semvec/pypi-packages huggingface dataset:
scmmd 0.1.0
Maximum mean discrepancy comparisons single cell profiles
License: Apache
Requires Python: >=3.6


There is a classical problem in statistics known as the **two-sample problem.**
In this setting, you are given discrete observations of two different distributions and asked to determine if the distributions are significantly different.
A special univariate case of this problem is familiar to many biologists as a "difference in means" comparison -- as performed using Student's *t*-test.



This problem becomes somewhat more complex in high-dimensions, as differences between distributions may manifest not only in the mean location, but also in the covariance structure and modality of the data.
One approach to comparing distributions in this setting leverages kernel similarity metrics to find the maximum mean discrepancy (Gretton et. al. 2012, *JMLR*) -- the largest difference in the means of the distributions under a flexible transformation.



Here, we adapt the MMD method to compare cell populations in single cell measurement data.
In the frame of the two-sample problem, each cell population of interest is considered as a distribution and each cell is a single observation from the source distribution.
We use the MMD to compute (1) a metric of the magnitude of difference between two cell populations, and (2) a p-value for the significance of this difference.
velodyn 0.1.0
Dynamical systems approaches for RNA velocity analysis
License: Apache
Requires Python: >=3.6


RNA velocity infers a rate of change for each transcript in an RNA-sequencing experiment based on the ratio of intronic to exonic reads. This inferred velocity vectors serves as a prediction for the future transcriptional state of a cell, while the current read counts serve as a measurement of the instantaneous state. Qualitative analysis of RNA velocity has been used to establish the order of gene expression states in a sequence, but quantitative analysis has generally been lacking.



velodyn adopts formalisms from dynamical systems to provide a quantitative framework for RNA velocity analysis. The tools provided by velodyn along with their associated usage are described below. All velodyn tools are designed to integrate with the scanpy ecosystem and anndata structures.



We have released velodyn in association with a recent pre-print. Please cite our pre-print if you find velodyn useful for your work.



Differentiation reveals the plasticity of age-related change in murine muscle progenitors

Jacob C Kimmel, David G Hendrickson, David R Kelley

bioRxiv 2020.03.05.979112; doi: https://doi.org/10.1101/2020.03.05.979112