and performs similarly in two real human datasets. Additionally, MUM&Co is particularly unique in its ability to find inversions in both simulated and real datasets. Lastly, MUM&Co's primary output is an intuitive tabulated file containing a list of SVs with only necessary genomic details. https://github.com/SAMtoBAM/MUMandCo. Supplementary data are available at Bioinformatics online.

MUSE: A MULTI-LOCUS SAMPLING-BASED EPISTASIS ALGORITHM FOR QUANTITATIVE GENETIC TRAIT PREDICTION.

Quantitative genetic trait prediction based on high-density genotyping arrays plays an important role for plant and animal breeding, as well as genetic epidemiology such as complex diseases. The prediction can be very helpful to develop breeding strategies and is crucial to translate the findings in genetics to precision medicine. Epistasis, the phenomena where the SNPs interact with each other, has been studied extensively in Genome Wide Association Studies (GWAS) but received relatively less attention for quantitative genetic trait prediction. As the number of possible interactions is generally extremely large, even pairwise interactions is very challenging. To our knowledge, there is no solid solution yet to utilize epistasis to improve genetic trait prediction. In this work, we studied the multi-locus epistasis problem where the interactions with more than two SNPs are considered. We developed an effcient algorithm MUSE to improve the genetic trait prediction with the help of multi-locus epistasis. MUSE is sampling-based and we proposed a few different sampling strategies. Our experiments on real data showed that MUSE is not only effcient but also effective to improve the genetic trait prediction. MUSE also achieved very significant improvements on a real plant data set as well as a real human data set.

Three-dimensional B-spline-based intersubject nonrigid registration with geodesic closest points constraints

Due to the variance between subjects, there is usually ambiguity in intensity-based intersubject registration. The topological constraint in the brain cortical surface might be violated because of the highly convolved nature of the human cortical cortex. We propose an intersubject brain registration method by combining the intensity and the geodesic closest point-based similarity measurements. Each of the brain hemispheres can be topologically equal to a sphere and a one-to-one mapping of the points on the spherical surfaces of the two subjects can be achieved. The correspondences in the cortical surface are obtained by searching the geodesic closest points in the spherical surface. The corresponding features on the cortical surfaces between subjects are then used as anatomical landmarks for intersubject registration. By adding these anatomical constraints of the cortical surfaces, the intersubject registration results are more anatomically plausible and accurate. We validate our method by using real human datasets. Experimental results in visual inspection and alignment error show that the proposed method performs better than the typical joint intensity- and landmark-distance-based methods.

Maximum likelihood estimation for second level fMRI data analysis with expectation trust region algorithm

The trust region method which originated from the Levenberg–Marquardt (LM) algorithm for mixed effect model estimation are considered in the context of second level functional magnetic resonance imaging (fMRI) data analysis. We first present the mathematical and optimization details of the method for the mixed effect model analysis, then we compare the proposed methods with the conventional expectation-maximization (EM) algorithm based on a series of datasets (synthetic and real human fMRI datasets). From simulation studies, we found a higher damping factor for the LM algorithm is better than lower damping factor for the fMRI data analysis. More importantly, in most cases, the expectation trust region algorithm is superior to the EM algorithm in terms of accuracy if the random effect variance is large. We also compare these algorithms on real human datasets which comprise repeated measures of fMRI in phased-encoded and random block experiment designs. We observed that the proposed method is faster in computation and robust to Gaussian noise for the fMRI analysis. The advantages and limitations of the suggested methods are discussed.

Recovering the geographic origin of early modern humans by realistic and spatially explicit simulations.

Most genetic and archeological evidence argue in favor of a recent and unique origin of modern humans in sub-Saharan Africa, but no attempt has ever been made at quantifying the likelihood of this model, relative to alternative hypotheses of human evolution. In this paper, we investigate the possibility of using multilocus genetic data to correctly infer the geographic origin of humans, and to distinguish between a unique origin (UO) and a multiregional evolution (ME) model. We introduce here an approach based on realistic simulations of the genetic diversity expected after an expansion process of modern humans into the Old World from different possible areas and their comparison to observed data. We find that the geographic origin of the expansion can be correctly recovered provided that a large number of independent markers are used, and that precise information on past demography and potential places of origins is available. In that case, it is also possible to unambiguously distinguish between a unique origin and a multiregional model of human evolution. Application to a real human data set of 377 STR markers tested in 22 populations points toward a unique but surprising North African origin of modern humans. We show that this result could be due to ascertainment bias in favor of markers selected to be polymorphic in Europeans. A new estimation modeling this bias explicitly reveals that East Africa is the most likely place of origin for modern humans.