BIOINFORMATICS ENGINEER

Complex data.
Clear analysis.

I’m Qiang, a bioinformatics engineer. I analyze sequencing data to understand gene regulation, cellular states and the biology behind the results.

Multi-omics · Single-cell · Spatial transcriptomics

My study notes connect sequencing principles with practical analysis: evaluating cell quality, comparing clustering choices, interpreting epigenetic signals and presenting results clearly.

Explore my skills Read my study notes
Biological questions
Gene regulation & cellular states
Working toolkit
R · Python · Shell

01 / SKILLS

Data analysis, from reads to results.

My work spans transcriptomics, epigenomics, single-cell and spatial analysis, supported by R, Python and Shell.

01

Multi-omics analysis

Analyze expression changes, chromatin signals and DNA methylation, then connect evidence across assays to investigate gene regulation in development and disease.

Analysis outputs Differential expression, epigenomic comparisons, functional enrichment and integrated interpretation.

  • RNA-seq
  • ATAC-seq
  • ChIP-seq
  • WGBS
  • DESeq2 / edgeR
  • MACS2 / Bismark
02

Single-cell & spatial

Assess data quality, identify cell populations and interpret marker expression. Explore trajectories and spatial patterns to connect cellular heterogeneity with tissue context.

Analysis outputs Cell annotations, marker profiles, trajectory views and spatial patterns.

  • Seurat
  • Scanpy
  • Signac
  • Stereopy
  • Visium / Stereo-seq
03

Statistics & visualization

Use R and Bioconductor for statistical analysis and figures, Python for data exploration and single-cell analysis, and Shell for sequencing data processing.

Analysis outputs Quality summaries, comparison plots and figures that connect statistical evidence to biological context.

  • R
  • Bioconductor
  • Python
  • Shell
CURRENTLY LEARNING

PyTorch, AI-driven drug discovery and virtual cell models — extending my analysis toolkit through continued learning.

02 / STUDY NOTES

A closer look at the analysis.

Bioinformatics Study Notes brings together sequencing methods, hands-on examples and the questions that come up while working with data.

BIOINFORMATICS STUDY NOTES

From methods to interpretation.

My collection is organized around three areas: bioinformatics concepts, computing foundations and applied NGS analysis. It includes method notes, R and Python scripts, and analysis notebooks.

The examples are part of an ongoing learning collection, with particular depth in single-cell analysis and spatial transcriptomics.

Explore the topics ↓
01 Bioinformatics
Sequencing technologies, transcriptomics, epigenomics, gene function and single-cell methods.
02 Computing
Statistics, data structures, SQL, Git, Docker and deep-learning study notes.
03 Data science for NGS
R and Python examples, Seurat and Scanpy notebooks, spatial analysis and figure preparation.
SINGLE-CELL

Quality, clusters & cell identity

Notes on doublet detection, normalization, integration and annotation, alongside code for comparing principal components and clustering resolutions.

  • Seurat / Scanpy
  • DoubletFinder
  • clustree
Inside the notes

The clustering-resolution example, scan_clustering_resolution.R, explores how clustering choices change the grouping of cells. Related notes cover quality filtering, doublet detection and annotation.

Learning example · parameter comparison

SPATIAL DATA

From counts to tissue context

Study notes covering Stereo-seq and Visium processing, image quality checks, Space Ranger and SAW, with downstream Seurat and Stereopy examples.

  • Visium / Visium HD
  • Stereo-seq
  • Stereopy
Inside the notes

Topics follow the path from platform-specific processing to downstream exploration: image quality, expression matrices and the tissue context of spatial patterns.

Method notes · spatial transcriptomics

EPIGENOMICS

Reading regulatory signals

Dedicated notes on DNA modification, histone marks, chromatin accessibility and chromosome conformation, supported by gene-function and motif-analysis topics.

  • DNA methylation
  • Chromatin
  • Motifs
Inside the notes

The collection groups chromatin accessibility, histone modifications and DNA methylation with gene-function and motif topics, connecting different ways of studying regulation.

Topic collection · gene regulation

These are study materials and analysis examples from an evolving personal collection.

03 / APPROACH

A clear path through the data.

I keep the biological question and the analysis decisions connected.

  1. 01

    Define

    Start with the biological question, sample groups and replicates. Define the comparison and the outputs that would make the result useful.

  2. 02

    Check

    Review sample metadata, quality summaries and reference compatibility. Make batch structure and potential confounders visible before interpreting differences.

  3. 03

    Analyze

    Choose methods that fit the question and data. Keep parameters and analysis decisions traceable, then connect statistical results with annotations and evidence across assays.

  4. 04

    Interpret

    Bring figures and biological context together. Distinguish associations from mechanisms, make limitations explicit and identify questions for follow-up.

MAKING RESULTS READABLE

Keep the reasoning with the result.

Data context
Sample definitions, quality observations and the comparison being made.
Analysis decisions
Methods, parameters and assumptions needed to understand the analysis.
Interpretation
Annotated figures, biological context and the limits of the evidence.

04 / PUBLICATIONS

Publications

Co-first-author publications.

Publication record on ORCID ↗

05 / CONTACT

Let’s talk data.

For data analysis, biological interpretation or technical exchange, start with the question, the data you have and what you want to understand.

  • The biological question or analysis challenge
  • The assay, sample groups and current data stage
  • The result or figure you want to discuss