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Computational Spatial Transcriptomics information

What are some typical challenges faced when working in computational spatial transcriptomics, and how can new team members prepare for them?

Professionals in computational spatial transcriptomics often encounter challenges related to handling and analyzing large, complex datasets that combine spatial and gene expression information. Integrating data from different technologies and ensuring data quality can be demanding, requiring strong programming skills and familiarity with bioinformatics pipelines. New team members can prepare by strengthening their skills in statistical analysis, programming languages like Python or R, and staying updated on the latest spatial transcriptomics techniques. Collaborating closely with experimental biologists and data scientists is also key to overcoming these challenges and driving successful research outcomes.

What is the difference between Computational Spatial Transcriptomics vs Computational Biologist?

AspectComputational Spatial TranscriptomicsComputational Biologist
Required CredentialsAdvanced degrees in bioinformatics, computational biology, or related fields; experience with spatial data analysisTypically a PhD or Master's in biology, bioinformatics, or related disciplines; strong programming skills
Work EnvironmentResearch labs, biotech companies, academic institutions focusing on spatial genomicsResearch institutions, biotech firms, academia working on biological data analysis
Industry UsageSpecialized in spatial transcriptomics techniques and data interpretationBroad biological data analysis across various fields

Computational Spatial Transcriptomics focuses on analyzing spatial gene expression data within tissues, requiring specialized skills in spatial data processing. In contrast, Computational Biologists work on a wider range of biological data types. While both roles involve bioinformatics expertise, the former emphasizes spatial data analysis techniques specific to transcriptomics.

What is computational spatial transcriptomics?

Computational spatial transcriptomics is a field that combines advanced computational methods with spatial transcriptomics, a technique that measures gene expression within the physical context of tissue samples. It involves processing and analyzing large datasets to map where specific genes are active within tissues, helping researchers understand how cells interact and function in their native environments. This approach is crucial for studies in developmental biology, cancer research, and neuroscience, as it provides insights into cellular organization and tissue architecture. Computational tools help extract meaningful patterns from complex data, enabling discoveries that were previously impossible with traditional methods.

What are the key skills and qualifications needed to thrive as a Computational Spatial Transcriptomics Scientist, and why are they important?

To excel in Computational Spatial Transcriptomics, you need a strong background in bioinformatics, genomics, and statistical data analysis, typically supported by advanced degrees in computational biology or related fields. Familiarity with programming languages (such as R and Python), spatial transcriptomics platforms (like 10x Genomics Visium), and high-throughput sequencing data analysis tools is essential. Strong problem-solving skills, attention to detail, and effective communication are crucial soft skills for interpreting complex datasets and collaborating with multidisciplinary teams. These competencies ensure accurate data interpretation, innovative research, and successful integration of spatial transcriptomics insights into biological and clinical applications.
What are popular job titles related to Computational Spatial Transcriptomics jobs in Texas? For Computational Spatial Transcriptomics jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Computational Spatial Transcriptomics jobs in Texas look for? The top searched job categories for Computational Spatial Transcriptomics jobs in Texas are:
Computational Spatial Transcriptomics jobs near you
Infographic showing various Computational Spatial Transcriptomics job openings in Texas as of June 2026, with employment types broken down into 70% Full Time, 27% Part Time, 1% Temporary, and 2% Contract. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution.

Advanced Integrated Wet Lab and Computational Researcher, Surgery

429026 - SY-Lab Levi

Dallas, TX • On-site

$114K/yr

Full-time

Medical, Retirement, PTO

Posted 4 days ago

Job description

WHY UT SOUTHWESTERN?
With over 75 years of excellence in Dallas-Fort Worth, Texas, UT Southwestern is committed to excellence, innovation, teamwork, and compassion. As a world-renowned medical and research center, we strive to provide the best possible care, resources, and benefits for our valued employees. Ranked as the number 1 hospital in Dallas-Fort Worth according to U.S. News & World Report , we invest in you with opportunities for career growth and development to align with your future goals. Our highly competitive benefits package offers healthcare, PTO and paid holidays, on-site childcare, wage, merit increases and so much more. We invite you to be a part of the UT Southwestern team where you'll discover a culture of teamwork, professionalism, and a rewarding career!
JOB SUMMARY
Perform professional work in support of scientific research using technical knowledge of software, software development, networking, and/or hardware in a complex computing environment. Subject matter expert in multiple areas of computing technology, and specific knowledge of molecular biology and genetics. Work collaboratively with Principal Investigator ("PI") to determine the most suitable computational methods and tools to analyze large-scale biological data sets. Prepare reports for presentation or publication. Responsible for assisting PI in meeting the system needs for multiple research projects and/or labs.
Perform advanced, independent computational and systems biology research in support of high-impact translational and mechanistic studies in musculoskeletal biology, regeneration, tumor microenvironment biology, and aberrant mesenchymal cell fate.
Serve as a computational subject matter expert in multi-omic data integration, including single-cell RNA sequencing (scRNA-seq), single-nucleus ATAC-seq, spatial transcriptomics, CyTOF, bulk RNA-seq, proteomics, and imaging-derived quantitative datasets. Apply expertise in software development, high-performance computing, and reproducible bioinformatics pipelines within a complex research computing environment. Collaborate directly with the Principal Investigator (PI) and multidisciplinary teams to design computational strategies that interrogate cellular heterogeneity, lineage trajectories, immune-stromal interactions, extracellular matrix remodeling, mechanotransductive signaling, and neurovascular regulation of tissue regeneration and tumor progression. Lead computational components of peer-reviewed publications, develop predictive models, and generate high-dimensional datasets for presentation in high-impact journals and national/international scientific forums. Provide strategic computational oversight across multiple funded research projects within the laboratory.
JOB DUTIES
1. Multi-Omic Data Analysis and Integration
  • Design and implement analysis pipelines for scRNA-seq, snATAC-seq, spatial transcriptomics, bulk RNA-seq, CyTOF, and proteomics datasets.
  • Perform trajectory inference, cell-cell communication modeling, ligand-receptor interaction analysis, gene regulatory network reconstruction, and pathway enrichment analyses.
  • Integrate transcriptomic, epigenomic, and imaging-derived data to define mechanistic signaling axes.
  • Develop reproducible workflows using R, Python, and high-performance computing environments.
2. Translational and Predictive Modeling
  • Develop machine learning models for disease prediction and therapeutic response.
  • Identify biomarkers, therapeutic targets, and signaling networks in regenerative and oncologic contexts.
  • Analyze longitudinal and multimodal datasets to model dynamic cellular state transitions.
3. Mechanistic Discovery Support
  • Collaborate with experimental scientists to design experiments informed by computational hypotheses.
  • Perform differential expression, chromatin accessibility, and integrative multi-omic analyses.
  • Quantitatively analyze imaging datasets and integrate findings with molecular data.
4. Scientific Leadership and Publication
  • Generate publication-quality figures and high-dimensional data visualizations.
  • Lead computational sections of manuscripts, grants, and presentations.
  • Contribute to NIH grant preparation, including data analysis plans and preliminary data generation.
5. Laboratory Computational Infrastructure
  • Maintain and optimize laboratory computational pipelines and data management strategies.
  • Advise trainees and laboratory personnel on statistical design and data interpretation.
  • Ensure best practices in reproducibility, version control, and data sharing compliance.
6. Additional Responsibilities
  • Assist PI in strategic planning of computational approaches across multiple funded projects.
  • Mentor graduate students, postdoctoral fellows, and research staff in bioinformatics methodologies.
  • Perform other duties as assigned in support of laboratory research goals.
BENEFITS
UT Southwestern is proud to offer a competitive and comprehensive benefits package to eligible employees. Our benefits are designed to support your overall wellbeing, and include:
  • PPO medical plan, available day one at no cost for full-time employee-only coverage
  • 100% coverage for preventive healthcare-no copay
  • Paid Time Off, available day one
  • Retirement Programs through the Teacher Retirement System of Texas (TRS)
  • Paid Parental Leave Benefit
  • Wellness programs
  • Tuition Reimbursement
  • Public Service Loan Forgiveness (PSLF) Qualified Employer
  • Learn more about these and other UTSW employee benefits!
EXPERIENCE AND EDUCATION
Required
  • Education
    PhD in Computer Science or a related field of biological science, with thesis work in bioinformatics and computational biology or
    Master's Degree in Computer Science or a related field of biological science or
    Bachelor's Degree in Computer Science or a related field of biological science
  • Experience
    2 years of related research experience in bioinformatics and computational biology with Master's Degree or
    4 years of related research experience in bioinformatics and computational biology with Bachelor's Degree.
JOB DUTIES
  • Manage computer hardware and software programming and maintenance for one or more research laboratories. Ensure existing systems meet the continuing needs of the labs and/or recommend and test new systems as necessary.
  • Perform or direct others to perform complex data analysis related to specialized research methodologies and results. Manage assigned research projects with minimal input from PI.
  • Develop and/or modify software to support new and ongoing research projects.
  • Resolve hardware, software, and network issues in support of the lab.
  • Oversee the development and maintenance of complex databases to support new and ongoing research projects.
  • Prepare research reports for presentation, and assist with preparation of manuscripts for publication.
  • May specialize in bioinformatics, software development, network administration, or hardware/software maintenance.
  • Perform other duties as assigned.
SECURITY AND EEO STATEMENT
Security
This position is security-sensitive and subject to Texas Education Code 51.215, which authorizes UT Southwestern to obtain criminal history record information.
EEO
UT Southwestern Medical Center is committed to an educational and working environment that provides equal opportunity to all members of the University community. As an equal opportunity employer, UT Southwestern prohibits unlawful discrimination, including discrimination on the basis of race, color, religion, national origin, sex, sexual orientation, gender identity, gender expression, age, disability, genetic information, citizenship status, or veteran status.

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