Junyan Lu

From Fig. 1: Study overview

Fig.3a from the paper: Pathway connectivity network shown as a bipartite graph. Genes and perturbations are connected if the perturbation led to differential expression of that gene. The total number of DEGs for each treatment is depicted by the size of the central nodes. Colour and thickness of edges represent whether genes are induced or suppressed, and significance of drug effect, respectively.

"Cancer pathway connectivity resolved by drug
perturbation and RNA sequencing" presents a unique RNA-seq after perturbation dataset of B cell cancer samples from 116 patients, (almost) each perturbed with 10 small molecule compounds & control.

www.biorxiv.org/content/10.6...

07.01.2026 09:27 — 👍 10    🔁 5    💬 1    📌 0

Huge thanks to our collaborators —
Britta Velten (@brittavelten.bsky.social)
, the Klingmüller lab at DKFZ (@klingmuelab.bsky.social), and the Winter team at Thoraxklink Heidelberg
— for their contributions 🙏
We invite you to try out msBayesImpute and welcome any feedback or suggestions! 💬

07.10.2025 08:47 — 👍 2    🔁 0    💬 0    📌 1

msBayesImpute works for both small and large datasets, requires no parameter tuning, and is available in R and Python. A Shiny app will be available soon!
👉 Python version: github.com/Lu-Group-UKH...
👉 R version: github.com/Lu-Group-UKH...

07.10.2025 08:47 — 👍 1    🔁 0    💬 1    📌 0

Across synthetic, semi-synthetic, and experimental serial dilution datasets, msBayesImpute consistently:
✅ Achieved the lowest imputation error
✅ Improved sample-wise normalization
✅ Delivered the highest accuracy in DE analysis across 9 methods

07.10.2025 08:47 — 👍 0    🔁 0    💬 1    📌 0

Missing values in proteomics are often not missing at random (MNAR). Existing methods either assume MAR or oversimplify MNAR.
💡 msBayesImpute learns protein-specific dropout curves directly from the data using Bayesian matrix factorization + probabilistic dropout models.

07.10.2025 08:47 — 👍 2    🔁 0    💬 1    📌 0

🚀 Excited to share our new preprint: msBayesImpute - A Versatile Framework for Addressing Missing Values in Biomedical Mass Spectrometry Proteomics Data
👉 Improves imputation accuracy, normalization, and differential expression detection
📝https://www.biorxiv.org/content/10.1101/2025.10.02.679746v1

07.10.2025 08:47 — 👍 16    🔁 2    💬 1    📌 1

Postdoc / Bioinformatician – Computational Multi-Omics for Precision Oncology (m/f/d) Pflichtfelder sind mit einem (*) markiert.

🚀 Postdoc / Bioinformatician in Computational Multi-Omics for Precision Oncology at Heidelberg University Hospital: karriere.klinikum.uni-heidelberg.de/index.php?ac...
A great short-term opportunity for anyone looking for a bridge position between PhD, postdoc, or the next career step.

01.10.2025 13:24 — 👍 6    🔁 2    💬 0    📌 0

Application - Helmholtz Information & Data Science Academy

We're seeking a PhD student passionate about ML, biology, and driving impact in healthcare!

You'll be working on multi-omics factorization models for personalized medicine, co-supervised by @junyanlu.bsky.social.

👉 Project: www.helmholtz.de/assets/hidss...
✍️ Apply: hidss4health.de/application

05.08.2025 08:08 — 👍 6    🔁 4    💬 0    📌 0

PhD Student Bioinformatics, Computational Biology (m/f/d) Analyze bulk and single-cell multi-omics data generated from in vivo AML models Curate and integrate public datasets (eg scRNA-seq) to derive prior knowledge on AML dormancy Develop and apply integrat...

🔬 PhD Position in Bioinformatics!
Join my group at Heidelberg University (lu-group-ukhd.github.io) to study AML dormancy & therapy resistance using multi-omics & single-cell data.
👉Apply or share: www.jobvector.de/job/bioinfor...

22.07.2025 08:03 — 👍 4    🔁 3    💬 0    📌 0