Statistical approach study microarray di bhatt drushti (4 risultati)
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Da: preigu, Osnabrück, Germaniapreigu
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Taschenbuch. Condizione: Neu. Statistical Approach to Study Microarray Analysis Using Bioconductor | Study of gene expression using R Language | Drushti Bhatt (u. a.) | Taschenbuch | Englisch | LAP Lambert Academic Publishing | EAN 9783659133411 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19,…49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
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Da: Mispah books, Redhill, SURRE, Regno UnitoMispah books
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Paperback. Condizione: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
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Da: moluna, Greven, Germaniamoluna
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Bhatt DrushtiPursuing M.Phil Bioinformatics from Gujarat University, Ahmedabad, India. Completed research work on Microarray data analysis using R Language. Presently working on the project miRNA(micro…RNA), targeting to treat disease.
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Microarray is a novel technology to identify gene expression of thousands of genes simultaneously. This work is attempted to perform microarray data analyses to determine differential gene expression using the open-source R programming…environment in conjunction with the open-source Bioconductor software.We describe procedures for analysis of data using box plots and recommended procedures from Affymetrix for quality control are discussed. The Robust Multichip Averaging (RMA) and MAS5 procedure was used for background correction, normalization and summarization of the AffyBatch probe-level data to obtain expression level data and to discover differentially expressed genes. Heatmaps are used to demonstrate over and under expressed genes in conjunction with t-statistics for determining interesting genes while pFDR was performed to remove false negative. We showed, with real data, how implementation of functions in R and Bioconductor successfully identified differentially expressed genes that may play a role in obesity.
