The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform

Angshul Majumdar - One of the best experts on this subject based on the ideXlab platform.

  • mcimpute matrix completion based imputation for single Cell rna seq data
    Frontiers in Genetics, 2019
    Co-Authors: Aanchal Mongia, Debarka Sengupta, Angshul Majumdar
    Abstract:

    Motivation: Single-Cell RNA sequencing has been proved to be revolutionary for its potential of zooming into complex biological systems. Genome-wide expression Analysis at single-Cell resolution provides a window into dynamics of Cellular phenotypes. This facilitates the characterization of transcriptional heterogeneity in normal and diseased tissues under various conditions. It also sheds light on the development or emergence of specific Cell populations and phenotypes. However, owing to the paucity of input RNA, a typical single Cell RNA sequencing data features a high number of dropout events where transcripts fail to get amplified. Results: We introduce mcImpute, a low-rank matrix completion based technique to impute dropouts in single Cell expression data. On a number of real datasets, application of mcImpute yields significant improvements in the separation of true zeros from dropouts, Cell-clustering, differential expression Analysis, Cell type separability, the performance of dimensionality reduction techniques for Cell visualization, and gene distribution. Availability and Implementation: https://github.com/aanchalMongia/McImpute_scRNAseq.

  • mcimpute matrix completion based imputation for single Cell rna seq data
    bioRxiv, 2018
    Co-Authors: Aanchal Mongia, Debarka Sengupta, Angshul Majumdar
    Abstract:

    Motivation: Single Cell RNA sequencing has been proved to be revolutionary for its potential of zooming into complex biological systems. Genome wide expression Analysis at single Cell resolution, provides a window into dynamics of Cellular phenotypes. This facilitates characterization of transcriptional heterogeneity in normal and diseased tissues under various conditions. It also sheds light on development or emergence of specific Cell populations and phenotypes. However, owing to the paucity of input RNA, a typical single Cell RNA sequencing data features a high number of dropout events where transcripts fail to get amplified. Results: We introduce mcImpute, a low-rank matrix completion based technique to impute dropouts in single Cell expression data. On a number of real datasets, application of mcImpute yields significant improvements in separation of true zeros from dropouts, Cell-clustering, differential expression Analysis, Cell type separability and performance of dimensionality reduction techniques for Cell visualization. On a large single Cell data containing~sim 68k transcriptomes, mcImpute alone was able to produce the imputed matrix. Availability and Implementation: https://github.com/aanchalMongia/McImpute_scRNAseq

Aanchal Mongia - One of the best experts on this subject based on the ideXlab platform.

  • mcimpute matrix completion based imputation for single Cell rna seq data
    Frontiers in Genetics, 2019
    Co-Authors: Aanchal Mongia, Debarka Sengupta, Angshul Majumdar
    Abstract:

    Motivation: Single-Cell RNA sequencing has been proved to be revolutionary for its potential of zooming into complex biological systems. Genome-wide expression Analysis at single-Cell resolution provides a window into dynamics of Cellular phenotypes. This facilitates the characterization of transcriptional heterogeneity in normal and diseased tissues under various conditions. It also sheds light on the development or emergence of specific Cell populations and phenotypes. However, owing to the paucity of input RNA, a typical single Cell RNA sequencing data features a high number of dropout events where transcripts fail to get amplified. Results: We introduce mcImpute, a low-rank matrix completion based technique to impute dropouts in single Cell expression data. On a number of real datasets, application of mcImpute yields significant improvements in the separation of true zeros from dropouts, Cell-clustering, differential expression Analysis, Cell type separability, the performance of dimensionality reduction techniques for Cell visualization, and gene distribution. Availability and Implementation: https://github.com/aanchalMongia/McImpute_scRNAseq.

  • mcimpute matrix completion based imputation for single Cell rna seq data
    bioRxiv, 2018
    Co-Authors: Aanchal Mongia, Debarka Sengupta, Angshul Majumdar
    Abstract:

    Motivation: Single Cell RNA sequencing has been proved to be revolutionary for its potential of zooming into complex biological systems. Genome wide expression Analysis at single Cell resolution, provides a window into dynamics of Cellular phenotypes. This facilitates characterization of transcriptional heterogeneity in normal and diseased tissues under various conditions. It also sheds light on development or emergence of specific Cell populations and phenotypes. However, owing to the paucity of input RNA, a typical single Cell RNA sequencing data features a high number of dropout events where transcripts fail to get amplified. Results: We introduce mcImpute, a low-rank matrix completion based technique to impute dropouts in single Cell expression data. On a number of real datasets, application of mcImpute yields significant improvements in separation of true zeros from dropouts, Cell-clustering, differential expression Analysis, Cell type separability and performance of dimensionality reduction techniques for Cell visualization. On a large single Cell data containing~sim 68k transcriptomes, mcImpute alone was able to produce the imputed matrix. Availability and Implementation: https://github.com/aanchalMongia/McImpute_scRNAseq

Zhengzhi Zou - One of the best experts on this subject based on the ideXlab platform.

  • synergistic induction of erlotinib mediated apoptosis by resveratrol in human non small Cell lung cancer Cells by down regulating survivin and up regulating puma
    Cellular Physiology and Biochemistry, 2015
    Co-Authors: Peipei Nie, Tao Zhang, Yiju Yang, Benxin Hou, Zhengzhi Zou
    Abstract:

    Background/Aim: Treatment of human non–small-Cell lung cancer (NSCLC) often involves uses of multiple therapeutic strategies with different mechanisms of action. Here we found that resveratrol (RV) enhanced the anti-tumor effects of epidermal growth factor receptor (EGFR) inhibitor erlotinib in NSCLC Cells. Methods: Cell viability was measured by MTT assay and clonogenicity assay. Western blot was applied to assess the protein expression levels of target genes. Cell apoptosis was monitored by AnnexinV-FITC assay and sub-G1 population assay. IntraCellular ROS were measured by flow cytometric Analysis. Cell caspase activities were carried out by fluorometric assays. Results: Exposure of H460, A549, PC-9 and H1975 Cells to minimal or non-toxic concentrations of RV and erlotinib synergistically reduced Cell viability, colony formation and induced Cell apoptosis. Furthermore, RV synergistically enhanced erlotinib-induced apoptosis was involved in ROS production. Additionally, cotreatment with RV and erlotinib repressed the expressions of anti-apoptosis proteins, such as survivin and Mcl-1, whereas promoted p53 and PUMA expression and caspase 3 activity. Moreover, the combination was also more effective at inhibiting the AKT/mTOR/S6 kinase pathway. Subsequently, small interfering RNA (siRNA) depletion of PUMA and overexpression of survivin significantly attenuated NSCLC Cells apoptosis induced by the combination of the two drugs. Conclusion: Our findings suggested that RV synergistically enhanced the antitumor effects of erlotinib in NSCLC Cells were involved in decrease of survivin expression and

Debarka Sengupta - One of the best experts on this subject based on the ideXlab platform.

  • mcimpute matrix completion based imputation for single Cell rna seq data
    Frontiers in Genetics, 2019
    Co-Authors: Aanchal Mongia, Debarka Sengupta, Angshul Majumdar
    Abstract:

    Motivation: Single-Cell RNA sequencing has been proved to be revolutionary for its potential of zooming into complex biological systems. Genome-wide expression Analysis at single-Cell resolution provides a window into dynamics of Cellular phenotypes. This facilitates the characterization of transcriptional heterogeneity in normal and diseased tissues under various conditions. It also sheds light on the development or emergence of specific Cell populations and phenotypes. However, owing to the paucity of input RNA, a typical single Cell RNA sequencing data features a high number of dropout events where transcripts fail to get amplified. Results: We introduce mcImpute, a low-rank matrix completion based technique to impute dropouts in single Cell expression data. On a number of real datasets, application of mcImpute yields significant improvements in the separation of true zeros from dropouts, Cell-clustering, differential expression Analysis, Cell type separability, the performance of dimensionality reduction techniques for Cell visualization, and gene distribution. Availability and Implementation: https://github.com/aanchalMongia/McImpute_scRNAseq.

  • mcimpute matrix completion based imputation for single Cell rna seq data
    bioRxiv, 2018
    Co-Authors: Aanchal Mongia, Debarka Sengupta, Angshul Majumdar
    Abstract:

    Motivation: Single Cell RNA sequencing has been proved to be revolutionary for its potential of zooming into complex biological systems. Genome wide expression Analysis at single Cell resolution, provides a window into dynamics of Cellular phenotypes. This facilitates characterization of transcriptional heterogeneity in normal and diseased tissues under various conditions. It also sheds light on development or emergence of specific Cell populations and phenotypes. However, owing to the paucity of input RNA, a typical single Cell RNA sequencing data features a high number of dropout events where transcripts fail to get amplified. Results: We introduce mcImpute, a low-rank matrix completion based technique to impute dropouts in single Cell expression data. On a number of real datasets, application of mcImpute yields significant improvements in separation of true zeros from dropouts, Cell-clustering, differential expression Analysis, Cell type separability and performance of dimensionality reduction techniques for Cell visualization. On a large single Cell data containing~sim 68k transcriptomes, mcImpute alone was able to produce the imputed matrix. Availability and Implementation: https://github.com/aanchalMongia/McImpute_scRNAseq

Chorok Jung - One of the best experts on this subject based on the ideXlab platform.

  • setdb1 regulates smad7 expression for breast cancer metastasis
    Journal of Biochemistry and Molecular Biology, 2019
    Co-Authors: Junghwa Oh, Chorok Jung
    Abstract:

    : Breast cancer (BRC) is the most invasive cancer in women. Although the survival rate of BRC is gradually increasing due to improved screening systems, development of novel therapeutic targets for inhibition of BRC proliferation, metastasis and recurrence have been constantly needed. Thus, in this study, we identified overexpression of SETDB1 (SET Domain Bifurcated 1), a histone methyltransferase, in RNA-seq data of BRC derived from TCGA portal. In Gene Ontology (GO) Analysis, Cell migration-related GO terms were enriched, and we confirmed down-regulation of Cell migration/invasion and alteration of EMT /MET markers after knockdown of SETDB1. Moreover, gene network Analysis showed that SMAD7 expression is regulated by SETDB1 levels, indicating that up-regulation of SMAD7 by SETDB1 knockdown inhibited BRC metastasis. Therefore, development of SETDB1 inhibitors and functional studies may help develop more effective clinical guidelines for BRC treatment. [BMB Reports 2019; 52(2): 139-144].