Biology subjects

Bhaskara, A.

Publications and source records attributed to Bhaskara, A..

3 recordsLinked to original sources

bioRxiv2021

Factorization-based Imputation of Expression in Single-cell Transcriptomic Analysis (FIESTA) recovers Gene-Cell-State Relationships

Single cell RNA sequencing (scRNA-seq) is a gene expression profiling technique that is presently revolutionizing the study of complex cellular systems in the biological sciences. Existing scRNA-seq methods suffer from sub-optimal target recovery leading to inaccurate measurements including many false negatives. The resulting zero-inflated data may confound data interpretation and visualization. Since cells have coherent phenotypes defined by conserved molecular circuitries (i.e. multiple gene products working together) and since similar cells utilize similar circuits, information about each expression value or node in a multi-cell, multi-gene scRNA-seq data set is expected to also be predictable from other nodes in the data set. Based on this logic, several approaches have been proposed to impute missing values in a data set by extracting information from its non-zero measurements. In this study, we apply non-negative matrix factorization to a selection of published scRNA-seq data sets followed by multiplication of the factor matrices to generate idealized completed model versions of the data. From the model matrices, we recommend new values where original measurements are likely to be inaccurate and where zero measurements are predicted to be false negatives. The resulting imputed data model predicts novel type markers and expression patterns that match orthogonal measurements and field literature better than those obtained from pre-imputation data or alternative imputation strategies. Contactbenjamin.spike@hci.utah.edu Availability and implementationFIESTA is written in R and is available at https://github.com/elnazmirzaei/FIESTA and https://github.com/TheSpikeLab/FIESTA. Author summaryIn this work, we develop FIESTA, a novel, unsupervised, mathematical approach to impute missing values in scRNA-seq data. For each dataset, we use parts-based, non-negative matrix factorization to break the cells-by-genes expression matrix into optimized component matrices and then multiply these component matrices to generate an idealized, completed matrix. The completed matrix has many of the null values filled in because the optimized low rank factors from which it is generated, take multiple cells into account when estimating a particular component, including some cells with positive expression values for genes which are false negatives in other related cells. We also implement scaling and thresholding approaches based on intrinsic data topology for improved interpretability and graphical representation. Overall, FIESTA performs favorably relative to alternative imputation approaches and uncovers gene-gene and gene-cell relationships that are occluded in the raw data. The FIESTA computational pipeline is freely available for download and use by other researchers analyzing scRNA-seq data or other sparse data sets.

bioinformatics↗

bioRxiv2019

The ROP16III-dependent early immune response determines the sub-acute CNS immune response and type III Toxoplasma gondii survival

Toxoplasma gondii is an intracellular parasite that persistently infects the CNS and that has genetically distinct strains which provoke different acute immune responses. How differences in the acute immune response affect the CNS immune response is unknown. To address this question, we used two persistent Toxoplasma strains (type II and type III) and examined the CNS immune response at 21 days post infection (dpi). Contrary to acute infection studies, type III-infected mice had higher numbers of total CNS T cells and macrophages/microglia but fewer alternatively activated macrophages (M2s) and regulatory T cells (Tregs) than type II-infected mice. By profiling splenocytes at 5, 10 and 21 dpi, we determined that at 5 dpi type III-infected mice had more M2s while type II-infected mice had more classically activated macrophages (M1s) and these responses flipped over time. To test how these early differences influence the CNS immune response, we engineered the type III strain to lack ROP16 (III{Delta}rop16), the polymorphic effector protein that drives the type III-associated M2 response. III{Delta}rop16-infected mice showed a type II-like neuroinflammatory response with fewer infiltrating T cells and macrophages/microglia and more M2s and an unexpectedly low CNS parasite burden. At 5 dpi, III{Delta}rop16-infected mice showed a mixed inflammatory response with more M1s, M2s, T effector cells, and Tregs, and decreased rates of infection of peritoneal exudative cells (PECs). These data suggested that type III parasites need the early ROP16-associated M2 response to avoid clearance, possibly by the Immunity-Related GTPases (IRGs), IFN-{gamma} dependent proteins essential for murine defenses against Toxoplasma. To test this possibility, we infected IRG-deficient mice and found that III{Delta}rop16 parasites now maintained parental levels of PECs infection. Collectively, these studies suggest that, for the type III strain, rop16III plays a key role in parasite persistence and influences the sub-acute CNS immune response.\n\nAuthor SummaryToxoplasma is a ubiquitous intracellular parasite that establishes an asymptomatic brain infection in immunocompetent individuals. However, in the immunocompromised and the developing fetus, Toxoplasma can cause problems ranging from fever to chorioretinitis to severe toxoplasmic encephalitis. Emerging evidence suggests that the genotype of the infecting Toxoplasma strain may influence these outcomes, possibly through the secretion of Toxoplasma strain-specific polymorphic effector proteins that trigger different host cell signaling pathways. While such strain-specific modulation of host cell signaling has been shown to affect acute immune responses, it is unclear how these differences influence the sub-acute or chronic responses in the CNS, the major organ affected in symptomatic disease. This study shows that genetically distinct strains of Toxoplasma provoke strain-specific CNS immune responses and that, for one strain (type III), the acute and sub-acute immune responses and parasite survival are heavily influenced by a polymorphic parasite gene (rop16III).

microbiology↗

bioRxiv2018

A graph-based algorithm for RNA-seq data normalization

The use of RNA-sequencing has garnered much attention in the recent years for characterizing and understanding various biological systems. However, it remains a major challenge to gain insights from a large number of RNA-seq experiments collectively, due to the normalization problem. Current normalization methods are based on assumptions that fail to hold when RNA-seq profiles become more abundant and heterogeneous. We present a normalization procedure that does not rely on these assumptions, or on prior knowledge about the reference transcripts in those conditions. This algorithm is based on a graph constructed from intrinsic correlations among RNA-seq transcripts and seeks to identify a set of densely connected vertices as references. Application of this algorithm on our benchmark data showed that it can recover the reference transcripts with high precision, thus resulting in high-quality normalization. As demonstrated on a real data set, this algorithm gives good results and is efficient enough to be applicable to real-life data.\n\n2012 ACM Subject ClassificationApplied computing [->] Computational transcriptomics, Applied computing [->] Bioinformatics\n\nDigital Object Identifier10.4230/LIPIcs.WABI.2018.xxx\n\nFundingThis material was based on research supported by the National Heart, Lung, and Blood Institute (NHLBI)-NIH sponsored Programs of Excellence in Glycosciences [grant number HL107152 to B.K.], and partially by NSF [CAREER grant 1350344 to M.M.]. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon.

bioinformatics↗