SSCC: A Novel Computational Framework for Rapid and Accurate Clustering Large-scale Single Cell RNA-seq Data

Clustering is a prevalent analytical means to analyze single cell RNA sequencing (scRNA-seq) data but the rapidly expanding data volume can make this process computationally challenging. New methods for both accurate and efficient clustering are of pressing need. Here we proposed Spearman subsampling-clustering-classification (SSCC),a new clustering framework based on random projection and feature construction,for large-scale scRNA-seq data. SSCC greatly improves clustering accuracy,robustness,and computational efficacy for various state-of-the-art algorithms benchmarked on multiple real datasets. On a dataset with 68,578 human blood cells,SSCC achieved 20%improvement for clustering accuracy and 50-fold acceleration,but only consumed 66%memory usage,compared to the widelyused software package SC3. Compared to k-means,the accuracy improvement of SSCC can reach 3-fold. An R implementation of SSCC is available at https://github.com/Japrin/sscClust.

Transport properties of p-type CaMg_(2)Bi_(2) thermoelectrics

Due to the complex crystal structure for low lattice thermal conductivity and the tunable valence bands for superior electronic performance,CaAl_(2)Si_(2)-structured AB_(2)C_(2) Zintl compounds have been frequently proven as promising p-type thermoelectric materials.In this work,thermoelectric properties of CaMg_(2)Bi_(2) are systematically investigated in a broad carrier concentration(10^(18)-10^(20) cm^(-3))through Agdoping for comprehensively evaluating its potential for thermoelectric applications.The broad carrier concentration enables a well assessment of the carrier transport properties by single parabolic band with acoustic phonon scattering and a revelation of the carrier transport by multiple valence orbitals when the carrier concentration higher than ~2×10^(19) cm^(-3),leading to a significant enhancement in electronic performance.With the help of additional point defect phonon scattering introduced by BaMg_(2)Bi_(2)-alloying,a reduction in lattice thermal conductivity in the entire temperature range and the lowest one of ~0.7 W/m-K are achieved,leading to a 100% enhancement in average zTave.in addition to the contribution of a multiband transport.This work not only demonstrates CaMg_(2)Bi_(2) as a promising thermoelectric material,but also provides a well understanding of its underlying material physics.

Promising cubic MnGeTe_2 thermoelectrics

Semiconducting cubic group IV monotellurides,including PbTe and SnTe, have historically led most of the advancements in thermoelectrics. Recently, noncubic ones such as GeTe and MnTe have also shown to be promising,which motivates the current work focusing on the thermoelectric properties of MnGeTe2, a derivative compound of noncubic GeTe and MnTe but crystalizing in a cubic structure.This compound intrinsically comes with a carrier concentration as high as ~3.6×1021 cm-3, indicating the existence of highconcentration cation vacancies due to Ge-precipitation. This intrinsic carrier concentration is much higher than that needed for thermoelectric applications but can be successfully decreased to ~9×1020 cm-3 for MnGe0.9Bi0.1Te2 at room temperature. Such a broad carrier concentration not only offers a full assessment of its electronic transport properties according to a single parabolic band model with acoustic scattering, but also enables an optimization for thermoelectric power factor.The low lattice thermal conductivity of ~1.2 W m-1 K-1 or lower in the entire temperature range, can be understood by the highly disordered cations and cation vacancies. A peak zT approaching 1.0 at 850 K was achieved in materials at an optimal carrier concentration of ~9×1020 cm-3, an isotropic cubic structure as well as a Vickers hardness of >200 HV, strongly indicating MnGeTe2 as a promising thermoelectric material.