New features, data, types of data and more
More than 10 million cancer mutations in TCGA have no known functional effect. Rather than characterizing each one experimentally, this dataset uses transcription factor [...]
One of the persistent problems in AML is that leukemia cells don't all die from chemotherapy; some survive, go quiet, and eventually drive relapse. [...]
CELESTA addresses a core need in spatial biology and cancer research: assigning cell types in multiplexed in situ imaging data without requiring labeled training [...]
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 [...]