scholarly journals AdaTiSS: A Novel Data-Adaptive Robust Method for Quantifying Tissue Specificity Scores

2019 ◽  
Author(s):  
Meng Wang ◽  
Lihua Jiang ◽  
Michael P. Snyder
Keyword(s):  
Large Scale ◽  
Tissue Specificity ◽  
Population Distribution ◽  
Specific Gene ◽  
Robust Method ◽  
Gene Expressions ◽  
Regression Problem ◽  
Tissue Specific ◽  
Tissue Specific Gene ◽  
Data Adaptive

AbstractMotivationAccurately detecting tissue specificity (TS) in genes helps researchers understand tissue functions at the molecular level, and further identify disease mechanisms and discover tissue-specific therapeutic targets. The Genotype-Tissue Expression (GTEx) project (Consortium, 2015), and the Human Protein Atlas (HPA) project (Uhlén, et al., 2015) are two publicly available data resources, providing large-scale gene expressions across multiple tissue types. Multiple tissue comparisons, technical background noise and unknown variation factors make it challenging to accurately identify tissue specific gene expressions. Several methods worked on measuring the overall TS in gene expressions and classifying genes into tissue-enrichment categories. There still lacks a robust method to provide quantitative TS scores for each tissue.MethodsWe recognized that the key to quantify tissue specific gene expressions is to properly define a concept of expression population. We considered that inside the population, the sample expressions from various tissues are more or less balanced, and the outlier expressions outside the population may indicate tissue specificity. We then formulated the question to robustly estimate the population distribution. In a linear regression problem, we developed a novel data-adaptive robust estimation based on density-power-weight under unknown outlier distribution and non-vanishing outlier proportion (Wang, et al., 2019). In the question of quantifying TS, we focused on the Gaussian-population mixture model. We took into account gene heterogeneities and applied the robust data-adaptive procedure to estimate the population. With the robustly estimated population parameters, we constructed the AdaTiSS algorithm to obtain data-adaptive quantitative TS scores.ResultsOur TS scores from the AdaTiSS algorithm achieve the goal that the TS scores are comparable across tissues and also across genes, which standardize gene expressions in terms of TS. Compared to the categorical TS method such as the HPA criterion, our method provides more information on the population fitting, and shows advantages in quantitatively analyzing tissue specific functions, making the biology functional analysis more precise. We also discuss some limitations and possible future [email protected]

scholarly journals AdaReg: data adaptive robust estimation in linear regression with application in GTEx gene expressions

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Meng Wang ◽  
Lihua Jiang ◽  
Michael P. Snyder
Keyword(s):  
Linear Regression ◽  
Robust Estimation ◽  
Population Distribution ◽  
Selection Criterion ◽  
Simulation Studies ◽  
Gene Expressions ◽  
Regression Problem ◽  
Data Adaptive ◽  
Robust Likelihood ◽  
Tissue Effect

Abstract The Genotype-Tissue Expression (GTEx) project provides a valuable resource of large-scale gene expressions across multiple tissue types. Under various technical noise and unknown or unmeasured factors, how to robustly estimate the major tissue effect becomes challenging. Moreover, different genes exhibit heterogeneous expressions across different tissue types. Therefore, we need a robust method which adapts to the heterogeneities of gene expressions to improve the estimation for the tissue effect. We followed the approach of the robust estimation based on γ-density-power-weight in the works of Fujisawa, H. and Eguchi, S. (2008). Robust parameter estimation with a small bias against heavy contamination. J. Multivariate Anal. 99: 2053–2081 and Windham, M.P. (1995). Robustifying model fitting. J. Roy. Stat. Soc. B: 599–609, where γ is the exponent of density weight which controls the balance between bias and variance. As far as we know, our work is the first to propose a procedure to tune the parameter γ to balance the bias-variance trade-off under the mixture models. We constructed a robust likelihood criterion based on weighted densities in the mixture model of Gaussian population distribution mixed with unknown outlier distribution, and developed a data-adaptive γ-selection procedure embedded into the robust estimation. We provided a heuristic analysis on the selection criterion and found that our practical selection trend under various γ’s in average performance has similar capability to capture minimizer γ as the inestimable mean squared error (MSE) trend from our simulation studies under a series of settings. Our data-adaptive robustifying procedure in the linear regression problem (AdaReg) showed a significant advantage in both simulation studies and real data application in estimating tissue effect of heart samples from the GTEx project, compared to the fixed γ procedure and other robust methods. At the end, the paper discussed some limitations on this method and future work.

scholarly journals A systematic survey of human tissue-specific gene expression and splicing reveals new opportunities for therapeutic target identification and evaluation

2018 ◽  
Author(s):  
Robert Y. Yang ◽  
Jie Quan ◽  
Reza Sodaei ◽  
Francois Aguet ◽  
Ayellet V. Segrè ◽  
...  
Keyword(s):  
Gene Expression ◽  
Drug Discovery ◽  
Therapeutic Target ◽  
Tissue Specificity ◽  
Specific Gene ◽  
High Quality ◽  
Tissue Specific ◽  
Tissue Specific Gene ◽  
Specific Gene Expression ◽  
Novel Therapeutic

AbstractDifferences in the expression of genes and their splice isoforms across human tissues are fundamental factors to consider for therapeutic target evaluation. To this end, we conducted a transcriptome-wide survey of tissue-specific gene expression and splicing events in the unprecedented collection of 8527 high-quality RNA-seq samples from the GTEx project, covering 36 human peripheral tissues and 13 brain subregions. We derived a weighted tissue-specificity scoring scheme accounting for the similarity of related tissues and inherent variability across individual samples. We showed that ~50.6% of all annotated human genes show tissue-specific expression, including many low abundance transcripts vastly underestimated by previous array-based expression atlases. As utilities for drug discovery, we demonstrated that tissue-specificity is a highly desirable attribute of validated drug targets and tissue-specificity can be used to prioritize disease-associated genes from genome-wide association studies (GWAS). Using brain striatum-specific gene expression as an example, we provided a template to leverage tissue-specific gene expression to identify novel therapeutic targets. Mining of tissue-specific splicing further reveals new opportunities for tissue-specific targeting. Thus, the high quality transcriptome atlas provided by the GTEx is an invaluable resource for drug discovery and systematic analysis anchored on the human tissue specific gene expression provides a promising avenue to identify novel therapeutic target hypotheses.

scholarly journals Tissue-specific Gene Expression Prediction Associates Vitiligo with SUOX through an Active Enhancer

2018 ◽  
Author(s):  
Zhihua Qi ◽  
Shiqi Xie ◽  
Rui Chen ◽  
Haji A. Aisa ◽  
Gary C. Hon ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cell Activation ◽  
Cell Activation ◽  
Gene Expression Pattern ◽  
Specific Gene ◽  
Gene Expressions ◽  
Tissue Specific ◽  
Tissue Specific Gene ◽  
Specific Gene Expression ◽  
Putative Enhancer

Vitiligo is an autoimmune disease featuring destruction of melanocytes, which results in patchy depigemtation of skin and hair; two vitiligo GWAS studies identified multiple significant associations, including SNPs in 12q13.2 region. But one study ascribed the association to IKZF4 because it encodes a regulator of T cell activation and is associated with two autoimmune diseases; while the other study ascribed the association to PMEL because it encodes melanocyte protein and has the strongest differential expression between vitiligo lesions and perilesional normal skins. Here we show that vitiligo associated gene in 12q13.2 region is SUOX. Reanalyzing one GWAS dataset, we predicted tissue-specific gene-expression by leveraging Genotype-Tissue Expression (GTEx) datasets, and performed association mapping between the predicted gene-expressions and vitiligo status. SUOX expression is significantly associated with vitiligo in both Nerve (tibia) and Skin (sun exposed) tissues. Epigenetic marks encompass the most significant eQTL of SUOX in both nerve and skin tissues suggest a putative enhancer 3Kb downstream of SUOX. We silenced the putative enhancer using the CRISPR interference system and observed 50% decrease in SUOX expression in K562 cells, a cell line that has similar DNase hypersensitive sites and gene expression pattern to the skin tissue at SUOX locus. Our work provided an example to make sense GWAS hits through examining factors that affect gene expression both computationally and experimentally.

scholarly journals A unique histone 3 lysine 14 chromatin signature underlies tissue-specific gene regulation

2021 ◽  
Author(s):  
Isabel Regadas ◽  
Olle Dahlberg ◽  
Roshan Vaid ◽  
Oanh Ho ◽  
Sergey Belikov ◽  
...  
Keyword(s):  
Gene Regulation ◽  
Specific Gene ◽  
Chromatin Signature ◽  
Tissue Specific ◽  
Tissue Specific Gene ◽  
Histone 3

Histones: genetic diversity and tissue-specific gene expression

1997 ◽  
Vol 107 (1) ◽  
pp. 1-10 ◽  
Author(s):  
D. Doenecke ◽  
W. Albig ◽  
C. Bode ◽  
B. Drabent ◽  
K. Franke ◽  
...  
Keyword(s):  
Gene Expression ◽  
Genetic Diversity ◽  
Specific Gene ◽  
Tissue Specific ◽  
Tissue Specific Gene ◽  
Specific Gene Expression

scholarly journals Blood—Brain Barrier Genomics

2001 ◽  
Vol 21 (1) ◽  
pp. 61-68 ◽  
Author(s):  
Jian Yi Li ◽  
Ruben J. Boado ◽  
William M. Pardridge
Keyword(s):  
Gene Expression ◽  
Blood Brain Barrier ◽  
Brain Barrier ◽  
Specific Gene ◽  
Microvascular Endothelium ◽  
Tissue Specific ◽  
Tissue Specific Gene ◽  
Specific Gene Expression ◽  
Blood Brain ◽  
Tester Cdna

The blood–brain barrier (BBB) is formed by the brain microvascular endothelium, and the unique transport properties of the BBB are derived from tissue-specific gene expression within this cell. The current studies developed a gene microarray approach specific for the BBB by purifying the initial mRNA from isolated rat brain capillaries to generate tester cDNA. A polymerase chain reaction–based subtraction cloning method, suppression subtractive hybridization (SSH), was used, and the BBB cDNA was subtracted with driver cDNA produced from mRNA isolated from rat liver and kidney. Screening 5% of the subtracted tester cDNA resulted in identification of 50 gene products and more than 80% of those were selectively expressed at the BBB; these included novel gene sequences not found in existing databases, ESTs, and known genes that were not known to be selectively expressed at the BBB. Genes in the latter category include tissue plasminogen activator, insulin-like growth factor-2, PC-3 gene product, myelin basic protein, regulator of G protein signaling 5, utrophin, IκB, connexin-45, the class I major histocompatibility complex, the rat homologue of the transcription factors hbrm or EZH1, and organic anion transporting polypeptide type 2. Knowledge of tissue-specific gene expression at the BBB could lead to new targets for brain drug delivery and could elucidate mechanisms of brain pathology at the microvascular level.

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