synapsis
https://bioconductor.org/packages/synapsisSynapsis is a Bioconductor software package for automated (unbiased and reproducible) analysis of meiotic immunofluorescence datasets. The primary functions of the software can i) identify cells in meiotic prophase that are labelled by a synaptonemal complex axis or central element protein, ii) isolate individual synaptonemal complexes and measure their physical length, iii) quantify foci and co-localise them with synaptonemal complexes, iv) measure interference between synaptonemal complex-associated foci. The software has applications that extend to multiple species and to the analysis of other proteins that label meiotic prophase chromosomes. The software converts meiotic immunofluorescence images into R data frames that are compatible with machine learning methods. Given a set of microscopy images of meiotic spread slides, synapsis crops images around individual single cells, counts colocalising foci on strands on a per cell basis, and measures the distance between foci on any given strand.
Sourced from
- Bioconductor — synapsis
Related resources
Using single-cell RNA-Seq expression to visualize CNV in cells.
`muscat` provides various methods and visualization tools for DS analysis in multi-sample, multi-group, multi-(cell-)subpopulation scRNA-seq data, including cell-level mixed models and methods based on aggregated “pseudobulk” data, as well as a flexible simulation platform that mimics both single and multi-sample scRNA-seq data.
Splatter is a package for the simulation of single-cell RNA sequencing count data. It provides a simple interface for creating complex simulations that are reproducible and well-documented. Parameters can be estimated from real data and functions are provided for comparing real and simulated datasets.
Performs unbiased cell type recognition from single-cell RNA sequencing data, by leveraging reference transcriptomic datasets of pure cell types to infer the cell of origin of each single cell independently.
A tool for unsupervised clustering and analysis of single cell RNA-Seq data.
Fit linear models to overdispersed count data. The package can estimate the overdispersion and fit repeated models for matrix input. It is designed to handle large input datasets as they typically occur in single cell RNA-seq experiments.