About

Joint Bioconductor Asia - Hong Kong Bioinformatics Symposium
The purpose of this symposium is to share scientiﬁc information with other researchers and scientists and to promote communication in the ﬁeld of bioinformatics in Hong Kong and internationally.
Additionally, this conference will also host multiple workshops in R and Bioconductor to enhancethe education and training in computational biomedical sciences.

General Information
Date: 15-17 Oct 2023
Website: https://biocasia2023.bioconductor.org/
Venue: Boardroom, 1/F, Faculty Administration Wing (FAW), LKS Faculty of Medicine, 21 Sassoon
Road

Co-chairs
Dario Strbenac
Ellis Patrick

Organizer Comittee
Dr. Yuanhua Huang, HKU
Dr. Joshua Ho, HKU
Dr. Xueyi Dong, WEHI
Dr. Yingying Wei, CUHK
Dr. Jiguang Wang, HKUSTDr. Can Yang, HKUST

1

Monday, 16 Oct 2023

Evolving an ecosystem for scalable genomic data science
Prof. Vincent Carey
Harvard University
Bioconductor is entering its third decade as a central element of the genomic data science toolbox.
The problem space addressed by the Bioconductor community and ecosystem has diversiﬁed
greatly, while the solution components surfaced in Bioconductor are mostly R packages. There ismuch more under the surface, and our approaches to tackling technical and substantive problems
are changing at various scales. New approaches to genomic data and annotation representation areneeded. Conﬁguration complexity must be reduced. Developer support to improve reliability andperformance must dovetail with education of the user community to ensure the tooling matches
the most pressing needs. I will discuss selected developments in each of these areas.

Improved Estimation of Functional Enrichment in SNP Heritability Using Feasible
Generalized Least Squares
Mr. Zewei Xiong

Functional enrichment results typically implicate tissue or cell-type speciﬁc biological pathways in
disease pathogenesis and as therapeutic targets. We propose generalized Linkage disequilibrium
score regression (g-LDSC), a novel method that requires only genome-wide association studies(GWAS) summary level data to estimate functional enrichment. The method adopts the same
assumptions and regression model formulation as stratiﬁed Linkage disequilibrium score regression(s-LDSC). While s-LDSC only partially utilizes LD information, our method utilizes the whole LD
matrix which accounts for possible correlated error structure via a feasible generalized leastsquares estimation. We demonstrate through simulation studies under various scenarios, thatg-LDSC provides more precise estimates of functional enrichment than s-LDSC, regardless ofmodel misspeciﬁcation. In an application to GWAS summary statistics of 15 traits from the UK
Biobank, estimates of functional enrichment using g-LDSC were lower, and more realistic, thanthose obtained from s-LDSC. In addition, g-LDSC detected more signiﬁcantly enriched functionalannotations among 24 functional annotations for the 15 traits than s-LDSC (118 versus 51).

2

Systems approach for congruence and selection of cancer models towards precision medicine
Dr. Jian Zou
Chongqing Medical University
Cancer models are instrumental to substitute for human studies and expedite basic, translationaland clinical cancer research. For a given cancer subtype, a wide selection of models, such as celllines, patient-derived xenografts, tumoroids and genetically modiﬁed murine models, are often
available to researchers. However, how to quantify their congruence to human tumors and toselect the most appropriate cancer model is a largely unsolved issue. Here, we develop Congruence
Analysis and Selection of CAncer Models (CASCAM), a statistical and machine learning framework
for authenticating and selecting the most representative cancer models in pathway-speciﬁc anddrug-relevant context using transcriptomic data. CASCAM oﬀers harmonization between tumor
and cancer model omics data, interpretable machine learning for congruence quantiﬁcation,mechanistic investigation, and pathway-based topological visualization to determine the ﬁnal
cancer model selection. The workﬂow is presented using breast cancer invasive lobular carcinoma(ILC) subtype, while the method is generalizable to any cancer subtype for precision medicine
development.

High-Dimensional Causal Mediation in Imaging Genetics Study
Prof. George Tseng
University of Pittsburgh
Causal mediation analysis provides a systematic approach to investigate the causal role of one ormore mediators in an exposure-outcome association. In omics or imaging data analysis, mediators
are often high-dimensional, which brings new statistical challenges. Existing methods either violatecausal assumptions or fail in interpretable variable selection. Additionally, mediators are often
highly correlated, presenting diﬃculties in selecting and prioritizing top mediators. To address theseissues, we develop a framework using Partial Sum Statistic and Sample Splitting Strategies, namely
PS5, for general high- dimensional causal mediation analyses. The method provides a powerfulglobal mediation test satisfying causal assumptions, followed by an algorithm to select and prioritize
active mediators with quantiﬁcation of individual mediation contributions. We demonstrate its
accurate type I error control, superior statistical power, reduced bias in mediation eﬀect estimation,and high mediator selection accuracy using extensive simulations of varying levels of eﬀect size,
signal sparsity and correlation in mediators. Finally, we apply PS5 to a imaging genetics datasetof chronic obstructive pulmonary disease (COPD) patients (N =8,897) in the COPDGene study to
examine the causal mediation of lung images (p=5,810) in the associations between polygenic
risk score and lung function and between smoking exposure and lung function, separately. Bothcausal mediation analyses successfully estimate the global indirect eﬀect and detect mediating
image regions. We ﬁnd a lung region in the lower lobe of right lung with strong and concordant
mediation eﬀect for both genetic and environmental exposure, which argues the potential oftargeted treatment towards this region to control the severity of COPD due to genetic factor andcigarette smoking

3

A statistical method for Cross-population ﬁne-mapping by leveraging genetic
diversity and accounting for confounding bias
Dr. Mingxuan Cai
City University of Hong Kong
Fine-mapping prioritizes risk variants identiﬁed by genome-wide association studies (GWASs),
serving as a critical step to uncover biological mechanisms underlying complex traits. However,
several major challenges still remain for existing ﬁne-mapping methods. First, the strong linkage
disequilibrium among variants can limit the statistical power and resolution of ﬁne-mapping.
Second, it is computationally expensive to simultaneously search for multiple causal variants. Third,
the confounding bias hidden in GWAS summary statistics can produce spurious signals. To addressthese challenges, we develop a statistical method for cross-population ﬁne-mapping (XMAP) byleveraging genetic diversity and accounting for confounding bias. By using cross-population GWAS
summary statistics from global biobanks and genomic consortia, we show that XMAP can achieve
greater statistical power, better control of false positive rate, and substantially higher computationaleﬃciency for identifying multiple causal signals, compared to existing methods. Importantly, weshow that the output of XMAP can be integrated with single-cell datasets, which greatly improves
the interpretation of putative causal variants in their cellular context at single-cell resolution.

Functional Adaptive Double-Sparsity Estimator for Functional Linear Regression
with Application in Wearable Sensor Data Analysis
Dr. Xinyue Li
City University of Hong Kong
Wearable sensors have been increasingly used in health monitoring and early anomaly detection.
Wearable device can collect objective and continuous information on physical activity and vital
signs and have great potentials in studying the association with health outcomes. However, how
to eﬀectively analyze high-frequency multi-dimensional sensor data is challenging. In this talk,we propose a new Functional Adaptive Double-Sparsity Estimator (FadDoS) based on functional
regularization of sparse group lasso with multiple functional predictors, which can achieve global
sparsity via functional variable selection and local sparsity via zero-subinterval identiﬁcation withincoeﬃcient functions. We prove that the FadDoS estimator converges at a bounded rate and satisﬁes
the oracle property under mild conditions. Extensive simulation studies conﬁrm the theoreticalproperties and exhibit excellent performances compared to existing approaches. Application toa Kinect sensor study that utilized an advanced motion sensing device tracking human multiple
joint movements and conducted among community-dwelling elderly demonstrates how FadDoS
can eﬀectively characterize the detailed association between joint movements and physical healthassessments. The proposed method is not only eﬀective in Kinect sensor analysis but also applicable
to broader ﬁelds, where multi-dimensional sensor signals are collected simultaneously, to expand
the use of sensor devices in health studies.

4

Identifying predictors of glioma evolution from longitudinal sequencing
Dr. Quanhua Mu
The Hong Kong University of Science and Technology
Clonal evolution drives cancer progression and therapeutic resistance. Recent studies have revealed
divergent longitudinal trajectories in gliomas, but early molecular features steering post-treatment
cancer evolution remain unclear. Here we collected sequencing and clinical data of initial-recurrent
tumor pairs from 544 adult diﬀuse gliomas and performed multivariate analysis to identify earlymolecular predictors of tumor evolution in three diﬀuse glioma subtypes. We found that CDKN2A
deletion at initial diagnosis preceded tumor necrosis and microvascular proliferation that occur
at later stages of IDH-mutant glioma. Ki67 expression at diagnosis was positively correlated withacquiring hypermutation at recurrence in the IDH-wildtype glioma. Strikingly, in all glioma subtypes,
MYC gain or MYC-target activation at diagnosis was associated with treatment-induced hypermutation at recurrence. To predict glioma evolution, we constructed CELLO2 (Cancer EvoLution
for LOngitudinal data version 2), a machine-learning model integrating features at diagnosis toforecast hypermutation and progression after treatment. CELLO2 successfully stratiﬁed patients
into subgroups with distinct prognoses and identiﬁed a high-risk patient group featured by MYC
gain with worse post-progression survival, from the low-grade IDH-mutant-non-codel subtype. We
then performed chronic temozolomide-induction experiments in glioma cell lines and isogenic
patient-derived gliomaspheres and demonstrated that MYC drives temozolomide resistance bypromoting hypermutation. Mechanistically, we demonstrated that, by binding to open chromatin
and transcriptionally active genomic regions, c-MYC increases the vulnerability of key mismatch
repair genes to treatment-induced mutagenesis, thus triggering hypermutation. This study reveals
early predictors of cancer evolution under therapy and provides a resource for precision oncology
targeting cancer dynamics in diﬀuse gliomas.

5Epigenetic cancer drug facilitates the proteogenomics discovery of immunotherapy targets
Dr. Stephen Li
The University of Hong Kong
In recent years, immunotherapy has emerged as a promising cancer treatment. By stimulating
the host anti-tumor immune response, immunotherapy has improved prognosis and reduced
the risk of relapsing. Nonetheless, the availability of neoantigen targets has limited the clinicaleﬃcacy of immunotherapy. The conventional method of identifying neoantigens, which focuses ontumor-speciﬁc SNVs and INDELs, suﬀers from patient speciﬁcity, dependence on mutation burden,
and low immunogenicity. Such limitations underscore the importance of identifying neoantigens
from other genetic elements.
Transposable Elements (TE) can serve as a potential source of neoantigens. Recent studies haveidentiﬁed TE-derived products as recurring targets for neoantigens. TE can trigger various immune
responses, from producing dsRNA for viral mimicry to producing TE-derived peptide targets forcancer vaccines or CAR-T. However, TE expression is prone to repression by DNA methylation. One
strategy to overcome TE epigenetic silencing is to reverse DNA methylation via cancer drugs like
decitabine (DAC). However, the use of DAC to discover TE-derived antigens, particularly peptides
that involve splicing between TE and exons, is currently rare.
This study aims to identify DAC-driven TE neoantigens across cancer types, using published andin-house transcriptomic data encompassing AML, glioblastoma, and colorectal cancers. The current
ﬁndings have highlighted DAC-induced upregulation of TE subfamilies and TE-exon splice junctions,with varying subfamily distribution across diﬀerent types of cancer. Compared with canonicaltranscripts, TE-derived antigens showed more frequent upregulation than exon-derived splicing isoforms. Translated peptide sequences from DAC-induced TE-derived antigens will enable subsequent
proteogenomics search, HLA-aﬃnity prediction, and experimental assays on immunogenicity. The
results of this study will provide a novel list of neoantigen targets while highlighting the potentialof using DAC in conjunction with immunotherapy across cancer types.

The TEMDA approach in drug design
Prof. Balaji Seetharaman

6Unmasking LUAD’s Vulnerabilities: Targeting AURKA-TPX2 Interaction for Innovative Therapeutics
Dr. MUKUNTHAN KS
Manipal Institute of Technology
Lung adenocarcinoma (LUAD) is one of the most prevalent and leading causes of cancer deaths
globally, with limited diagnostic and clinically signiﬁcant therapeutic targets. Identifying the genes
and processes involved in developing and progressing¬†LUAD is crucial for developing eﬀective
targeted therapeutics and improving patient outcomes. Therefore, the study aimed to explore theRNA sequencing data of LUAD from The Cancer Genome Atlas (TCGA) and gene expression proﬁledatasets involving GSE10072, GSE31210, and GSE32863 from the Gene Expression Omnibus (GEO)
databases. The diﬀerential gene expression and the downstream analysis determined clinicallysigniﬁcant biomarkers using a network-based approach. These therapeutic targets predominantly
enriched the dysregulation of mitotic cell cycle regulation and revealed the co-overexpression of
Aurora-A Kinase (AURKA) and Targeting Protein for Xklp2 (TPX2) with high survival risk in LUAD
patients. The hydrophobic residues of the AURKA-TPX2 interaction were considered as the target
site to block the autophosphorylation of AURKA during the mitotic cell cycle. The tyrosine kinase
inhibitor (TKI) dacomitinib demonstrated the strong binding potential to hinder TPX2, shielding the
AURKA destabilization. This in silico study lays the foundation for repurposing targeted therapeutic
options to impede the protein-protein interactions (PPIs) in LUAD progression and aid in futuretranslational investigations.

7Integrative Epigenomics and Transcriptomics Analysis to Identify Signature Biomarkers of Lung Adenocarcinoma
Mr. Arnab Mukherjee
Manipal Institute of Technology
Lung carcinoma is one of the most prevalent and life-threatening cancers globally, with tobaccosmoking being the most signiﬁcant cause of lung cancer deaths. Lung adenocarcinoma (LUAD)
accounts for approximately 80-85% of reported lung cancer cases and unfolds in a sequential
multistage pattern, gradually developing genetic and epigenetic alterations. Alterations in DNA
methylation at CpG sites are associated with smoking-induced lung cancer. Smoking-related epigenetic alterations are involved in the modulation of multiple biological pathways. Numerous tumors
exhibit atypical methylation patterns, which can involve either increased (hypermethylation) or
decreased (hypomethylation) addition of a methyl group to the cytosine. Demethylation of CpG
sites is associated with the upregulation of oncogenes and genomic instability observed in multiple
solid tumors, including lung cancer. However, hypermethylation is linked to the downregulationof the genes and silencing of tumor suppressors. Enhancers govern gene expression across great
distances by looping DNA and oﬀering distant regulatory regions closer to their target gene promoters. Therefore, we employed Illumina HM450k DNA methylation data of patients from The Cancer
Genome Atlas (TCGA) to determine enhancers and link enhancer status with the expression of target genes to discover transcriptional targets using The Enhancer Linking by Methylation/Expression
Relationship (ELMER) package of Bioconductor. In this study, we investigated a technique forpredicting enhancer-target interactions by combining epigenomic and transcriptomic data from asubstantial collection of primary tumor samples. This approach allowed us to identify target genes
speciﬁcally regulated by enhancers with diﬀerential methylation patterns in LUAD and revealed
the target genes of the diﬀerentially methylated sites and the enriched motifs modulating theirexpression in LUAD progression. The network-based approach aided in determining the hub genes
playing a key role as central regulators of ribosome biogenesis, RNA processing, cell cycle regulation,and MMR pathways in LUAD pathogenesis.

8Epigenetic clock CpGs are associated with chromatin and are enriched at enhancers
Mr. Yik Chai Charles Lau
The University of Hong Kong
DNA methylation-based epigenetic clocks are biomarkers that can determine chronological age,mortality and propensity for age-associated diseases (1). Since 2011 (2), epigenetic clocks havebecome known for being among the most robust and accurate ageing predictors, with the popular
Horvath 2013 clock capable of accurately predicting age across multiple tissue types (3). The
mechanisms underlying epigenetic clocks are still poorly understood as existing clocks have all been
generated using microarrays which are biased towards promoter regions, hindering an accuratequantitative and qualitative assessment of the CpGs (cytosine-guanine motifs) that form such clocks
and their likely causal relationships with ageing. With the current goals of ﬁnding and categorizing
features underlying Horvath clock CpGs and CpGs that are signiﬁcantly correlated with age, andconstructing models to systematically identify and classify CpGs for age predicting capacity, awhole genome bisulphite (WGBS) dataset was built and used for epigenetic clock creation and
CpG enrichment analysis. This dataset consisting of 617 samples resulted from a comprehensivemining of GEO (4) for all compatible WGBS samples that have age metadata, and as such is diverse
not only in age range, but also disease status (cancer and neuropathology, etc.), tissue type andother traits, making it suitable both for the veriﬁcation of the multitissue Horvath clock and the
exploration of relationships between age-associated eﬀects on the methylome and vice-versa. The
analyses conﬁrm that both the epigenetic clock methodology and Horvath‚Äôs clock are applicableto the data and that it contains age-predicting information. Age acceleration eﬀects, such as
deceleration in females and schizophrenic patients are reproduced. Annotation of age-signiﬁcant
CpGs for genomic and chromatin features in the dataset demonstrates signiﬁcant association withchromatin states, especially enhancers. The results imply that causes of ageing should be sought
within chromatin and histone-based mechanisms, with a primary emphasis on enhancer-associatedpathways.
1. Horvath S, Raj K. DNA methylation-based biomarkers and the epigenetic clock theory of ageing.
Nat Rev Genet. 2018 Apr 11;19.
2. Bocklandt S, Lin W, Sehl ME, S°nchez FJ, Sinsheimer JS, Horvath S, et al. Epigenetic Predictor ofAge. PLOS ONE. 2011 Jun 22;6(6):e14821.
3. Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013 Dec
10;14(10):3156.
4. NCBI GEO: archive for functional genomics data sets‚Äîupdate | Nucleic Acids Research | Oxford
Academic [Internet]. [cited 2023 Sep 15].
Available from: https://academic.oup.com/nar/article/41/D1/D991/1067995?login=false

9

Copy number state minimum evolutionary path algorithm for inferring wholegenome doublings
Mr. Wai Tung Jonathan Tse
The University of Hong Kong
The study of DNA copy numbers is essential to many areas of genomics including research into
population diversity and evolutionary processes of diseases. Various copy number inference tools
have been made to infer copy number states of tumor samples using sequencing data. However,some tools are limited by the use of non-identiﬁable models which give ambiguous solutions,potentially giving solutions with large discrepancies in ploidy which suggest the tumor sample tobe both diploid or tetraploid. We introduce an algorithm which infers whole genome doubling
status to choose an optimal solution between multiple solutions based on minimum steps in thetumor evolutionary path using copy number states.

Transcriptomic analysis of cisplatin resistance in ovarian cancer
Ms. Noel Yue
The University of Hong Kong
High-grade serous ovarian cancer (HGSOC) is the most aggressive type of ovarian cancer; standard
treatment of HGSOC consists of debulking followed by platinum-based chemotherapy. Meanwhile,
34% of patients will develop resistance during the treatment. We seek to determine the transcriptomic dysregulation that lead to platinum resistance in high grade serous ovarian cancer with thegene expression proﬁle of the HGSOC samples. Speciﬁcally, we would like to encounter cellular
heterogeneity such that we will be performing transcriptome deconvolution computationally. However, we confronted common bioinformatics obstacles, including mismatches in gene expression
platforms, batch eﬀects, missing normal references for deconvolution, and suboptimal results inpublicly accessible algorithms. To address these challenges, we implemented the following strategies: conduct quantile normalization on mismatch-platform gene expression data, referencing the
distribution of the TCGA-ovarian cancer RNA-seq dataset, such that all the datasets follow a similar
distribution; batch eﬀects are mitigated through the application of ComBat; pseudo normal bulk
RNA-seq data is generated using normal single cell RNA-seq data, serving as a substitute for theabsent of normal references for deconvolution; conduct evaluation of multiple publicly available
algorithms and made modiﬁcations to the algorithm to optimize its performance. Successfully
addressing these challenges will lead us to a robust dataset that allows us to conduct in-depth
analyses, ultimately leading us to determine the transcriptomic dysregulation that lead to platinum
resistance in HGSOC patients.

10Comprehensive genomic characterization of EBV-associated carcinomas
Ms. Yucan ChenThe University of Hong Kong
Epstein-Barr virus (EBV)-associated malignancies display unique clinicopathologic features andmolecular alterations, including genetic and epigenetic changes. However, suﬃcient molecularevidence supporting the interaction between EBV and the host genome is still lacking. We aimed
to investigate the host genomic alterations and identify features associated with EBV infection.
We downloaded whole-genome sequencing data and RNA-seq data of gastric carcinoma (GC) andBurkitt lymphoma (BL) samples, both annotated with EBV status, and ChIP-seq data of GM12878
and B cells. We investigated the mutational proﬁles and mutational signatures of EBV-infected anduninfected groups and conducted replication timing, replication strand bias, and transcriptional
bias analyses on samples labeled EBV-positive and EBV-negative. We then explore the regional
diﬀerences in mutation distribution across the genome and the changes of chromatin modiﬁcations
upon EBV infection. Finally, we performed diﬀerential expression and gene set enrichment analyses(GSEA) for hints from transcriptional data. We found the mutations in EBV-infected samples werefeatured with T>G in the TTT trinucleotide context and TTTTN pentanucleotide context. The
mutational signatures showed diﬀerent SBS28 and SBS17 exposure associated with EBV infection,
more prevalent SBS28, and less SBS17 in EBV-infected samples in both GC and BL samples. The
trend of replication timing curves of T>G and T>A mutations were higher in early replication
timing regions in EBV-infected samples. We found the distinct mutation distribution across genic
regions and diﬀerent chromatin states, and the modiﬁcation signals in GM12878 versus B cells
correlated diﬀerentially with mutations between EBV-associated GCs (EBVaGCs) and controls, ﬂatterin EBVaGCs. The GSEA result unraveled the altered expression of genes involved in DNA repairassociated pathways enriched in EBV-positive samples. Our ﬁndings characterized EBV-associatedgenomic features, indicating the distinct etiology behind EBV-associated carcinomas shedding lighton the diﬀerent therapeutic strategies for cancers and their EBV-associated subtypes.

11

Tuesday, 17 Oct 2023

Exploring Cell Fate and Memory through Single-Cell Multi-Omic Lineage Tracing
Dr. Shou-Wen Wang
Westlake University
Cellular lineage histories and their molecular states encode fundamental principles of tissue development and homeostasis. Current lineage-recording mouse models have insuﬃcient barcode
diversity and single-cell lineage coverage for proﬁling tissues composed of millions of cells. Here,
we developed DARLIN, an inducible Cas9 barcoding mouse line that utilizes terminal deoxynucleotidyl transferase (TdT) and 30 CRISPR target sites. DARLIN is inducible, generates massive
lineage barcodes across tissues, and enables detection of edited barcodes in 70% of proﬁled single
cells. Using DARLIN, we examined fate bias within developing hematopoietic stem cells (HSCs)
and revealed unique features of HSC migration. Additionally, we established a protocol for joint
transcriptomic and epigenomic single-cell measurements with DARLIN and found that cellularclonal memory is associated with genome-wide DNA methylation rather than gene expression or
chromatin accessibility. DARLIN will enable high- resolution study of lineage relationships and theirmolecular signatures in diverse tissues and physiological contexts.

Covidscope: An atlas scale COVID‚Äì19 resource for single-cell meta analysis at
sample and cellular levels
Ms. Danqing (Angela) Yin
The University of Hong Kong
To triangulate the cellular response and processes from the sample level (e.g. individual patient)in the COVID-19 pandemic, single-cell technologies have been widely exploited. The abundanceof rising single-cell data atlases poses an unprecedented barrier for researchers to eﬃciently
accessing, organizing, sharing and interpreting massively available information. Here, we provide
a carefully integrated atlas-scale resource Covidscope oﬀering a uniform method by utilizing
scMerge, scClassify and scFeature to accelerate eﬃcient downstream meta-analysis for COVID19 single-cell data. We acquired 5 million single PBMC cells from 20 studies consisting of 1000
donors across the globe and made it available online through the Covidscope data portal via
https://covidsc.d24h.hk. The ability to search and visualize large single cell expression data
matrices in real-time based on users‚ Äô queries up to 4x101̂4 combinations using patient orcell-level characteristics makes Covidscope distinctive to other existing portals. We believe that
Covidscope will have a profound impact on the scientiﬁc community, providing a ﬁrst encyclopedic
all-in-one resource for COVID-19 researchers to unravel the hidden and complex insights at theatlas scale.

12Temporal preserved representation learning from time-series single-cell transcriptomics data
Ms. Yijun Liu
The University of Hong Kong
Time-series designs of single-cell RNA-seq experiments have been broadly utilised to study various
biological processes, including embryonic development, cell diﬀerentiation, and disease progression. However, it remains a grand challenge to eﬀectively discover dynamical modules and theaccording gene regulation from such temporally structured data, largely due to the tight couplingof time stamps and experiment batches and the complexity of cell states. In this study, we introduce temporalVAE, a computational method that leverages a variational auto-encoder to infer
the biological time of cells from a compressed latent space, and simultaneously conducts batchcorrection via an adversarial learning strategy. We illustrate the eﬀectiveness and strengths of
temporalVAE on experimental data sets from multiple biological systems, including ﬁne-resolution
mouse embryonic development and human endometrium across the menstrual cycle. Notably,
our results highlight a strong positive correlation between temporalVAE’s prediction capabilities
and the time-sensitivity of distinct cell types. As the diversity of single-cell data types continues
to expand, we envision that temporalVAE can further integrate diﬀerent types of data and obtain
continuous cellular time, which will facilitate the elucidation of gene regulatory processes at an
unprecedented resolution.

Identiﬁcation of potential neural precursor cell populations using atlas-level
single-cell data
Prof. Zhichao Miao
Guangzhou National Laboratory
The extent to which neurogenesis occurs in adult primates remains controversial. In this study,
using an optimized single-cell RNA sequencing pipeline, we proﬁled 207,785 cells from the adultmacaque hippocampus and identiﬁed 34 cell populations comprising all major hippocampal cell
types. Analysis of their gene expression, speciﬁcation trajectories and gene regulatory networks
revealed the presence of all key neurogenic precursor cell populations, including a heterogeneouspool of radial glia-like cells (RGLs), intermediate progenitor cells (IPCs) and neuroblasts. We
identiﬁed HMGB2 as a novel IPC marker. Comparison with mouse single-cell transcriptomic data
revealed diﬀerences in neurogenic processes between species. We conﬁrmed that neurogenesis is
recapitulated in ex vivo neurosphere cultures from adult primates, further supporting the existenceof neural precursor cells (NPCs) that are able to proliferate and diﬀerentiate. Our large-scale dataset
provides a comprehensive adult neurogenesis atlas for primates13

Annotating and discovering cell types from single cell RNA-seq data using machinelearning
Dr. Yu Li
The Chinese University of Hong Kong

JSNMF enables eﬀective and accurate integrative analysis of single-cell multiomics data
Prof. Zhixiang Lin
The Chinese University of Hong Kong
The single-cell multiomics technologies provide an unprecedented opportunity to study the cellular
heterogeneity from diﬀerent layers of transcriptional regulation. However, the datasets generated
from these technologies tend to have high levels of noise, making data analysis challenging. Here,
we propose jointly semi-orthogonal nonnegative matrix factorization (JSNMF), which is a versatile
toolkit for the integrative analysis of transcriptomic and epigenomic data proﬁled from the samecell. JSNMF enables data visualization and clustering of the cells and also facilitates downstream
analysis, including the characterization of markers and functional pathway enrichment analysis.
The core of JSNMF is an unsupervised method based on JSNMF, where it assumes diﬀerent latentvariables for the two molecular modalities, and integrates the information of transcriptomic andepigenomic data with consensus graph fusion, which better tackles the distinct characteristics andlevels of noise across diﬀerent molecular modalities in single-cell multiomics data. We applied
JSNMF to single-cell multiomics datasets from diﬀerent tissues and diﬀerent technologies. The
results demonstrate the superior performance of JSNMF in clustering and data visualization of thecells. JSNMF also allows joint analysis of multiple single-cell multiomics experiments and single-cell
multiomics data with more than two modalities proﬁled on the same cell. JSNMF also provides
rich biological insight on the markers, cell-type-speciﬁc region-gene associations and the functionsof the identiﬁed cell subpopulation.

14

Diﬀerential inference for single-cell RNA-sequencing data
Dr. Fangda SongThe Chinese University of Hong Kong, Shenzhen
Single-cell RNA-sequencing (scRNA-seq) experiments are becoming more and more complicatedwith multiple biological conditions or treatments. However, guidelines on experimental designs
and rigorous statistical methods for a comparative scRNA-seq study that collects data from multiple
conditions are still lacking. Here, we derive the conditions for a valid design for a comparative scRNAseq study so that the batch eﬀects, cell type eﬀects, and condition eﬀects can be decomposed. We
develop an interpretable Bayesian hierarchical model, DIFferential Inference for Single-cell RNAsequencing Data (DIFseq), to rigorously quantify the condition eﬀects on both cellular compositions
and cell-type-speciﬁc gene expression levels for scRNA-seq data. DIFseq substantially outperforms
the state-of-the-art methods in terms of the accuracy of cell type clustering, identiﬁcation of
diﬀerential cellular abundance, and detection of intrinsic genes‚Äîgenes that are diﬀerentiallyexpressed (DE) between cell types at the baseline‚Äîand cell-type-speciﬁc DE genes‚Äîgenes thatare DE between conditions for both simulated and real data.

Spatiotemporal transcriptomic and genomic analysis reveals waves of hematopoiesis
in human embryonic organoids
Ms. Yiming Chao
1. The University of Hong Kong, HKSAR 2. Centre for Translational Stem Cell Biology, HKSAR 3.
Wellcome-MRC Cambridge Stem Cell Institute, UK
Hematopoiesis is a dynamic process that encompasses multiple waves at diﬀerent sites throughout
the lifespan. The ﬁrst extra-embryonic hematopoiesis wave arises in the yolk sac during the early
embryonic stage. It remains unknown how extra-embryonic tissues support the hematopoiesis in
the blood island during embryo development. In our recent work, we model human embryonic development and hematopoiesis with stem-cell-derived human embryonic organoid (HEMO) and use
advanced sequencing technology including single-cell RNA sequencing and spatial transcriptomics
to deﬁne the gene expression and cellular interactions. Single-cell resolution spatial transcriptomics
deﬁned the yolk sac erythro-megakaryopoietic niche. Vitronectin-integrin signaling remarked theyolk sac niche in HEMO and human fetal samples. Moreover, we exploit the mitochondrial SNVs as
an innate cellular barcode to discriminate diﬀerent hematopoietic waves that correspond to known
human embryonic hematopoiesis in the yolk sac. Our study advances the spatiotemporal transcriptomic and genomic analysis of human embryonic development in stem-cell-derived organoids.
We emphasize the regulations of extra-embryonic tissues during development and highlight the
dynamic hematopoiesis modeled by the organoids.

15

Unveiling the Complexity: The Current State of Single-Cell Benchmarking
Dr. Yue Cao
University of Sydney
With the rapid development of computational methods for single-cell sequencing data, benchmarking provides a valuable solution for guiding method selection. As the number of single-cellbenchmarking studies has surged over the years, it is now timely to assess the current state of theﬁeld and to understand best practices behind high-quality benchmarks. To this end, we conducteda systematic literature search and comprehensively assessed a total of 245 papers. Among these,
we examined all 95 pure benchmarking papers from the literature search and selected an additional
150 papers across multiple areas of method development that include a benchmarking component
in their evaluation. We illustrated the characteristic of benchmarking across ten broad complementary categories covering over 70 variables. As expected, our analysis revealed a diﬀering focus
in evaluation between pure benchmarking papers and method development papers. In addition,
our analysis highlights a number of challenges such as dataset dependent method performanceand the emerging issue related to eﬀective benchmarking. This work lays the foundation for theneed of collective eﬀort from the community to establish a repository for benchmarking datasets
as well as a ‚Äúliving benchmark‚Äù platform that facilitate methods comparison for single-cell
method developers.

Deep autoencoder for interpretable tissue-adaptive deconvolution and cell-typespeciﬁc gene analysis
Ms. Yixuan Wang
The Chinese University of Hong Kong
Single-cell RNA-sequencing has become a powerful tool to study biologically signiﬁcant characteristics at explicitly high resolution. However, its application on emerging data is currently limited by
its intrinsic techniques. Here, we introduce Tissue-AdaPtive autoEncoder (TAPE), a deep learning
method connecting bulk RNA-seq and single-cell RNA-seq to achieve precise deconvolution in ashort time. By constructing an interpretable decoder and training under a unique scheme, TAPE
can predict cell-type fractions and cell-type-speciﬁc gene expression tissue-adaptively. Comparedwith popular methods on several datasets, TAPE has a better overall performance and comparable
accuracy at cell type level. Additionally, it is more robust among diﬀerent cell types, faster, andsensitive to provide biologically meaningful predictions. Moreover, through the analysis of clinicaldata, TAPE shows its ability to predict cell-type-speciﬁc gene expression proﬁles with biological signiﬁcance. We believe that TAPE will enable and accelerate the precise analysis of high-throughput
clinical data in a wide range.

16

Deciphering sequence-informed regulatory patterns from single-cell multi-omics
Ms. Fangxin Cai
The University of Hong Kong
Single cell multi-omics presents parallel and asynchronous perspectives to biological processes.
Spanning transcription and RNA splicing, paired scRNA-seq and scATAC-seq data provide complementary views to gene regulation. To disentangle their relation, previous works categorize
transcription patterns by the coupling between ATAC and RNA data, which corresponds to distinct
regulatory mechanisms. These patterns can be diﬃcult to interpret especially when based on
reduced data dimensions.
We present a sequence-informed autoencoder framework that may extract biologically meaningful
cell dimensions. On spliced and unspliced RNA data, latent cell dimensions retain splicing dynamics,
from which RNA velocity can be inferred. For more asynchronous ATAC and RNA data, cell embeddings are aligned by sequence elements such that similar TSS and peak sequences are activatedby the same dimension. Cross modal prediction reveals coupled and uncoupled chromatin andexpression dynamics. Globally, ATAC states align to later RNA states known as transcription priming.
The relation between latent RNA velocity and ATAC dimensions remains to be modeled.

Con-AAE: contrastive cycle adversarial autoencoders for single-cell multi-omics
alignment and integration
Mr. Xuesong Wang
The Chinese University of Hong Kong
Motivation: We have entered the multi-omics era and can measure cells from diﬀerent aspects.
Hence, we can get a more comprehensive view by integrating or matching data from diﬀerent
spaces corresponding to the same object. However, it is particularly challenging in the single-cell
multi-omics scenario because such data are very sparse with extremely high dimensions. Thoughsome techniques can be used to measure scATAC-seq and scRNA-seq simul- taneously, the data
are usually highly noisy due to the limitations of the experimental environment.
Results: To promote single-cell multi-omics research, we overcome the above challenges, proposing
a novel framework, contrastive cycle adversarial autoencoders, which can align and integrate singlecell RNA-seq data and single-cell ATAC- seq data. Con-AAE can eﬃciently map the above data withhigh sparsity and noise from diﬀerent spaces to a coordinated subspace, where alignment andintegration tasks can be easier. We demonstrate its advantages on several datasets.

17

Biologically-informed self-supervised learning for segmentation of subcellularspatial transcriptomics data
Dr. Xiaohang Fu
The University of Sydney
Recent advances in subcellular imaging transcriptomics platforms have enabled high-resolution
spatial mapping of gene expression, while also introducing signiﬁcant analytical challenges in accurately identifying cells and assigning transcripts. Existing methods grapple with cell segmentation,
frequently leading to fragmented cells or oversized cells that capture contaminated expression. To
this end, we present BIDCell, a self-supervised deep learning-based framework with biologicallyinformed loss functions that learn relationships between spatially resolved gene expression andcell morphology. BIDCell incorporates cell-type data, including single-cell transcriptomics datafrom public repositories, with cell morphology information. Using a comprehensive evaluation
framework consisting of metrics in ﬁve complementary categories for cell segmentation performance, we demonstrate that BIDCell outperforms other state-of-the-art methods according tomany metrics across a variety of tissue types and technology platforms. Our ﬁndings underscorethe potential of BIDCell to signiﬁcantly enhance single-cell spatial expression analyses, enabling
great potential in biological discovery.

Benchmarking translational potential of spatial transcriptomics imputation from
histology images
Ms. Chuhan Wang
The University of Sydney
Spatial transcriptomics (ST) enables the quantiﬁcation of gene expression within speciﬁc spatial
coordinates, oﬀering crucial insights into tumour heterogeneity of tissues at high resolution. In
light of this, the feasibility of predicting spatial gene expression (GE) from conveniently obtainable
and cost-eﬀective haematoxylin-and-eosin-stained (H&E) histology images gains signiﬁcance. To
this end, we conducted a comprehensive benchmarking study encompassing 6 developed methods
designed to predict spatial GE from H&E histology images. These methods were reproduced
and evaluated using HER2-positive breast tumour (her2st) and human cutaneous squamous cell
carcinoma (cSCC) datasets through a 4-fold cross-validation framework, followed by external
validation using The Cancer Genome Atlas (TCGA-BRCA) data. Our evaluation demonstrates diverse
metrics, including the performance measures of predicted and ground truth GE values, performancevariations across diﬀerent image regions and levels of gene expression, the translational potential
indicated by survival analysis, and the overall usability of the methods.

18

Systematic comparison of sequencing-based spatial transcriptomic methods
Dr. Luyi Tian
Guangzhou Laboratory
Sequencing-based spatial transcriptomic techniques have undergone rapid development in recent
years, enabling unbiased, transcriptome-scale measurements of spatial gene expression. However,these methods have yet to be systematically benchmarked, and the considerable variability acrosstechnologies and datasets complicates the establishment of evaluation standards. To address this,we have developed a set of reference tissues with well-deﬁned histological structures, utilizingthem to generate data and assess six distinct technologies. Despite variations in resolution, capture
eﬃciency, and spatial precision, spatial transcriptomic data exhibit characteristics distinct fromsingle-cell RNAseq data, such as enhanced capabilities for capturing certain genes, along with more
pronounced blood contamination. This study aims not only to guide biologists in method selection
but also to build a consensus on evaluation criteria, establish a framework for future benchmarking,and provide gold standards for the assessment of computational tools

CellContrast: Reconstructing Spatial Relationships in Single-Cell RNA Sequencing
Data via Deep Contrastive Learning
Dr. Shumin Li
The University of Hong Kong
A vast amount of single-cell RNA-seq (SC) data has been accumulated via various studies andconsortiums, but the lack of spatial information limits its analysis dissection of complex biologicalactivities. To bridge this gap, we introduce cellContrast, a computational method for reconstructing
spatial relationships among SC cells from spatial transcriptomics (ST) reference. By adopting a deep
contrastive learning framework and training with ST data, cellContrast projects gene expressions
into a hidden space where proximate cells share similar representation values. We performed extensive benchmarking on diverse platforms, including SeqFISH, Stereo-Seq, and 10X Visium, on mouseembryo and human breast cells. The results reveal that cellContrast substantially outperforms
other related methods, facilitating accurate spatial reconstruction of SC. We further demonstrate
cellContrast’s utility by applying it to cell-type co-localization and cell-cell communication analysis
with real-world SC samples, proving the recovered cell locations empower novel discoveries andmitigate potential false positives.

19

Decoding Fine-grained Cellular Architectures from Histology Images
Mr. Weiqin Zhao
The University of Hong Kong
The cellular architecture of tissues, where distinct cell types are organized in space, underlies
cell‚Äìcell communication, organ function and pathology. Emerging spatial transcriptomics technologies provide opportunities to map resident cell types and cell signaling in situ in a scalable
manner. The key principle is to integrate spatial RNA-seq with the reference transcriptome signatures of cell types obtained from coupled scRNA-seq proﬁles. However, spatial transcriptome
data has not been utilized in large-scale studies due to the expensive costs. Relatively, histology
images are much cheaper and easier to obtain and are routinely generated in clinics. Nevertheless,
how to identify ﬁne-grained cell type spatial variations and cell-cell communication directly from
histology images remains to be an important problem. To address this issue, we present Hist2Cell, aGraph-Transformer framework to resolve ﬁne-grained cell types directly from histology images andcreate cellular maps of diverse tissues. Hist2Cell was trained on human breast cancer and humanlung spatial transcriptome datasets with spot-level cell abundance estimations. For held-out test
patients, Hist2Cell can predict the spatial variation in the localization of ﬁne-grained cell types - at aresolution of around 100Œºm. Moreover, it can reveal accurate cell-cell communications by feeding
predictions to SpatialDM toolbox, ﬁguring out the prioritization of interaction features as well as
the identiﬁcation of interaction spots in the spatial context. More importantly, Hist2Cell can also
provide above analysis under higher resolution than spatial transcriptome data in a scalable manner.
As independent external tests, Hist2Cell accurately predicts spatial cell localization and cell-cellcommunications without any modiﬁcation or tuning. This suggests that it robustly generalizes to
new samples. Hist2Cell is more accurate than methods that predicting cell abundance solely fromsingle histology image spot or limited neighboring context, as it leverages both local and global
correlations of histology image spots with a Graph-Transformer architecture.

20

Towards the Visual-Transcriptomics Foundation Model for Spatial Transcriptomics
Analysis
Ms. Zhuo Liang
The University of Hong Kong
Histopathology, with its remarkable advancements in tumor analysis and morphology, is becoming
indispensable for studying correlations in disease pathology. Spatial transcriptomics, with its highspatial resolution, has emerged as a powerful tool for studying gene expression, enabling us toinvestigate the relationship between cellular neighborhoods and gene patterns. Existing works
have revealed the connection between image morphology and genomics through analyzing images
and gene expressions independently, while neglecting the potential advantages of aligning these
two modalities. We argue that aligning modalities can open up new possibilities for the inferenceof genomics from morphology, thereby advancing our understanding of spatial transcriptomics.
In this study, we propose a visual-transcriptomics foundation model named CONTH (CONtrastive
learning from Transcriptomics for Histopathology) to align image and gene representations eﬀectively in latent space. This process, referred to as the contrastive task, utilizes cross-entropy loss toevaluate the dissimilarity between two distributions achieved by projecting image and gene expression into low-dimensional space. In addition to assessing the accuracy of the alignment process,the preservation of vital information is crucial. To address this concern, we introduce a generativetask. After obtaining image representations, we generate corresponding gene predictions basedon these representations. Subsequently, we calculate the image-to-gene reconstruction loss bycomparing the predictions with the ground truth. Similarly, we reconstruct images using gene
representations and compute the gene-to-image reconstruction loss. These three losses work
synergistically to guide the learning process of CONTH.
We demonstrate the eﬀectiveness of our framework via retrieval which serves as an indicator forthe generalized consistency between two modalities. On 23 cross-tissue 10xGenomics datasets,
we currently achieved a recall of 34%, compared to 20% by randomized selection, proving thesuperiority of alignment.
We foresee our model will pave the way for a more comprehensive knowledge of the intricate
interplay between morphology and genomics.

21

Multi-task Deep Learning Network for Embryo Quality Assessment
Ms. Lu Yu
The University of Hong Kong
Blastocyst selection is one of the most signiﬁcant challenges in the in-vitro fertilization (IVF) process
because the current assessment is largely based on morphology evaluation by embryologists whichbrings more subjectivity and less precision. Artiﬁcial intelligence can play a role in overcoming
the limitations of the manual assessment system by inputting either observation of single static
morphology images or time-lapse video sequences.
Here, we design a multi-task deep learning network to assist blastocyst selection using static
images. The ﬁrst branch can automatically grading the fresh blastocyst based on the morphology
of expansion (1-6), inner cell mass (A,B,C), and trophectoderm (A,B,C) respectively, which is trainedon a gold-standard public dataset. It performs well on our clinical data by using a small size of ourclinical data to ﬁnetune, which shows good potential of overcoming the bias caused by subjectivity
in diﬀerent embryologists and IVF centers. The second branch which can assess the embryo viability,
is trained not only on fresh blastocyst dataset but also by creatively borrowing richer informationfrom post-thawing embryo dataset as frozen embryo transfer has been proved to lead to betterlive-birth outcomes. YOLO-v3 model is deployed to locate and crop the embryo from the static
image to overcome the eﬀects of background noise, which can also extends in time-lapse images.
Various preprocessing and online and oﬄine augmentation techniques are applied to balance andﬂourish the dataset. A series of backbone model pretrained on Imagenet, such as ResNet andVision Transformer, are combined with grid-search and bayesian optimization to establish optimalmodel which results in the most accurate predictions.

Experiment-guided clustering to unravel complex drug-diet interactions in nutriomics data
Prof. Jean Yang
University of Sydney
Easier and more comprehensive access to large omics data has revolutionised not just biologicalresearch but also nutrition research. The emerging ﬁeld of nutriomics is enhancing our understanding of the interactions between nutrients and the host, oﬀering novel insights into personalised
nutrition and deepening our understanding of nutrition on health. In this talk, we will discuss
how recent approaches in statistical modelling and machine learning methods are addressing data
science challenges presented by both experimental and proﬁling studies in nutriomics. Specifically, we will present eNODAL, an experiment-guided clustering method that takes advantage
of both ANOVA-type analyses and unsupervised learning methods in order to extract maximum
information from experimental nutriomics studies. Applying eNODAL on a mouse proteomics study,
we demonstrate eNODAL ability to identify clusters of proteins that are aﬀected by interactions
between nutrition information and drug intake.

22

Cell-type aware CRISPR editing outcomes prediction
Mr. Weizhong Zheng
The University of Hong Kong
The CRISPR-Cas system has revolutionized gene editing by enabling precise and eﬃcient modiﬁcations, encompassing a wide range of applications, including gene activity modulation and thegeneration of model organisms. However, the inherent variability in CRISPR-induced DNA repair
processes, inﬂuenced by sequence context and cell lines, poses a signiﬁcant challenge in accurately
predicting editing outcomes before conducting experiments. In this study, we introduce inDecay, a
ﬂexible system for predicting CRISPR editing outcomes. InDecay predicts the probability of a large
number of editing outcomes derived from the target sequence, and incorporates cell-type-speciﬁc
repair preferences through a multi-stage design. By utilizing informative and parameter-eﬃcientfeatures for each indel event, inDecay outperforms existing prediction methods across multiple
evaluation measures, including identifying the most common DNA alterations and the overalloccurrence of frameshift mutations. Furthermore, inDecay exhibits superior few-shot learning
capabilities when transferred to novel cell types. The unique features of inDecay establish it asa valuable tool for planning CRISPR editing experiments that require precise control over editing
outcomes or for experiments conducted in previously unexplored cellular environments.

Integrating long-read RNA sequencing improves locus-speciﬁc quantiﬁcation oftransposable element expression
Ms. Sojung Lee
The University of Hong Kong
Endogenous transposable elements (TEs) are implicated in human diseases due to their propensity to compromise genome integrity. Although short-read sequencing is now frequently used
to examine TE expression, the highly repetitive nature of TEs limits their accurate quantiﬁcation
at the locus-speciﬁc level. We have developed LocusMasterTE, an improved method that integrates information from long-read RNA sequencing to enhance TE quantiﬁcation. The fractionaltranscript per million (TPM) from long reads serves as a prior distribution during the ExpectationMaximization (EM) model in short-read TE quantiﬁcation, thereby enabling the reassignment ofmulti-mapped reads to correct expression values. Using simulated short-reads, our results indicatethat LocusMasterTE outperforms existing quantitative approaches and is especially favorable forquantifying evolutionarily younger TEs. Using matched cell line RNA-seq data, we further demonstrate improved locus-speciﬁc TE quantiﬁcation by LocusMasterTE with stronger enrichment in
active, and depletion at repressive, histone marks. Finally, by integrating colorectal cancer cellline long-read sequencing data with short read RNA-seq data from The Cancer Genome Atlas
colorectal cancer cohort, we demonstrate LocusMasterTE‚Äôs ability to identify survival-related
TEs and uncover new expression associations between locus-speciﬁc TEs and neighboring genes.
By providing more accurate quantiﬁcation, LocusMasterTE oﬀers the potential to reveal novel
functions of TE transcripts.

23

Improving long-read scRNA-seq data analysis with FLAMES
Mr. Changqing Wang
Walter And Eliza Hall Institute Of Medical ResearchLong-read sequencing enables accurate determination of novel isoforms, yet pipelines dedicatedfor such analyses are limited. The previous FLAMES pipeline covers from data preprocessing all
the way to diﬀerential analyses. We recently updated the FLAMES pipeline to integrate with standard Bioconductor containers , adding support multi-sample datasets, support for using external
packages to replace certain steps and additional data visualisation functions. The updated pipeline
is now modularised, with each step using standard ﬁle formats as inputs and outputs, allowing
more external packages to be included in FLAMES. We hope to integrate the software ecology ofthis ﬁeld in FLAMES to provide a convenient one-stop-shop for single cell long-read RNA-seq dataprocessing and analysis, and hence promoting further development in both the software side andthe application side.

Biodiversity Genomics - From evolution to conservation policy
Prof. Jerome Hui
The Chinese University of Hong Kong

Lipidomic signatures of resilience to coronary artery disease learnt from chimpanzees
Mr. Andy Tran
University of Sydney
Coronary artery disease continues to be one of the leading causes of death globally. Despite
sharing over 96% of our DNA sequence, our closest animal relative, chimpanzees, have remarkablediﬀerences in susceptibility to coronary artery disease, where there has only been a single report ofdeath from myocardial infarction, despite thousands of necropsies being performed. In this study,
blood plasma has been collected from 20 chimpanzees where over 800 lipid species were isolated
and analysed. The same protocol was performed on the BioHEART-CT Discovery cohort, withapproximately 1000 patients. We investigate the diﬀerences between humans and chimpanzees
to reveal evolutionary diﬀerences in their lipidomic proﬁles, and discover lipidomic signatures ofresilience to coronary artery disease.

24

TDbasedUFE and TDbasedUFEadv: bioconductor packages to perform tensor
decomposition based unsupervised feature extraction
Prof. Y-h. Taguchi
Chuo University
Motivation: Tensor decomposition (TD) based unsupervised feature extraction (FE) [1] has proveneﬀective for a wide range of bioinformatics applications ranging from biomarker identiﬁcation to
the identiﬁcation of disease-causing genes and drug repositioning. It can process any kind of omics
data set formatted as tensor. For example, gene expression of various tissues of many humansubjects can be analyzed to select tissue speciﬁc diﬀerentially expressed genes. Alternatively, by
selecting drugs that target selected genes, we can also perform drug repositioning. We can also
make use of this method to seek biomarkers of diseases. However, TD-based unsupervised FE
failed to gain widespread acceptance due to the lack of user-friendly tools for non-experts.
Results: We developed two bioconductor packages -TDbasedUFE and TDbasedUFEadv- enable
researchers unfamiliar with TD to utilize TD-based unsupervised FE [2]. TDbasedUFE facilitates
the identiﬁcation of diﬀerentially expressed genes and multiomics analysis. TDbasedUFE wasfound to outperform two state-of-the-art methods, DESeq2 and DIABLO. TDbasedUFEadv can deal
with various specialized cases, including vertical (features) and horizontal (samples) integration,
reduction of memory with summation of vertical or horizontal direction which can be later recovered and the usage of singular value decomposition as preprocessing. All of these featureallow users to analyse wide range of bioinformatics analysis. Availability and implementation
TDbasedUFE and TDbasedUFEadv are freely available as R/Bioconductor packages, which can be accessed at https://bioconductor.org/packages/TDbasedUFE and https://bioconductor.
org/packages/TDbasedUFEadv, respectively.
References: [1] https://doi.org/10.1007/978-3-030-22456-1[2] https://www.frontiersin.org/articles/10.3389/frai.2023.1237542/abstract25