Quentin Bayard

This paper benchmarks the Ultima UG100 and Illumina NovaSeq platforms on FFPE tissues for various sequencing modalities, finding they produce comparable results with subtle discrepancies in non-coding transcripts.

The paper introduces a machine-learning method to identify recurrent cell-cell communication patterns across tumor samples using spatial transcriptomics and single-cell data, revealing key immune and genetic programs in bladder cancer.

This paper introduces SpaMIL, a spatial transcriptomics-based multiple instance learning framework for patient-level outcome prediction in glioblastoma, achieving high prognostic accuracy.

The paper compares three transcriptomic methods on FFPE tumor samples to assess their ability to detect inter- and intra-patient heterogeneity.