scRNA-seq Pipeline Review
Describe where your Scanpy or Seurat workflow is becoming difficult, whether that is batch effects, cell-type annotation, or clustering decisions.
- ✓ Useful context: a short workflow summary and the point where it stops making sense
- ✓ Supported tools: Seurat (R) and Scanpy (Python)
- ✓ Next step: use the contact form to describe your research question
HPC & Environment Setup
If an environment, container, or Slurm job is blocking your work, get in touch with the command, error message, and the system you are using.
- ✓ Useful context: command output, environment file, or a safe error-log excerpt
- ✓ Supported tools: Conda, Slurm, and Singularity
- ✓ Next step: use the contact form to explain the setup problem
1-on-1 Mentoring
If you are moving from wet-lab work into computational biology, contact The Omics Hub about the concepts or tools you want to understand next.
- ✓ Topics: pipeline logic, coding foundations, and bioinformatics theory
- ✓ Useful context: your current background and the topic you want to clarify
- ✓ Next step: use the contact form to begin a conversation
Deep Dive into scRNA-seq Pipeline Review
Single-cell RNA sequencing (scRNA-seq) analysis is rarely a straightforward path. Even when following established tutorials, real-world datasets often present unique challenges that standard pipelines do not cover. This service is designed to help you navigate those specific roadblocks. Whether you are struggling with excessive mitochondrial reads in your quality control step, unexpected clustering patterns, or difficulty in assigning accurate cell-type annotations, a dedicated review can save weeks of frustration.
When you request a pipeline review, we will look at the specific parameters you have chosen and the biological rationale behind them. For example, setting the resolution parameter too high during clustering might fragment a single biological state into multiple artificial clusters, while setting it too low might merge distinct cell types. We will discuss these trade-offs and how to evaluate them in the context of your specific research question. This is not about writing code for you; it is about reviewing your approach, identifying potential pitfalls, and suggesting robust analytical strategies that you can confidently defend in a manuscript.
A typical review session covers the rationale behind data integration methods (like Harmony or Seurat integration anchors) and how to interpret complex visualizations like UMAPs and feature plots. The goal is to ensure that your analytical choices are biologically sound and technically correct.
Mastering HPC & Environment Setup
High-Performance Computing (HPC) environments are powerful but can be intimidating, especially when transitioning from a local laptop to a shared cluster. Each institution has its own unique configuration, scheduler (like Slurm or PBS), and module system. Setting up a reproducible environment in these settings is often the biggest hurdle for new computational biologists. This service provides targeted help to get your environment running smoothly so you can focus on the science.
We can cover everything from creating isolated Conda environments that do not conflict with system packages, to building Singularity containers for complex tools that are difficult to compile. We can also review your job submission scripts to ensure you are requesting appropriate resources (CPU, memory, and time) without wasting queue time or risking job failure due to memory limits.
If you are encountering cryptic error messages during installation or execution, we can systematically trace the cause. Often, issues stem from missing system libraries or path conflicts. By understanding how to read and interpret these logs, you will become more independent in managing your computational workspace in the future.
Structured 1-on-1 Mentoring
Transitioning into computational biology requires learning a completely new language and way of thinking. Online tutorials and documentation are valuable, but they often lack the interactive feedback needed to solidify understanding. 1-on-1 mentoring offers a tailored approach to your learning journey, focusing on the specific areas where you need the most guidance.
Mentoring sessions can cover a wide range of topics, depending on your current level and goals. For beginners, we might focus on command-line proficiency, basic scripting in Python or R, and understanding data formats (like FASTQ, BAM, and VCF). For more advanced learners, we might dive into statistical considerations for differential expression, workflow management with Snakemake, or the theoretical underpinnings of machine learning applications in genomics.
The structure of these sessions is flexible. We can review a piece of code you have written, walk through a complex concept step-by-step, or discuss the broader strategy for your thesis or project. The objective is to build your confidence and competence, transforming you from a tool-user into a tool-builder who understands the mechanics of the analyses you perform.
Disclaimer: The technical and learning support described here is intended for educational purposes. It does not constitute medical advice, clinical interpretation, or a guarantee of specific scientific results or publication acceptance.