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Form of work2024 Summer Intern - Wei Lab (Cellular and Tissue Genomics, CTG), co-mentored with Li Lab (Biology Research | Artificial Intelligence Development, BRAID)
Department Summary
Spatial transcriptomics (ST) represents a paradigm shift in clinical medicine and therapy. This cutting-edge technology offers an unprecedented view of gene expression within tissues, enhancing our understanding of disease pathology and uncovering novel therapeutic targets. It enables the observation of cellular community dynamics across diverse physiological and pathological states, shedding light on potential cell-cell interactions. However, the field faces challenges such as platform-related noise and computational biases. A critical challenge is the accurate annotation of cell types/states, essential for interpreting spatial transcriptome data. Effective cell annotation can significantly deepen our insight into cellular community shifts and uncover potential disease-related interactions. Despite these obstacles, the transformative potential of spatial transcriptomics is immense, underscoring the urgency to surmount these challenges, refine methodologies, and unravel the complex interplay between genetic activity, cellular behavior, and health or disease onset.
The Wei Lab at CTG is at the forefront of leveraging spatial and single-cell omics to decipher cellular dynamics in various human diseases and their potential clinical and translational applications. Dr. Wei, the Principal Investigator (PI), has pioneered the development of CellTrek (Nature Biotechnology, 2022), an innovative computational framework enabling precise single-cell-level spatial mapping. Additionally, Dr. Wei is a key contributor to the Human Breast Cell Atlas project, focusing on spatial data analysis (Nature, 2023). The Li Lab at BRAID focuses on single-cell multiomics and spatial omics data analysis and tool development, with applications to oncology. Dr. Li's representative works include RSEM, a gold standard tool for transcript quantification from bulk RNA-Seq data (Citation: 17,850) and Cumulus, the first comprehensive cloud-based analysis engine for single-cell RNA-seq (Nature Methods, 2020).
The Wei Lab seeks a summer intern with a strong foundation in Bioinformatics/Computational Biology, well-versed in single-cell and spatial transcriptomics. The candidate will be co-mentored by the Li Lab. The selected candidate will explore the dynamic field of spatial transcriptomics, engaging in systemic benchmarking of various cell annotation methods, including both reference-based and reference-free approaches. The project's goal is to develop a comprehensive recommendation framework. This role offers the intern a unique opportunity to contribute to pioneering research in spatial transcriptomics, potentially leading to groundbreaking discoveries that redefine our understanding of cellular behavior and disease mechanisms.
This internship position is located in South San Francisco, on site.
Key Responsibilities
Program Highlights
Who You Are (Required)
Required Education:
Required Skills:
Preferred Skills:
The expected salary range for this position based on the primary location of San Francisco, CA is $50/hr. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. This position also qualifies for paid holiday time off benefits.
Relocation benefits are not available for this job posting.
#GNE-gCS-2024-Interns
Genentech is an equal opportunity employer, and we embrace the increasingly diverse world around us. Genentech prohibits unlawful discrimination based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin or ancestry, age, disability, marital status and veteran status.
Department Summary
Spatial transcriptomics (ST) represents a paradigm shift in clinical medicine and therapy. This cutting-edge technology offers an unprecedented view of gene expression within tissues, enhancing our understanding of disease pathology and uncovering novel therapeutic targets. It enables the observation of cellular community dynamics across diverse physiological and pathological states, shedding light on potential cell-cell interactions. However, the field faces challenges such as platform-related noise and computational biases. A critical challenge is the accurate annotation of cell types/states, essential for interpreting spatial transcriptome data. Effective cell annotation can significantly deepen our insight into cellular community shifts and uncover potential disease-related interactions. Despite these obstacles, the transformative potential of spatial transcriptomics is immense, underscoring the urgency to surmount these challenges, refine methodologies, and unravel the complex interplay between genetic activity, cellular behavior, and health or disease onset.
The Wei Lab at CTG is at the forefront of leveraging spatial and single-cell omics to decipher cellular dynamics in various human diseases and their potential clinical and translational applications. Dr. Wei, the Principal Investigator (PI), has pioneered the development of CellTrek (Nature Biotechnology, 2022), an innovative computational framework enabling precise single-cell-level spatial mapping. Additionally, Dr. Wei is a key contributor to the Human Breast Cell Atlas project, focusing on spatial data analysis (Nature, 2023). The Li Lab at BRAID focuses on single-cell multiomics and spatial omics data analysis and tool development, with applications to oncology. Dr. Li's representative works include RSEM, a gold standard tool for transcript quantification from bulk RNA-Seq data (Citation: 17,850) and Cumulus, the first comprehensive cloud-based analysis engine for single-cell RNA-seq (Nature Methods, 2020).
The Wei Lab seeks a summer intern with a strong foundation in Bioinformatics/Computational Biology, well-versed in single-cell and spatial transcriptomics. The candidate will be co-mentored by the Li Lab. The selected candidate will explore the dynamic field of spatial transcriptomics, engaging in systemic benchmarking of various cell annotation methods, including both reference-based and reference-free approaches. The project's goal is to develop a comprehensive recommendation framework. This role offers the intern a unique opportunity to contribute to pioneering research in spatial transcriptomics, potentially leading to groundbreaking discoveries that redefine our understanding of cellular behavior and disease mechanisms.
This internship position is located in South San Francisco, on site.
Key Responsibilities
- Analyzing Spatial Transcriptomics with Multiple Platforms: The intern will analyze spatial transcriptomics data using various platforms such as 10X Visium, Xenium, and NanoString CosMx. This will also include the analysis of single-cell sequencing data.
- Benchmarking Different Cell Annotation/Label Transfer Approaches: A significant responsibility will involve benchmarking various cell annotation and label transfer methods (including single-cell reference-based approach and reference-free approach). This task will require the intern to assess the efficiency and accuracy of different approaches, comparing their performance in various scenarios. The intern will need to have a good understanding of computational biology and statistical methods to effectively evaluate these approaches.
- Providing a Systematic Recommendation Workflow: Lastly, the intern will be expected to develop and provide a systematic workflow for recommendation purposes. This workflow should integrate the insights gained from the analysis of spatial transcriptomics and the benchmarking of cell annotation methods. It should be designed in a way that is user-friendly and can be easily adapted for different research needs. The intern will need to have strong skills in data integration and workflow development, along with the ability to clearly communicate their recommendations.
Program Highlights
- Intensive 12-weeks, full time (40 hours per week) paid internship.
- Program start dates are in May/June (Summer)
- A stipend, based on location, will be provided to help alleviate costs associated with the internship.
- Ownership of challenging and impactful business-critical projects.
- Work with some of the most talented people in the biotechnology industry.
Who You Are (Required)
Required Education:
- Must be pursuing a PhD focused in Bioinformatics, Biostatistics, Computational biology, Computer science, or Bioengineering
- Enrolled within an accredited university
Required Skills:
- Demonstrated proficiency and deep understanding in Bioinformatics, Biostatistics, Computational biology, Computer science, or Bioengineering;
- Experienced in Spatial Transcriptomics and Single-Cell RNA-Seq Analysis: A solid track record of working with spatial transcriptomics and single-cell RNA sequencing data, showcasing a thorough understanding of these cutting-edge techniques
Preferred Skills:
- Advanced Proficiency in Python and R: Skilled in programming with Python and R, including extensive experience with specialized packages such as Seurat, Scanpy, and scVI-tools, essential for omics data analysis;
- Experience with Imaging Analysis and Deep Learning Frameworks (Preferred): Familiarity with imaging analysis (OpenCV, scikit-image, etc.) and deep learning frameworks (PyTorch, TensorFlow, Keras, etc.) is an advantageous addition, enhancing the capacity to handle image and high-dimensional datasets.
- Excellent communication, collaboration, and interpersonal skills.
- Complements our culture and the standards that guide our daily behavior & decisions: Integrity, Courage, and Passion.
The expected salary range for this position based on the primary location of San Francisco, CA is $50/hr. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. This position also qualifies for paid holiday time off benefits.
Relocation benefits are not available for this job posting.
#GNE-gCS-2024-Interns
Genentech is an equal opportunity employer, and we embrace the increasingly diverse world around us. Genentech prohibits unlawful discrimination based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin or ancestry, age, disability, marital status and veteran status.
