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Article ; Online: Quantification and visualization of

Liu, Chaozhong / Wang, Linhua / Liu, Zhandong

NAR genomics and bioinformatics

2024 Volume 6, Issue 1, Page(s) lqae007

Abstract: Recent advances in single-cell multi-omics technologies have provided unprecedented insights into regulatory processes. We introduce TREASMO, a versatile Python package designed to quantify and visualize transcriptional regulatory dynamics in single-cell ...

Abstract	Recent advances in single-cell multi-omics technologies have provided unprecedented insights into regulatory processes. We introduce TREASMO, a versatile Python package designed to quantify and visualize transcriptional regulatory dynamics in single-cell multi-omics datasets. TREASMO has four modules, spanning data preparation, correlation quantification, downstream analysis and visualization, enabling comprehensive dataset exploration. By introducing a novel single-cell gene-peak correlation strength index, TREASMO facilitates accurate identification of regulatory changes at single-cell resolution. Validation on a hematopoietic stem and progenitor cell dataset showcases TREASMO's capacity in quantifying the gene-peak correlation strength at the single-cell level, identifying regulatory markers and discovering temporal regulatory patterns along the trajectory.
Language	English
Publishing date	2024-02-02
Publishing country	England
Document type	Journal Article
ISSN	2631-9268
ISSN (online)	2631-9268
DOI	10.1093/nargab/lqae007
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Article ; Online: Single-cell multi-omics integration for unpaired data by a siamese network with graph-based contrastive loss.

Liu, Chaozhong / Wang, Linhua / Liu, Zhandong

BMC bioinformatics

2023 Volume 24, Issue 1, Page(s) 5

Abstract: Background: Single-cell omics technology is rapidly developing to measure the epigenome, genome, and transcriptome across a range of cell types. However, it is still challenging to integrate omics data from different modalities. Here, we propose a ... ...

Abstract	Background: Single-cell omics technology is rapidly developing to measure the epigenome, genome, and transcriptome across a range of cell types. However, it is still challenging to integrate omics data from different modalities. Here, we propose a variation of the Siamese neural network framework called MinNet, which is trained to integrate multi-omics data on the single-cell resolution by using graph-based contrastive loss. Results: By training the model and testing it on several benchmark datasets, we showed its accuracy and generalizability in integrating scRNA-seq with scATAC-seq, and scRNA-seq with epitope data. Further evaluation demonstrated our model's unique ability to remove the batch effect, a common problem in actual practice. To show how the integration impacts downstream analysis, we established model-based smoothing and cis-regulatory element-inferring method and validated it with external pcHi-C evidence. Finally, we applied the framework to a COVID-19 dataset to bolster the original work with integration-based analysis, showing its necessity in single-cell multi-omics research. Conclusions: MinNet is a novel deep-learning framework for single-cell multi-omics sequencing data integration. It ranked top among other methods in benchmarking and is especially suitable for integrating datasets with batch and biological variances. With the single-cell resolution integration results, analysis of the interplay between genome and transcriptome can be done to help researchers understand their data and question.
MeSH term(s)	Humans ; Multiomics ; COVID-19 ; Transcriptome ; Neural Networks, Computer ; Single-Cell Analysis/methods
Language	English
Publishing date	2023-01-04
Publishing country	England
Document type	Journal Article
ZDB-ID	2041484-5
ISSN	1471-2105 ; 1471-2105
ISSN (online)	1471-2105
ISSN	1471-2105
DOI	10.1186/s12859-022-05126-7
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Article ; Online: Correction: Single-cell multi-omics integration for unpaired data by a siamese network with graph-based contrastive loss.

Liu, Chaozhong / Wang, Linhua / Liu, Zhandong

BMC bioinformatics

2023 Volume 24, Issue 1, Page(s) 123

Language	English
Publishing date	2023-03-29
Publishing country	England
Document type	Published Erratum
ZDB-ID	2041484-5
ISSN	1471-2105 ; 1471-2105
ISSN (online)	1471-2105
ISSN	1471-2105
DOI	10.1186/s12859-023-05249-5
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Article: Unraveling Spatial Gene Associations with SEAGAL: a Python Package for Spatial Transcriptomics Data Analysis and Visualization.

Wang, Linhua / Liu, Chaozhong / Liu, Zhandong

bioRxiv : the preprint server for biology

2023

Abstract: Summary: In the era where transcriptome profiling moves towards single-cell and spatial resolutions, the traditional co-expression analysis lacks the power to fully utilize such rich information to unravel spatial gene associations. Here we present a ... ...

Abstract	Summary: In the era where transcriptome profiling moves towards single-cell and spatial resolutions, the traditional co-expression analysis lacks the power to fully utilize such rich information to unravel spatial gene associations. Here we present a Python package called Spatial Enrichment Analysis of Gene Associations using L-index (SEAGAL) to detect and visualize spatial gene correlations at both single-gene and gene-set levels. Our package takes spatial transcriptomics data sets with gene expression and the aligned spatial coordinates as input. It allows for analyzing and visualizing spatial correlations at both single-gene and gene-set levels. The output could be visualized as volcano plots and heatmaps with a few lines of code, thus providing an easy-yet-comprehensive tool for mining spatial gene associations. Availability and implementation: The Python package SEAGAL can be installed using pip: https://pypi.org/project/seagal/ . The source code and step-by-step tutorials are available at: https://github.com/linhuawang/SEAGAL . Contact: linhuaw@bcm.edu.
Language	English
Publishing date	2023-02-13
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.02.13.528331
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Article: scGREAT: Graph-based regulatory element analysis tool for single-cell multi-omics data.

Liu, Chaozhong / Wang, Linhua / Liu, Zhandong

bioRxiv : the preprint server for biology

2023

Abstract: Motivation: With the development in single-cell multi-omics sequencing technology and data integration algorithms, we have entered the single-cell multi-omics era. Current multi-omics analysis algorithms failed to systematically dissect the ... ...

Abstract	Motivation: With the development in single-cell multi-omics sequencing technology and data integration algorithms, we have entered the single-cell multi-omics era. Current multi-omics analysis algorithms failed to systematically dissect the heterogeneity within the datasets when inferring cis-regulatory events. Thus, there is a need for cis-regulatory element inferring algorithms that considers the cellular heterogeneity. Results: Here, we propose scGREAT, a single-cell multi-omics regulatory state analysis Python package with a rapid graph-based correlation measurement Availability: https://github.com/ChaozhongLiu/scGREAT.
Language	English
Publishing date	2023-01-28
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.01.27.525916
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Article ; Online: A benchmark for RNA-seq deconvolution analysis under dynamic testing environments.

Jin, Haijing / Liu, Zhandong

Genome biology

2021 Volume 22, Issue 1, Page(s) 102

Abstract: Background: Deconvolution analyses have been widely used to track compositional alterations of cell types in gene expression data. Although a large number of novel methods have been developed, due to a lack of understanding of the effects of modeling ... ...

Abstract	Background: Deconvolution analyses have been widely used to track compositional alterations of cell types in gene expression data. Although a large number of novel methods have been developed, due to a lack of understanding of the effects of modeling assumptions and tuning parameters, it is challenging for researchers to select an optimal deconvolution method suitable for the targeted biological conditions. Results: To systematically reveal the pitfalls and challenges of deconvolution analyses, we investigate the impact of several technical and biological factors including simulation model, quantification unit, component number, weight matrix, and unknown content by constructing three benchmarking frameworks. These frameworks cover comparative analysis of 11 popular deconvolution methods under 1766 conditions. Conclusions: We provide new insights to researchers for future application, standardization, and development of deconvolution tools on RNA-seq data.
MeSH term(s)	Computational Biology/methods ; Computational Biology/standards ; Gene Expression Profiling/methods ; RNA-Seq/methods ; RNA-Seq/standards ; Reproducibility of Results ; Software
Language	English
Publishing date	2021-04-12
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2040529-7
ISSN	1474-760X ; 1474-760X
ISSN (online)	1474-760X
ISSN	1474-760X
DOI	10.1186/s13059-021-02290-6
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Article: A cross-sectional study exploring the relationship between symptoms of anxiety/depression and P50 sensory gating in adult patients diagnosed with chronic fatigue syndrome/myalgic encephalomyelitis.

Liu, Xinyi / Liu, Sitong / Ren, Runtao / Wang, Xue / Han, Chunyu / Liu, Zhandong

Frontiers in neuroscience

2024 Volume 17, Page(s) 1286340

Abstract: Introduction: Chronic fatigue syndrome (CFS) is a clinical disease that affects multiple body systems. It is characterized by persistent or recurring fatigue, which may be linked to immune, neuroendocrine, and energy metabolism dysfunctions. Individuals ...

Abstract	Introduction: Chronic fatigue syndrome (CFS) is a clinical disease that affects multiple body systems. It is characterized by persistent or recurring fatigue, which may be linked to immune, neuroendocrine, and energy metabolism dysfunctions. Individuals with CFS may experience pain, sleep disorders, anxiety, and depression. This research analyzed the fundamental characteristics of anxiety/depression symptoms in patients with CFS and investigated the association between these symptoms and the P50 SG (sensory gate) ratio. Methods: Two hundred and forty-nine subjects fulfilled the CDC-1994 criteria for CFS and were included in the study. The subjects successively completed the Symptom CheckList-90-Revised (SCL-90-R), Hamilton Anxiety Rating Scale-14 (HAMA-14), and Hamilton Depression Rating Scale-24 (HAMD-24). Auditory-evoked potential P50 were measured using the 128-lead-electroencephalograph. Result: According to HAMA and HAMD, 17.3% ( Discussion: A significant correlation exists between the P50 SG ratio and clinical symptoms such as fatigue, anxiety, and depression. Abnormalities in brain function among patients with CFS may play a crucial role in the pathogenesis of the condition, leading to their classification as being prone to functional neurological disorders. The P50 SG ratio cannot be used as a diagnostic marker for CFS but show some significance on the mechanism, classification, treatment, and prognosis of CFS.
Language	English
Publishing date	2024-01-05
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2411902-7
ISSN	1662-453X ; 1662-4548
ISSN (online)	1662-453X
ISSN	1662-4548
DOI	10.3389/fnins.2023.1286340
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Article ; Online: PolyAMiner-Bulk is a deep learning-based algorithm that decodes alternative polyadenylation dynamics from bulk RNA-seq data.

Jonnakuti, Venkata Soumith / Wagner, Eric J / Maletić-Savatić, Mirjana / Liu, Zhandong / Yalamanchili, Hari Krishna

Cell reports methods

2024 Volume 4, Issue 2, Page(s) 100707

Abstract: Alternative polyadenylation (APA) is a key post-transcriptional regulatory mechanism; yet, its regulation and impact on human diseases remain understudied. Existing bulk RNA sequencing (RNA-seq)-based APA methods predominantly rely on predefined ... ...

Abstract	Alternative polyadenylation (APA) is a key post-transcriptional regulatory mechanism; yet, its regulation and impact on human diseases remain understudied. Existing bulk RNA sequencing (RNA-seq)-based APA methods predominantly rely on predefined annotations, severely impacting their ability to decode novel tissue- and disease-specific APA changes. Furthermore, they only account for the most proximal and distal cleavage and polyadenylation sites (C/PASs). Deconvoluting overlapping C/PASs and the inherent noisy 3' UTR coverage in bulk RNA-seq data pose additional challenges. To overcome these limitations, we introduce PolyAMiner-Bulk, an attention-based deep learning algorithm that accurately recapitulates C/PAS sequence grammar, resolves overlapping C/PASs, captures non-proximal-to-distal APA changes, and generates visualizations to illustrate APA dynamics. Evaluation on multiple datasets strongly evinces the performance merit of PolyAMiner-Bulk, accurately identifying more APA changes compared with other methods. With the growing importance of APA and the abundance of bulk RNA-seq data, PolyAMiner-Bulk establishes a robust paradigm of APA analysis.
MeSH term(s)	Humans ; Polyadenylation/genetics ; RNA-Seq ; RNA ; Deep Learning ; Sequence Analysis, RNA/methods ; Algorithms
Chemical Substances	RNA (63231-63-0)
Language	English
Publishing date	2024-02-06
Publishing country	United States
Document type	Journal Article
ISSN	2667-2375
ISSN (online)	2667-2375
DOI	10.1016/j.crmeth.2024.100707
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Article: NMRQNet: a deep learning approach for automatic identification and quantification of metabolites using Nuclear Magnetic Resonance (NMR) in human plasma samples.

Wang, Wanli / Ma, Li-Hua / Maletic-Savatic, Mirjana / Liu, Zhandong

bioRxiv : the preprint server for biology

2023

Abstract: Nuclear Magnetic Resonance is a powerful platform that reveals the metabolomics profiles within biofluids or tissues and contributes to personalized treatments in medical practice. However, data volume and complexity hinder the exploration of NMR spectra. ...

Abstract	Nuclear Magnetic Resonance is a powerful platform that reveals the metabolomics profiles within biofluids or tissues and contributes to personalized treatments in medical practice. However, data volume and complexity hinder the exploration of NMR spectra. Besides, the lack of fast and accurate computational tools that can handle the automatic identification and quantification of essential metabolites from NMR spectra also slows the wide application of these techniques in clinical. We present NMRQNet, a deep-learning-based pipeline for automatic identification and quantification of dominant metabolite candidates within human plasma samples. The estimated relative concentrations could be further applied in statistical analysis to extract the potential biomarkers. We evaluate our method on multiple plasma samples, including species from mice to humans, curated using three anticoagulants, covering healthy and patient conditions in neurological disorder disease, greatly expanding the metabolomics analytical space in plasma. NMRQNet accurately reconstructed the original spectra and obtained significantly better quantification results than the earlier computational methods. Besides, NMRQNet also proposed relevant metabolites biomarkers that could potentially explain the risk factors associated with the condition. NMRQNet, with improved prediction performance, highlights the limitations in the existing approaches and has shown strong application potential for future metabolomics disease studies using plasma samples.
Language	English
Publishing date	2023-03-02
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.03.01.530642
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Article ; Online: Region-specific denoising identifies spatial co-expression patterns and intra-tissue heterogeneity in spatially resolved transcriptomics data.

Wang, Linhua / Maletic-Savatic, Mirjana / Liu, Zhandong

Nature communications

2022 Volume 13, Issue 1, Page(s) 6912

Abstract: Spatially resolved transcriptomics is a relatively new technique that maps transcriptional information within a tissue. Analysis of these datasets is challenging because gene expression values are highly sparse due to dropout events, and there is a lack ... ...

Abstract	Spatially resolved transcriptomics is a relatively new technique that maps transcriptional information within a tissue. Analysis of these datasets is challenging because gene expression values are highly sparse due to dropout events, and there is a lack of tools to facilitate in silico detection and annotation of regions based on their molecular content. Therefore, we develop a computational tool for detecting molecular regions and region-based Missing value Imputation for Spatially Transcriptomics (MIST). We validate MIST-identified regions across multiple datasets produced by 10x Visium Spatial Transcriptomics, using manually annotated histological images as references. We benchmark MIST against a spatial k-nearest neighboring baseline and other imputation methods designed for single-cell RNA sequencing. We use holdout experiments to demonstrate that MIST accurately recovers spatial transcriptomics missing values. MIST facilitates identifying intra-tissue heterogeneity and recovering spatial gene-gene co-expression signals. Using MIST before downstream analysis thus provides unbiased region detections to facilitate annotations with the associated functional analyses and produces accurately denoised spatial gene expression profiles.
MeSH term(s)	Transcriptome/genetics
Language	English
Publishing date	2022-11-14
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-022-34567-0
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