Preprocessing implementation for microarray (PRIM): an efficient method for processing cDNA microarray data

cDNA microarray technology is useful for systematically analyzing the expression profiles of thousands of genes at once. Although many useful results inferred by using this technology and a hierarchical clustering method for statistical analysis have been confirmed using other methods, there are still questions about the reproducibility of the data. We have therefore developed a data processing method that very efficiently extracts reproducible data from the result of duplicate experiments. It is designed to automatically filter the raw results obtained from cDNA microarray image-analysis software. We optimize the threshold value for filtering the data by using the product of N and R, where N is the ratio of the number of spots that passed the filtering vs. the total number of spots, and R is the correlation coefficient for results obtained in the duplicate experiments. Using this method to process mouse tissue expression profile data that contain 1,881,600 points of analysis, we obtained clustered results more reasonable than those obtained using previously reported filtering methods.

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Identification and characterization of expression profiles of neuropeptides and their GPCRs in the swimming crab, Portunus trituberculatus

PeerJ ◽

10.7717/peerj.12179 ◽

2021 ◽

Vol 9 ◽

pp. e12179

Author(s):

Shisheng Tu ◽

Rui Xu ◽

Mengen Wang ◽

Xi Xie ◽

Chenchang Bao ◽

...

Keyword(s):

Central Nervous System ◽

Nervous System ◽

Expression Profile ◽

Expression Profiles ◽

Tissue Expression ◽

Portunus Trituberculatus ◽

Physiological Processes ◽

Tissue Expression Profile ◽

The Central Nervous System ◽

Reproductive Processes

Neuropeptides and their G protein-coupled receptors (GPCRs) regulate multiple physiological processes. Currently, little is known about the identity of native neuropeptides and their receptors in Portunus trituberculatus. This study employed RNA-sequencing and reverse transcription-polymerase chain reaction (RT-PCR) techniques to identify neuropeptides and their receptors that might be involved in regulation of reproductive processes of P. trituberculatus. In the central nervous system transcriptome data, 47 neuropeptide transcripts were identified. In further analyses, the tissue expression profile of 32 putative neuropeptide-encoding transcripts was estimated. Results showed that the 32 transcripts were expressed in the central nervous system and 23 of them were expressed in the ovary. A total of 47 GPCR-encoding transcripts belonging to two classes were identified, including 39 encoding GPCR-A family and eight encoding GPCR-B family. In addition, we assessed the tissue expression profile of 33 GPCRs (27 GPCR-As and six GPCR-Bs) transcripts. These GPCRs were found to be widely expressed in different tissues. Similar to the expression profiles of neuropeptides, 20 of these putative GPCR-encoding transcripts were also detected in the ovary. This is the first study to establish the identify of neuropeptides and their GPCRs in P. trituberculatus, and provide information for further investigations into the effect of neuropeptides on the physiology and behavior of decapod crustaceans.

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Deep-learning-based cell composition analysis from tissue expression profiles

10.1101/659227 ◽

2019 ◽

Cited By ~ 1

Author(s):

Kevin Menden ◽

Mohamed Marouf ◽

Sergio Oller ◽

Anupriya Dalmia ◽

Karin Kloiber ◽

...

Keyword(s):

Deep Learning ◽

Expression Profiles ◽

Tissue Expression ◽

Mouse Tissue ◽

Composition Analysis ◽

Complex Data ◽

Rna Seq ◽

Data Types ◽

Multiple Data ◽

Multiple Data Sets

AbstractWe present Scaden, a deep neural network for cell deconvolution that uses gene expression information to infer the cellular composition of tissues. Scaden is trained on single cell RNA-seq data to engineer discriminative features that confer robustness to bias and noise, making complex data preprocessing and feature selection unnecessary. We demonstrate that Scaden outperforms existing deconvolution algorithms in both precision and robustness. A single trained network reliably deconvolves bulk RNA-seq and microarray, human and mouse tissue expression data and leverages the combined information of multiple data sets. Due to this stability and flexibility, we surmise that deep learning will become an algorithmic mainstay for cell deconvolution of various data types. Scaden’s comprehensive software package is easy to use on novel as well as diverse existing expression datasets available in public resources, deepening the molecular and cellular understanding of developmental and disease processes.

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Identification and characterization of key long non-coding RNAs in the mouse cochlea

10.1101/2020.07.10.197251 ◽

2020 ◽

Author(s):

Tal Koffler-Brill ◽

Shahar Taiber ◽

Alejandro Anaya ◽

Mor Bordeynik-Cohen ◽

Einat Rosen ◽

...

Keyword(s):

Inner Ear ◽

Auditory System ◽

Cellular Localization ◽

Expression Profiles ◽

Cell Types ◽

Tissue Expression ◽

Distinct Pattern ◽

Mouse Tissue ◽

Large Variability ◽

Non Coding Rnas

AbstractThe auditory system is a complex sensory network with an orchestrated multilayer regulatory program governing its development and maintenance. Accumulating evidence has implicated long non-coding RNAs (lncRNAs) as important regulators in numerous systems, as well as in pathological pathways. However, their function in the auditory system has yet to be explored. Using a set of specific criteria, we selected four lncRNAs expressed in the mouse cochlea, which are conserved in the human transcriptome and are relevant for inner ear function. Bioinformatic characterization demonstrated a lack of coding potential and an absence of evolutionary conservation that represent properties commonly shared by their class members. RNAscope analysis of the spatial and temporal expression profiles revealed specific localization to inner ear cells. Sub-cellular localization analysis presented a distinct pattern for each lncRNA and mouse tissue expression evaluation displayed a large variability in terms of level and location. Our findings establish the expression of specific lncRNAs in different cell types of the auditory system and present a potential pathway by which the lncRNA Gas5 acts in the inner ear. Studying lncRNAs and deciphering their functions may deepen our knowledge of inner ear physiology and morphology and may reveal the basis of as yet unresolved genetic hearing loss-related pathologies. Moreover, our experimental design may be employed as a reference for studying other inner ear-related lncRNAs, as well as lncRNAs expressed in other sensory systems.

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Deep learning–based cell composition analysis from tissue expression profiles

Science Advances ◽

10.1126/sciadv.aba2619 ◽

2020 ◽

Vol 6 (30) ◽

pp. eaba2619 ◽

Cited By ~ 4

Author(s):

Kevin Menden ◽

Mohamed Marouf ◽

Sergio Oller ◽

Anupriya Dalmia ◽

Daniel Sumner Magruder ◽

...

Keyword(s):

Deep Learning ◽

Web Application ◽

Expression Profiles ◽

Tissue Expression ◽

Mouse Tissue ◽

Composition Analysis ◽

Complex Data ◽

Rna Seq ◽

Data Types ◽

Multiple Datasets

We present Scaden, a deep neural network for cell deconvolution that uses gene expression information to infer the cellular composition of tissues. Scaden is trained on single-cell RNA sequencing (RNA-seq) data to engineer discriminative features that confer robustness to bias and noise, making complex data preprocessing and feature selection unnecessary. We demonstrate that Scaden outperforms existing deconvolution algorithms in both precision and robustness. A single trained network reliably deconvolves bulk RNA-seq and microarray, human and mouse tissue expression data and leverages the combined information of multiple datasets. Because of this stability and flexibility, we surmise that deep learning will become an algorithmic mainstay for cell deconvolution of various data types. Scaden’s software package and web application are easy to use on new as well as diverse existing expression datasets available in public resources, deepening the molecular and cellular understanding of developmental and disease processes.

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cDNA microarray analysis of gene expression profiles in human placenta: up-regulation of the transcript encoding muscle subunit of glycogen phosphorylase in preeclampsia

Journal of the Society for Gynecologic Investigation ◽

10.1016/s1071-5576(03)00154-0 ◽

2003 ◽

Vol 10 (8) ◽

pp. 496-502 ◽

Cited By ~ 21

Author(s):

S Tsoi

Keyword(s):

Gene Expression ◽

Microarray Analysis ◽

Cdna Microarray ◽

Human Placenta ◽

Glycogen Phosphorylase ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Cdna Microarray Analysis

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Review for "Circulating and tissue expression profile of microRNAs in primary hyperparathyroidism due to sporadic parathyroid adenomas"

10.1002/jbm4.10431/v2/review2 ◽

2020 ◽

Keyword(s):

Primary Hyperparathyroidism ◽

Expression Profile ◽

Tissue Expression ◽

Parathyroid Adenomas ◽

Tissue Expression Profile

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Review for "Circulating and tissue expression profile of microRNAs in primary hyperparathyroidism due to sporadic parathyroid adenomas"

10.1002/jbm4.10431/v1/review1 ◽

2020 ◽

Keyword(s):

Primary Hyperparathyroidism ◽

Expression Profile ◽

Tissue Expression ◽

Parathyroid Adenomas ◽

Tissue Expression Profile

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Development and Validation of a Prognostic Nomogram Based on DNA Methylation-Driven Genes for Patients With Ovarian Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.675197 ◽

2021 ◽

Vol 12 ◽

Author(s):

Min Zhou ◽

Shasha Hong ◽

Bingshu Li ◽

Cheng Liu ◽

Ming Hu ◽

...

Keyword(s):

Ovarian Cancer ◽

Dna Methylation ◽

Mrna Expression ◽

Risk Score ◽

Cox Regression ◽

Expression Profiles ◽

Figo Stage ◽

Prognostic Indicator ◽

Tissue Expression ◽

The Cancer Genome Atlas

Background: DNA methylation affects the development, progression, and prognosis of various cancers. This study aimed to identify DNA methylated-differentially expressed genes (DEGs) and develop a methylation-driven gene model to evaluate the prognosis of ovarian cancer (OC).Methods: DNA methylation and mRNA expression profiles of OC patients were downloaded from The Cancer Genome Atlas, Genotype-Tissue Expression, and Gene Expression Omnibus databases. We used the R package MethylMix to identify DNA methylation-regulated DEGs and built a prognostic signature using LASSO Cox regression. A quantitative nomogram was then drawn based on the risk score and clinicopathological features.Results: We identified 56 methylation-related DEGs and constructed a prognostic risk signature with four genes according to the LASSO Cox regression algorithm. A higher risk score not only predicted poor prognosis, but also was an independent poor prognostic indicator, which was validated by receiver operating characteristic (ROC) curves and the validation cohort. A nomogram consisting of the risk score, age, FIGO stage, and tumor status was generated to predict 3- and 5-year overall survival (OS) in the training cohort. The joint survival analysis of DNA methylation and mRNA expression demonstrated that the two genes may serve as independent prognostic biomarkers for OS in OC.Conclusion: The established qualitative risk score model was found to be robust for evaluating individualized prognosis of OC and in guiding therapy.

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