Separability (a Kronecker product) of a scatter matrix is one of favorable structures when multivariate heavy-tailed data are collected in a matrix form, due to its parsimonious representation. ...However, little attempt has been made to test separability beyond Gaussianity. In this paper, we present nonparametric separability tests that can be applied to a larger class of multivariate distributions not only including elliptical distributions but also generalized elliptical distributions and transelliptical distributions. The proposed test statistic exploits robustness of Tyler's M (or Kendall's tau) estimator and a likelihood function of a scaled variable. Since its distribution is hard to specify, we approximate the p-value using a permutation procedure, whose unbiasedness is obtained from the permutation invariance of multivariate paired data. Our simulation study demonstrates the efficacy of our method against other alternatives, and we apply it to rhesus monkey data and corpus callosum data.
In this paper, we derive a new version of Hanson–Wright inequality for a sparse bilinear form of sub‐Gaussian variables. Our results are a generalization of previous deviation inequalities that ...consider either sparse quadratic forms or dense bilinear forms. We apply the new concentration inequality to testing the cross‐covariance matrix when data are subject to missing. Using our results, we can find a threshold value of correlations that controls the family‐wise error rate. Furthermore, we discuss the multiplicative measurement error case for the bilinear form with a boundedness condition.
We consider the clustering of repeatedly measured 'min-max' type interval-valued data. We read the data as matrix variate data and assume the covariance matrix is separable for the model-based ...clustering (M-clustering). The use of a separable covariance matrix introduces several advantages in M-clustering, which include fewer samples required for a valid procedure. In addition, the numerical study shows that this structured matrix allows us to find the correct number of clusters more accurately compared to other commonly assumed covariance matrices. We apply the M-clustering with various covariance structures to clustering the longitudinal blood pressure data from the National Heart, Lung, and Blood Institute Growth and Health Study (NGHS).
Single-cell lineage tracing provides crucial insights into the fates of individual cells. Single-cell RNA sequencing (scRNA-seq) is commonly applied in modern biomedical research, but genetics-based ...lineage tracing for scRNA-seq data is still unexplored. Variant calling from scRNA-seq data uniquely suffers from “expressional drop-outs,” including low expression and allelic bias in gene expression, which presents significant obstacles for lineage reconstruction. We introduce SClineager, which infers accurate evolutionary lineages from scRNA-seq data by borrowing information from related cells to overcome expressional drop-outs. We systematically validate SClineager and show that genetics-based lineage tracing is applicable for single-cell-sequencing studies of both tumor and non-tumor tissues using SClineager. Overall, our work provides a powerful tool that can be applied to scRNA-seq data to decipher the lineage histories of cells and that could address a missing opportunity to reveal valuable information from the large amounts of existing scRNA-seq data.
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•Variant calling from scRNA-seq data uniquely suffers from “expressional drop-outs”•SClineager overcomes expressional drop-outs in variant-based single-cell lineage tracing•SClineager is capable of inter- and intra-individual single-cell lineage tracing•Discovery of genetic shifting and variant accumulation in tumor and non-tumor tissues
Variant calling from single-cell sequencing (scRNA-seq) data uniquely suffers from “expressional drop-outs,” including low expression and allelic bias in expression. Lu et al. introduce SClineager, which infers accurate evolutionary lineages for tumor and non-tumor single cells from scRNA-seq data by borrowing information across cells to overcome expressional drop-outs.
Neoantigens are the key targets of antitumor immune responses from cytotoxic T cells and play a critical role in affecting tumor progressions and immunotherapy treatment responses. However, little is ...known about how the interaction between neoantigens and T cells ultimately affects the evolution of cancerous masses. Here, we develop a hierarchical Bayesian model, named neoantigen-T cell interaction estimation (netie) to infer the history of neoantigen-CD8
T cell interactions in tumors. Netie was systematically validated and applied to examine the molecular patterns of 3,219 tumors, compiled from a panel of 18 cancer types. We showed that tumors with an increase in immune selection pressure over time are associated with T cells that have an activation-related expression signature. We also identified a subset of exhausted cytotoxic T cells postimmunotherapy associated with tumor clones that newly arise after treatment. These analyses demonstrate how netie enables the interrogation of the relationship between individual neoantigen repertoires and the tumor molecular profiles. We found that a T cell inflammation gene expression profile (TIGEP) is more predictive of patient outcomes in the tumors with an increase in immune pressure over time, which reveals a curious synergy between T cells and neoantigen distributions. Overall, we provide a new tool that is capable of revealing the imprints left by neoantigens during each tumor's developmental process and of predicting how tumors will progress under further pressure of the host's immune system.
The endoplasmic reticulum (ER) and mitochondria form a unique subcellular compartment called mitochondria-associated ER membranes (MAMs). Disruption of MAMs impairs Ca
homeostasis, triggering ...pleiotropic effects in the neuronal system. Genome-wide kinase-MAM interactome screening identifies casein kinase 2 alpha 1 (CK2A1) as a regulator of composition and Ca
transport of MAMs. CK2A1-mediated phosphorylation of PACS2 at Ser207/208/213 facilitates MAM localization of the CK2A1-PACS2-PKD2 complex, regulating PKD2-dependent mitochondrial Ca
influx. We further reveal that mutations of PACS2 (E209K and E211K) associated with developmental and epileptic encephalopathy-66 (DEE66) impair MAM integrity through the disturbance of PACS2 phosphorylation at Ser207/208/213. This, in turn, causes the reduction of mitochondrial Ca
uptake and the dramatic increase of the cytosolic Ca
level, thereby, inducing neurotransmitter release at the axon boutons of glutamatergic neurons. In conclusion, our findings suggest a molecular mechanism that MAM alterations induced by pathological PACS2 mutations modulate Ca
-dependent neurotransmitter release.
Separability is an attractive feature of covariance matrices or matrix variate data, which can improve and simplify many multivariate procedures. Due to its importance, testing separability has ...attracted much attention in the past. The procedures in the literature are of two types, likelihood ratio test (LRT) and Rao’s score test (RST). Both are based on the normality assumption or the large-sample asymptotic properties of the test statistics. In this paper, we develop a new approach that is very different from existing ones. We propose to reformulate the null hypothesis (the separability of a covariance matrix of interest) into many sub-hypotheses (the separability of the sub-matrices of the covariance matrix), which are testable using a permutation based procedure. We then combine the testing results of sub-hypotheses using the Bonferroni and two-stage additive procedures. Our permutation based procedures are inherently distribution free; thus it is robust to non-normality of the data. In addition, unlike the LRT, they are applicable to situations when the sample size is smaller than the number of unknown parameters in the covariance matrix. Our numerical study and data examples show the advantages of our procedures over the existing LRT and RST.
The clinical utility of blood transcriptomic biosignatures for the treatment monitoring and outcome prediction of tuberculosis (TB) remains limited. In this study, we aimed to discover and validate ...biomarkers for pulmonary TB treatment monitoring and outcome prediction based on kinetic responses of gene expression during treatment. In particular, differentially expressed genes (DEGs) were identified by time-series comparison. Subsequently, DEGs with the monotonic expression alterations during the treatment were selected. Ten consistently down-regulated genes (CD274, KIF1B, IL15, TLR1, TLR5, FCGR1A, GBP1, NOD2, GBP2, EGF) exhibited significant potential in treatment monitoring, demonstrated via biological and technical validation. Additionally, the biosignature showed potential in predicting the cured versus relapsed patients. Furthermore, the biosignature could be utilized for TB diagnosis, latent tuberculosis infection/active TB differential diagnosis, and risk of progression to active TB. Benchmarking analysis of the 10-gene biosignature with other biosignatures showed equivalent performance in tested data sets. In conclusion, we established a 10-gene transcriptomic biosignature that represents the kinetic responses of TB treatment. Subsequent studies are warranted to validate, refine and translate the biosignature into a precise assay to assist clinical decisions in a broad spectrum of TB management.
•A more robust transcriptomic signature could enhance tuberculosis management.•Novel biomarkers were discovered using the kinetic response of host transcriptome.•The knowledge-based selection led to a signature with better performance.•A 10-gene signature could be used for treatment monitoring and outcome prediction.•The signature could also be applied for tuberculosis diagnosis and risk assessment.
The endoplasmic reticulum (ER) and mitochondria form a unique subcellular compartment called mitochondria-associated ER membranes (MAMs). Disruption of MAMs impairs Ca2+ homeostasis, triggering ...pleiotropic effects in the neuronal system. Genome-wide kinase-MAM interactome screening identifies casein kinase 2 alpha 1 (CK2A1) as a regulator of composition and Ca2+ transport of MAMs. CK2A1-mediated phosphorylation of PACS2 at Ser207/208/213 facilitates MAM localization of the CK2A1–PACS2–PKD2 complex, regulating PKD2-dependent mitochondrial Ca2+ influx. We further reveal that mutations of PACS2 (E209K and E211K) associated with developmental and epileptic encephalopathy-66 (DEE66) impair MAM integrity through the disturbance of PACS2 phosphorylation at Ser207/208/213. This, in turn, causes the reduction of mitochondrial Ca2+ uptake and the dramatic increase of the cytosolic Ca2+ level, thereby, inducing neurotransmitter release at the axon boutons of glutamatergic neurons. In conclusion, our findings suggest a molecular mechanism that MAM alterations induced by pathological PACS2 mutations modulate Ca2+-dependent neurotransmitter release.
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