scholarly journals ShapeCluster: Applying parametric regression to analyse time-series gene expression data

2016 ◽  
Author(s):  
Philip J Law ◽  
Vicky Buchanan-Wollaston ◽  
Andrew Mead
Keyword(s):  
Gene Expression ◽  
High Throughput ◽  
Time Course ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Parametric Models ◽  
Linear Functions ◽  
Biologically Relevant ◽  
Research Areas ◽  
Biological Stress

High-throughput technologies have made it possible to perform genome-scale analyses to investigate a variety of research areas. From these analyses, vast amounts of data are generated. However, these data can be noisy, which could obscure the underlying signal. Here, a high-throughput regression analysis approach was developed, where a variety of linear and nonlinear parametric models were fitted to gene expression profiles from time course experiments. These models include the logistic, Gompertz, exponential, critical exponential, linear+exponential, Gaussian and linear functions. The fitted parameters from these models reflect aspects of the model shape, and thus allowed for the interpretation of gene expression profiles in terms of the underlying biology, such as the time of initial gene expression. This provides a potentially more mechanistic ap-proach to studying the genetic responses to stimuli. Together with a cluster analysis, termed ShapeCluster, it was possible to group genes based on these aspects of the expression profiles. By investigating different combinations of parameters, this added flexibility to the analysis and allowed for the investigation of the data in multiple ways, including the identification of groups of genes that may be co-regulated, or participate in response to the biological stress in question. Clusters from these methods were assessed for significance through the use of over-represented annotation terms and motifs, and found to pro-duce biologically relevant sets of genes. The ShapeCluster package is available from https://sourceforge.net/projects/shapecluster/.

scholarly journals Bayesian approach for predicting responses to therapy from high-dimensional time-course gene expression profiles

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Arika Fukushima ◽  
Masahiro Sugimoto ◽  
Satoru Hiwa ◽  
Tomoyuki Hiroyasu
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Response To Therapy ◽  
Hcv Infection ◽  
Entire Treatment Period ◽  
Time Points ◽  
Time Course Data ◽  
Conventional Methods

Abstract Background Historical and updated information provided by time-course data collected during an entire treatment period proves to be more useful than information provided by single-point data. Accurate predictions made using time-course data on multiple biomarkers that indicate a patient’s response to therapy contribute positively to the decision-making process associated with designing effective treatment programs for various diseases. Therefore, the development of prediction methods incorporating time-course data on multiple markers is necessary. Results We proposed new methods that may be used for prediction and gene selection via time-course gene expression profiles. Our prediction method consolidated multiple probabilities calculated using gene expression profiles collected over a series of time points to predict therapy response. Using two data sets collected from patients with hepatitis C virus (HCV) infection and multiple sclerosis (MS), we performed numerical experiments that predicted response to therapy and evaluated their accuracies. Our methods were more accurate than conventional methods and successfully selected genes, the functions of which were associated with the pathology of HCV infection and MS. Conclusions The proposed method accurately predicted response to therapy using data at multiple time points. It showed higher accuracies at early time points compared to those of conventional methods. Furthermore, this method successfully selected genes that were directly associated with diseases.

scholarly journals P02.10 FocuSCOPE: a single cell, multi-omics solution to simultaneously analyze tumor variants and microenvironment

2021 ◽  
Vol 9 (Suppl 1) ◽  
pp. A12.1-A12
Author(s):  
Y Arjmand Abbassi ◽  
N Fang ◽  
W Zhu ◽  
Y Zhou ◽  
Y Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Tumor Microenvironment ◽  
Single Cell ◽  
High Throughput ◽  
Immune Cells ◽  
Genetic Variants ◽  
Expression Profiles ◽  
Single Cells ◽  
Gene Expression Profiles ◽  
Single Cell Sequencing

Recent advances of high-throughput single cell sequencing technologies have greatly improved our understanding of the complex biological systems. Heterogeneous samples such as tumor tissues commonly harbor cancer cell-specific genetic variants and gene expression profiles, both of which have been shown to be related to the mechanisms of disease development, progression, and responses to treatment. Furthermore, stromal and immune cells within tumor microenvironment interact with cancer cells to play important roles in tumor responses to systematic therapy such as immunotherapy or cell therapy. However, most current high-throughput single cell sequencing methods detect only gene expression levels or epigenetics events such as chromatin conformation. The information on important genetic variants including mutation or fusion is not captured. To better understand the mechanisms of tumor responses to systematic therapy, it is essential to decipher the connection between genotype and gene expression patterns of both tumor cells and cells in the tumor microenvironment. We developed FocuSCOPE, a high-throughput multi-omics sequencing solution that can detect both genetic variants and transcriptome from same single cells. FocuSCOPE has been used to successfully perform single cell analysis of both gene expression profiles and point mutations, fusion genes, or intracellular viral sequences from thousands of cells simultaneously, delivering comprehensive insights of tumor and immune cells in tumor microenvironment at single cell resolution.Disclosure InformationY. Arjmand Abbassi: None. N. Fang: None. W. Zhu: None. Y. Zhou: None. Y. Chen: None. U. Deutsch: None.

scholarly journals Microbial single-cell RNA sequencing by split-pool barcoding

Science ◽  
2020 ◽  
Vol 371 (6531) ◽  
pp. eaba5257 ◽  
Author(s):  
Anna Kuchina ◽  
Leandra M. Brettner ◽  
Luana Paleologu ◽  
Charles M. Roco ◽  
Alexander B. Rosenberg ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
High Throughput ◽  
Single Cell Analysis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Growth Stages ◽  
High Throughput Analysis ◽  
Single Cell Rna Sequencing

Single-cell RNA sequencing (scRNA-seq) has become an essential tool for characterizing gene expression in eukaryotes, but current methods are incompatible with bacteria. Here, we introduce microSPLiT (microbial split-pool ligation transcriptomics), a high-throughput scRNA-seq method for Gram-negative and Gram-positive bacteria that can resolve heterogeneous transcriptional states. We applied microSPLiT to >25,000 Bacillus subtilis cells sampled at different growth stages, creating an atlas of changes in metabolism and lifestyle. We retrieved detailed gene expression profiles associated with known, but rare, states such as competence and prophage induction and also identified unexpected gene expression states, including the heterogeneous activation of a niche metabolic pathway in a subpopulation of cells. MicroSPLiT paves the way to high-throughput analysis of gene expression in bacterial communities that are otherwise not amenable to single-cell analysis, such as natural microbiota.

scholarly journals Prediction of Pharmacological and Xenobiotic Responses to Drugs Based on Time Course Gene Expression Profiles

PLoS ONE ◽  
2009 ◽  
Vol 4 (12) ◽  
pp. e8126 ◽  
Author(s):  
Tao Huang ◽  
WeiRen Cui ◽  
LeLe Hu ◽  
KaiYan Feng ◽  
Yi-Xue Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Expression Profiles ◽  
Gene Expression Profiles

scholarly journals High-Throughput Gene Expression Profiles to Define Drug Similarity and Predict Compound Activity

2018 ◽  
Vol 16 (3) ◽  
pp. 162-176 ◽  
Author(s):  
Hans De Wolf ◽  
Laure Cougnaud ◽  
Kirsten Van Hoorde ◽  
An De Bondt ◽  
Joerg K. Wegner ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Throughput ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
High Throughput Gene Expression ◽  
Drug Similarity

scholarly journals Identification of Monotonically Differentially Expressed Genes for IFN-β-Treated Multiple Sclerosis Patients

2019 ◽  
Vol 2019 ◽  
pp. 1-6 ◽  
Author(s):  
Suyan Tian ◽  
Lei Zhang
Keyword(s):  
Gene Expression ◽  
Multiple Sclerosis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Biologically Relevant ◽  
Therapeutic Mechanism ◽  
The Central Nervous System ◽  
Multiple Sclerosis Patients ◽  
Term Response

Multiple sclerosis (MS) is a common neurological disability of the central nervous system. Immune-modulatory therapy with interferon-β (IFN-β) has been used as a first-line treatment to prevent relapses in MS patients. While the therapeutic mechanism of IFN-β has not been fully elucidated, the data of microarray experiments that collected longitudinal gene expression profiles to evaluate the long-term response of IFN-β treatment have been analyzed using statistical methods that were incapable of dealing with such data. In this study, the GeneRank method was applied to generate weighted gene expression values and the monotonically expressed genes (MEGs) for both IFN-β treatment responders and nonresponders were identified. The proposed procedure identified 13 MEGs for the responders and 2 MEGs for the nonresponders, most of which are biologically relevant to MS. Our work here provides some useful insight into the mechanism of IFN-β treatment for MS patients. A full understanding of the therapeutic mechanism will enable a more personalized treatment strategy possible.

Genomics and Systems Biology.

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. sci-51-sci-51
Author(s):  
Todd R. Golub
Keyword(s):  
Gene Expression ◽  
High Throughput ◽  
Biological Networks ◽  
High Throughput Screening ◽  
Expression Profiles ◽  
Low Cost ◽  
Gene Expression Profiles ◽  
Gene Expression Signature ◽  
Small Molecule Libraries ◽  
Approved Drugs

Genomics holds particular potential for the elucidation of biological networks that underlie disease. For example, gene expression profiles have been used to classify human cancers, and have more recently been used to predict graft rejection following organ transplantation. Such signatures thus hold promise both as diagnostic approaches and as tools with which to dissect biological mechanism. Such systems-based approaches are also beginning to impact the drug discovery process. For example, it is now feasible to measure gene expression signatures at low cost and high throughput, thereby allowing for the screening libraries of small molecule libraries in order to identify compounds capable of perturbing a signature of interest (even if the critical drivers of that signature are not yet known). This approach, known as Gene Expression-Based High Throughput Screening (GE-HTS), has been shown to identify candidate therapeutic approaches in AML, Ewing sarcoma, and neuroblastoma, and has identified tool compounds capable of inhibiting PDGF receptor signaling. A related approach, known as the Connectivity Map (www.broad.mit.edu/cmap) attempts to use gene expression profiles as a universal language with which to connect cellular states, gene product function, and drug action. In this manner, a gene expression signature of interest is used to computationally query a database of gene expression profiles of cells systematically treated with a large number of compounds (e.g., all off-patent FDA-approved drugs), thereby identifying potential new applications for existing drugs. Such systems level approaches thus seek chemical modulators of cellular states, even when the molecular basis of such altered states is unknown.

Gene expression profiling of normal and malignant CD34-derived megakaryocytic cells

Blood ◽  
2004 ◽  
Vol 104 (10) ◽  
pp. 3126-3135 ◽  
Author(s):  
Elena Tenedini ◽  
Maria Elena Fagioli ◽  
Nicola Vianelli ◽  
Pier Luigi Tazzari ◽  
Francesca Ricci ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Mitochondrial Permeability Transition ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Annexin V ◽  
Permeability Transition Pore ◽  
Permeability Transition ◽  
Course Evaluation ◽  
Oligonucleotide Microarray Analysis

Abstract Gene expression profiles of bone marrow (BM) CD34-derived megakaryocytic cells (MKs) were compared in patients with essential thrombocythemia (ET) and healthy subjects using oligonucleotide microarray analysis to identify differentially expressed genes and disease-specific transcripts. We found that proapoptotic genes such as BAX, BNIP3, and BNIP3L were down-regulated in ET MKs together with genes that are components of the mitochondrial permeability transition pore complex, a system with a pivotal role in apoptosis. Conversely, antiapoptotic genes such as IGF1-R and CFLAR were up-regulated in the malignant cells, as was the SDF1 gene, which favors cell survival. On the basis of the array results, we characterized apoptosis of normal and ET MKs by time-course evaluation of annexin-V and sub-G1 peak DNA stainings of immature and mature MKs after culture in serum-free medium with an optimal thrombopoietin concentration, and annexin-V–positive MKs only, with decreasing thrombopoietin concentrations. ET MKs were more resistant to apoptosis than their normal counterparts. We conclude that imbalance between proliferation and apoptosis seems to be an important step in malignant ET megakaryocytopoiesis.

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