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      Robust enumeration of cell subsets from tissue expression profiles

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          Abstract

          We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles. When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen, and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content, and closely related cell types. CIBERSORT should enable large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets ( http://cibersort.stanford.edu).

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          Most cited references 21

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          A global map of human gene expression.

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            Practical selection of SVM parameters and noise estimation for SVM regression.

            We investigate practical selection of hyper-parameters for support vector machines (SVM) regression (that is, epsilon-insensitive zone and regularization parameter C). The proposed methodology advocates analytic parameter selection directly from the training data, rather than re-sampling approaches commonly used in SVM applications. In particular, we describe a new analytical prescription for setting the value of insensitive zone epsilon, as a function of training sample size. Good generalization performance of the proposed parameter selection is demonstrated empirically using several low- and high-dimensional regression problems. Further, we point out the importance of Vapnik's epsilon-insensitive loss for regression problems with finite samples. To this end, we compare generalization performance of SVM regression (using proposed selection of epsilon-values) with regression using 'least-modulus' loss (epsilon=0) and standard squared loss. These comparisons indicate superior generalization performance of SVM regression under sparse sample settings, for various types of additive noise.
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              Is Open Access

              Selection of DDX5 as a novel internal control for Q-RT-PCR from microarray data using a block bootstrap re-sampling scheme

              Background The development of microarrays permits us to monitor transcriptomes on a genome-wide scale. To validate microarray measurements, quantitative-real time-reverse transcription PCR (Q-RT-PCR) is one of the most robust and commonly used approaches. The new challenge in gene quantification analysis is how to explicitly incorporate statistical estimation in such studies. In the realm of statistical analysis, the various available methods of the probe level normalization for microarray analysis may result in distinctly different target selections and variation in the scores for the correlation between microarray and Q-RT-PCR. Moreover, it remains a major challenge to identify a proper internal control for Q-RT-PCR when confirming microarray measurements. Results Sixty-six Affymetrix microarray slides using lung adenocarcinoma tissue RNAs were analyzed by a statistical re-sampling method in order to detect genes with minimal variation in gene expression. By this approach, we identified DDX5 as a novel internal control for Q-RT-PCR. Twenty-three genes, which were differentially expressed between adjacent normal and tumor samples, were selected and analyzed using 24 paired lung adenocarcinoma samples by Q-RT-PCR using two internal controls, DDX5 and GAPDH. The percentage correlation between Q-RT-PCR and microarray were 70% and 48% by using DDX5 and GAPDH as internal controls, respectively. Conclusion Together, these quantification strategies for Q-RT-PCR data processing procedure, which focused on minimal variation, ought to significantly facilitate internal control evaluation and selection for Q-RT-PCR when corroborating microarray data.
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                Author and article information

                Journal
                101215604
                32338
                Nat Methods
                Nat. Methods
                Nature methods
                1548-7091
                1548-7105
                12 March 2015
                30 March 2015
                May 2015
                03 February 2016
                : 12
                : 5
                : 453-457
                Affiliations
                [1 ]Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, California, USA
                [2 ]Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, California, USA
                [3 ]Center for Cancer Systems Biology, Stanford University, Stanford, California, USA
                [4 ]Department of Radiology, Stanford University, Stanford, California, USA
                [5 ]Department of Radiation Oncology, Stanford University, Stanford, California, USA
                [6 ]Division of Thoracic Surgery, Department of Cardiothoracic Surgery, Stanford University, Stanford, California, USA
                [7 ]Stanford Cancer Institute, Stanford University, Stanford, California, USA
                [8 ]Division of Hematology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, California, USA
                Author notes
                Correspondence and requests for materials should be addressed to: A.A.A. ( arasha@ 123456stanford.edu )
                [9]

                These authors contributed equally.

                Article
                NIHMS670442
                10.1038/nmeth.3337
                4739640
                25822800

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                Life sciences

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