The Experts below are selected from a list of 309 Experts worldwide ranked by ideXlab platform
Bahjat F. Qaqish - One of the best experts on this subject based on the ideXlab platform.
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row versus column correlations avoiding the Ecological Fallacy in rna protein expression studies
Briefings in Bioinformatics, 2018Co-Authors: Jonathon J Obrien, Harsha P. Gunawardena, Bahjat F. QaqishAbstract:Biomedical researchers are often interested in computing the correlation between RNA and protein abundance. However, correlations can be computed between rows of a data matrix or between columns, and the results are not the same. The belief that these two types of correlation are estimating the same phenomenon is a special case of a well-known logical error called the Ecological Fallacy. In this article, we review different uses of correlation found in the literature, explain the differences between row and column correlations and argue that one of them has an undesirable interpretation in most applications. Through simulation studies and theoretical derivations, we show that the commonly used Pearson's coefficient, computed from protein and transcript data from a single sample, is only loosely related to the biological correlation that most researchers will be interested in studying. Beyond our basic exploration of the Ecological Fallacy, we examine how correlations are affected by relative quantification proteomics data and common normalization procedures, finding that double normalization is capable of completely masking true correlative relationships. We conclude with guidelines for properly identifying and computing consistent correlation coefficients.
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Row versus column correlations: avoiding the Ecological Fallacy in RNA/protein expression studies.
Briefings in bioinformatics, 2017Co-Authors: Jonathon J. O’brien, Harsha P. Gunawardena, Bahjat F. QaqishAbstract:Biomedical researchers are often interested in computing the correlation between RNA and protein abundance. However, correlations can be computed between rows of a data matrix or between columns, and the results are not the same. The belief that these two types of correlation are estimating the same phenomenon is a special case of a well-known logical error called the Ecological Fallacy. In this article, we review different uses of correlation found in the literature, explain the differences between row and column correlations and argue that one of them has an undesirable interpretation in most applications. Through simulation studies and theoretical derivations, we show that the commonly used Pearson's coefficient, computed from protein and transcript data from a single sample, is only loosely related to the biological correlation that most researchers will be interested in studying. Beyond our basic exploration of the Ecological Fallacy, we examine how correlations are affected by relative quantification proteomics data and common normalization procedures, finding that double normalization is capable of completely masking true correlative relationships. We conclude with guidelines for properly identifying and computing consistent correlation coefficients.
Eric Neumayer - One of the best experts on this subject based on the ideXlab platform.
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Socioeconomic Factors and Suicide Rates at Large-unit Aggregate Levels: A Comment
Urban Studies, 2003Co-Authors: Eric NeumayerAbstract:Can socioeconomic factors seemingly explain variation in suicide rates at large-unit aggregate levels only due to an Ecological Fallacy? This is what Kunce and Anderson (2002) suggest based on fixed-effects estimation of US state suicide rates, in which they find little evidence that socioeconomic factors matter. This paper demonstrates that this result does not hold true for other large-unit aggregate levels in an analysis of suicide at the cross-national level. It is found that many socioeconomic factors have a statistically significant impact. It is concluded that sociological and economic theories explaining variation in suicide rates at the large-unit aggregate level with the help of aggregate socioeconomic factors cannot simply be dismissed because of an alleged Ecological Fallacy.
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Socioeconomic Factors and Suicide Rates at Large-unit Aggregate Levels: A Comment
Urban Studies, 2003Co-Authors: Eric NeumayerAbstract:Can socioeconomic factors seemingly explain variation in suicide rates at large-unit aggregate levels only due to an Ecological Fallacy? This is what Kunce and Anderson (2002) suggest based on fixe...
Harsha P. Gunawardena - One of the best experts on this subject based on the ideXlab platform.
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row versus column correlations avoiding the Ecological Fallacy in rna protein expression studies
Briefings in Bioinformatics, 2018Co-Authors: Jonathon J Obrien, Harsha P. Gunawardena, Bahjat F. QaqishAbstract:Biomedical researchers are often interested in computing the correlation between RNA and protein abundance. However, correlations can be computed between rows of a data matrix or between columns, and the results are not the same. The belief that these two types of correlation are estimating the same phenomenon is a special case of a well-known logical error called the Ecological Fallacy. In this article, we review different uses of correlation found in the literature, explain the differences between row and column correlations and argue that one of them has an undesirable interpretation in most applications. Through simulation studies and theoretical derivations, we show that the commonly used Pearson's coefficient, computed from protein and transcript data from a single sample, is only loosely related to the biological correlation that most researchers will be interested in studying. Beyond our basic exploration of the Ecological Fallacy, we examine how correlations are affected by relative quantification proteomics data and common normalization procedures, finding that double normalization is capable of completely masking true correlative relationships. We conclude with guidelines for properly identifying and computing consistent correlation coefficients.
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Row versus column correlations: avoiding the Ecological Fallacy in RNA/protein expression studies.
Briefings in bioinformatics, 2017Co-Authors: Jonathon J. O’brien, Harsha P. Gunawardena, Bahjat F. QaqishAbstract:Biomedical researchers are often interested in computing the correlation between RNA and protein abundance. However, correlations can be computed between rows of a data matrix or between columns, and the results are not the same. The belief that these two types of correlation are estimating the same phenomenon is a special case of a well-known logical error called the Ecological Fallacy. In this article, we review different uses of correlation found in the literature, explain the differences between row and column correlations and argue that one of them has an undesirable interpretation in most applications. Through simulation studies and theoretical derivations, we show that the commonly used Pearson's coefficient, computed from protein and transcript data from a single sample, is only loosely related to the biological correlation that most researchers will be interested in studying. Beyond our basic exploration of the Ecological Fallacy, we examine how correlations are affected by relative quantification proteomics data and common normalization procedures, finding that double normalization is capable of completely masking true correlative relationships. We conclude with guidelines for properly identifying and computing consistent correlation coefficients.
Jonathon J Obrien - One of the best experts on this subject based on the ideXlab platform.
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row versus column correlations avoiding the Ecological Fallacy in rna protein expression studies
Briefings in Bioinformatics, 2018Co-Authors: Jonathon J Obrien, Harsha P. Gunawardena, Bahjat F. QaqishAbstract:Biomedical researchers are often interested in computing the correlation between RNA and protein abundance. However, correlations can be computed between rows of a data matrix or between columns, and the results are not the same. The belief that these two types of correlation are estimating the same phenomenon is a special case of a well-known logical error called the Ecological Fallacy. In this article, we review different uses of correlation found in the literature, explain the differences between row and column correlations and argue that one of them has an undesirable interpretation in most applications. Through simulation studies and theoretical derivations, we show that the commonly used Pearson's coefficient, computed from protein and transcript data from a single sample, is only loosely related to the biological correlation that most researchers will be interested in studying. Beyond our basic exploration of the Ecological Fallacy, we examine how correlations are affected by relative quantification proteomics data and common normalization procedures, finding that double normalization is capable of completely masking true correlative relationships. We conclude with guidelines for properly identifying and computing consistent correlation coefficients.
Jonathon J. O’brien - One of the best experts on this subject based on the ideXlab platform.
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Row versus column correlations: avoiding the Ecological Fallacy in RNA/protein expression studies.
Briefings in bioinformatics, 2017Co-Authors: Jonathon J. O’brien, Harsha P. Gunawardena, Bahjat F. QaqishAbstract:Biomedical researchers are often interested in computing the correlation between RNA and protein abundance. However, correlations can be computed between rows of a data matrix or between columns, and the results are not the same. The belief that these two types of correlation are estimating the same phenomenon is a special case of a well-known logical error called the Ecological Fallacy. In this article, we review different uses of correlation found in the literature, explain the differences between row and column correlations and argue that one of them has an undesirable interpretation in most applications. Through simulation studies and theoretical derivations, we show that the commonly used Pearson's coefficient, computed from protein and transcript data from a single sample, is only loosely related to the biological correlation that most researchers will be interested in studying. Beyond our basic exploration of the Ecological Fallacy, we examine how correlations are affected by relative quantification proteomics data and common normalization procedures, finding that double normalization is capable of completely masking true correlative relationships. We conclude with guidelines for properly identifying and computing consistent correlation coefficients.
