New features, data, types of data and more
Immune cells called macrophages make up a large chunk of a glioblastoma tumor — and they help it grow, hide from treatment, and resist [...]
PyRadiomics addresses a core need in precision oncology: systematically extracting quantitative features from medical images. This open-source Python package pulls hundreds of radiomic features [...]
Bone metastasis is common across cancer types, but what's happening at the cellular level inside those lesions has been hard to map. This dataset uses [...]
Cumulus helps researchers tackle one of single-cell genomics' biggest challenges: analyzing datasets too large for a laptop. It's a cloud-based framework for large-scale scRNA-seq and [...]
Tumors aren't genetically uniform, and which clones survive and why they survive has direct bearing on treatment resistance. LinG3D adds spatial context to lineage tracing. [...]
A new single-cell RNA-sequencing dataset provides in-depth profiling of estrogen receptor (ER)-positive primary and metastatic breast cancer tumors from 23 patients, offering insight into [...]
Created and maintained by Cameron Davidson-Pilon, lifelines helps researchers answer “how long until something happens?” — whether that’s patient outcomes in cancer studies, subscriber [...]
We’re excited to share the dataset “Impact of Post-Lysis Stopping Point Prior to Reverse Transcription,” generated using the newly developed particle-templated instant partition sequencing [...]
phangorn is an R package for phylogenetic reconstruction and analysis. It supports Maximum Likelihood, Maximum Parsimony, distance-based methods, and Hadamard conjugation, enabling researchers to [...]
Understanding how genes behave within the structure of tissues is key to unlocking new biological insights. Current tools for spatial transcriptomics often look at [...]