Decoding the Spermatogenesis Program: New Insights from Transcriptomic Analyses.

Spermatogenesis is a complex differentiation process coordinated spatiotemporally across and along seminiferous tubules. Cellular heterogeneity has made it challenging to obtain stage-specific molecular profiles of germ and somatic cells using bulk transcriptomic analyses. This has limited our ability to understand regulation of spermatogenesis and to integrate knowledge from model organisms to humans. The recent advancement of single-cell RNA-sequencing (scRNA-seq) technologies provides insights into the cell type diversity and molecular signatures in the testis. Fine-grained cell atlases of the testis contain both known and novel cell types and define the functional states along the germ cell developmental trajectory in many species. These atlases provide a reference system for integrated interspecies comparisons to discover mechanistic parallels and to enable future studies. Despite recent advances, we currently lack high-resolution data to probe germ cell-somatic cell interactions in the tissue environment, but the use of highly multiplexed spatial analysis technologies has begun to resolve this problem. Taken together, recent single-cell studies provide an improvedunderstanding of gametogenesis to examine underlying causes of infertility and enable the development of new therapeutic interventions.

A rank-based marker selection method for high throughput scRNA-seq data.

BACKGROUND: High throughput microfluidic protocols in single cell RNA sequencing (scRNA-seq) collect mRNA counts from up to one million individual cells in a single experiment; this enables high resolution studies of rare cell types and cell development pathways. Determining small sets of genetic markers that can identify specific cell populations is thus one of the major objectives of computational analysis of mRNA counts data. Many tools have been developed for marker selection on single cell data; most of them, however, are based on complex statistical models and handle the multi-class case in an ad-hoc manner. RESULTS: We introduce RANKCORR, a fast method with strong mathematical underpinnings that performs multi-class marker selection in an informed manner. RANKCORR proceeds by ranking the mRNA counts data before linearly separating the ranked data using a small number of genes. The step of ranking is intuitively natural for scRNA-seq data and provides a non-parametric method for analyzing count data. In addition, we present several performance measures for evaluating the quality of a set of markers when there is no known ground truth. Using these metrics, we compare the performance of RANKCORR to a variety of other marker selection methods on an assortment of experimental and synthetic data sets that range in size from several thousand to one million cells. CONCLUSIONS: According to the metrics introduced in this work, RANKCORR is consistently one of most optimal marker selection methods on scRNA-seq data. Most methods show similar overall performance, however; thus, the speed of the algorithm is the most important consideration for large data sets (and comparing the markers selected by several methods can be fruitful). RANKCORR is fast enough to easily handle the largest data sets and, as such, it is a useful tool to add into computational pipelines when dealing with high throughput scRNA-seq data. RANKCORR software is available for download at https://github.com/ahsv/RankCorr with extensive documentation.

Oversimplifying quantum factoring.

Shor's quantum factoring algorithm exponentially outperforms known classical methods. Previous experimental implementations have used simplifications dependent on knowing the factors in advance. However, as we show here, all composite numbers admit simplification of the algorithm to a circuit equivalent to flipping coins. The difficulty of a particular experiment therefore depends on the level of simplification chosen, not the size of the number factored. Valid implementations should not make use of the answer sought.