Monocle 2 is a near-total re-write of the Monocle single-cell RNA-Seq analysis toolkit. It includes a number of new features:
- Dramatically better trajectory analysis. Monocle 2 is powered by DDRTree, a nonlinear manifold learning algorithm. Monocle 2’s trajectories are much more accurate and consistent than Monocle 1’s. You can also learn trajectories on experiments with thousands of cells.
- Extensive support for mRNA count analysis. Analyzing mRNA counts from experiments that use unique molecular identifiers (UMIs) or spike-in standards is typically far more accurate than experiments using read counts. Monocle 2’s functions have been re-written to take full advantage of mRNA count data. The package even includes an algorithm to convert relative expression values (e.g. FPKM) into mRNA counts without the use of spike-ins.
- New tools for analyzing branched trajectories. Monocle 2’s new trajectory reconstruction algorithm is far better at finding branches in single-cell processes. We now include an advanced statistical technique, BEAM, for identifying the genes affected by branches, along with some new visualization tools for tracking these changes.
- Cell classification and counting. Classifying cells by type based on their global expression profiles can reveal new cell types as well as help you track changes in cell frequencies in your experiment. Monocle 2 includes an easy to use system for classifying your cells either in an unsupervised manner or with the assistance of known cell-type specific markers.
This is just the first public release of Monocle 2 through GitHub. As we work towards distribution through Bioconductor, expect bug fixes and improvements in documentation. We also have some exciting new features in the pipeline as well as papers describing our new techniques that will hopefully appear over the coming months. You can check out a full list of features here.