Vibrational spectroscopy techniques, such as Fourier-transform infrared (FTIR) and Raman spectroscopy, are becoming widely used for biological applications. The use of machine learning algorithms are critical towards extracting important information and visualisation in a readily interpretable form.
We describe SCCAF a computational approach to identify putative cell clusters from single-cell RNA-seq data. SCCAF automatically identifies the “ground truth” cell assignments with high accuracy in various benchmark datasets and captures the discriminative feature genes of the cell types.
We modified the commercially available microraft array technology with standard confocal imaging techniques to perform an imaging-based CRISPR screen for regulators of a protein localization phenotype.
We describe a kethoxal-assisted single-stranded DNA sequencing (KAS-seq) approach. KAS-seq allows rapid (within 5 min), sensitive and genome-wide capture and mapping of ssDNA produced by transcriptionally active RNA polymerases or other processes in situ using as few as 1,000 cells.
Oligomerisation of membrane proteins remains a field characterized by intense research interest, due to the large number of pharmacological and clinical implications that the formation of molecular complexes of signaling proteins carries.
We present Spatial PAttern Recognition via Kernels (SPARK) as an effective statistical tool to identify genes with spatial expression patterns in spatially resolved transcriptomic studies. A particular feature of SPARK is its ability to produce calibrated p-values for spatial analysis.