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Robust computational reconstitution - a new method for the comparative analysis of gene expression in tissues and isolated cell fractions.

Hoffmann M, Pohlers D, Koczan D, Thiesen HJ, Wölfl S, Kinne RW - BMC Bioinformatics (2006)

Bottom Line: Genes that were either regulated (i.e. differentially-expressed in tissue and isolated cell fractions) or robustly-expressed in all patients were identified using different test statistics.Robust Computational Reconstitution uses an intermediate number of robustly-expressed genes to estimate the relative mRNA proportions.This avoids both the exclusive dependence on the robust expression of individual, highly cell type-specific marker genes and the bias towards an equal distribution upon inclusion of all genes for computation.

View Article: PubMed Central - HTML - PubMed

Affiliation: Leibniz Institute for Natural Products Research and Infection Biology - Hans Knöll Institute, Beutenbergstr, 11a, Jena, Germany. martin.hoffmann@hki-jena.de

ABSTRACT

Background: Biological tissues consist of various cell types that differentially contribute to physiological and pathophysiological processes. Determining and analyzing cell type-specific gene expression under diverse conditions is therefore a central aim of biomedical research. The present study compares gene expression profiles in whole tissues and isolated cell fractions purified from these tissues in patients with rheumatoid arthritis and osteoarthritis.

Results: The expression profiles of the whole tissues were compared to computationally reconstituted expression profiles that combine the expression profiles of the isolated cell fractions (macrophages, fibroblasts, and non-adherent cells) according to their relative mRNA proportions in the tissue. The mRNA proportions were determined by trimmed robust regression using only the most robustly-expressed genes (1/3 to 1/2 of all measured genes), i.e. those showing the most similar expression in tissue and isolated cell fractions. The relative mRNA proportions were determined using several different chip evaluation methods, among which the MAS 5.0 signal algorithm appeared to be most robust. The computed mRNA proportions agreed well with the cell proportions determined by immunohistochemistry except for a minor number of outliers. Genes that were either regulated (i.e. differentially-expressed in tissue and isolated cell fractions) or robustly-expressed in all patients were identified using different test statistics.

Conclusion: Robust Computational Reconstitution uses an intermediate number of robustly-expressed genes to estimate the relative mRNA proportions. This avoids both the exclusive dependence on the robust expression of individual, highly cell type-specific marker genes and the bias towards an equal distribution upon inclusion of all genes for computation.

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Results for patient 2. Mean and standard deviation of the computed relative mRNA proportions for macrophages (macro), fibroblasts (fibro) and non-adherent cells (nadc) for patient 2 as a function of the number k of genes included in the trimmed regression approach (Methods section, subsection Mathematical model). The measured expression profiles are MAS-S probe set summaries normalized by a symmetric trimmed mean (t) (Methods section, subsection Data preparation). The reconstituted tissue profile is either not normalized (dashed) or normalized by stepwise local regression (lr, solid) (Methods section, Data preparation). The proportions are determined from the respective means at the number of genes for which the standard deviations approach zero. The chosen numbers are indicated by the dashed and solid (lr) vertical lines, respectively. The convergence is faster for additional local regression (lr), but the resulting proportions are similar. The mRNA proportions are estimated to be pM = 0.82/0.79 (lr) for macrophages, pF = 0.09/0.06 (lr) for fibroblasts and pN = 0.10/0.15 (lr) for non-adherent cells. These values were determined at 6000 and 5000 (lr) included genes, respectively.
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Figure 2: Results for patient 2. Mean and standard deviation of the computed relative mRNA proportions for macrophages (macro), fibroblasts (fibro) and non-adherent cells (nadc) for patient 2 as a function of the number k of genes included in the trimmed regression approach (Methods section, subsection Mathematical model). The measured expression profiles are MAS-S probe set summaries normalized by a symmetric trimmed mean (t) (Methods section, subsection Data preparation). The reconstituted tissue profile is either not normalized (dashed) or normalized by stepwise local regression (lr, solid) (Methods section, Data preparation). The proportions are determined from the respective means at the number of genes for which the standard deviations approach zero. The chosen numbers are indicated by the dashed and solid (lr) vertical lines, respectively. The convergence is faster for additional local regression (lr), but the resulting proportions are similar. The mRNA proportions are estimated to be pM = 0.82/0.79 (lr) for macrophages, pF = 0.09/0.06 (lr) for fibroblasts and pN = 0.10/0.15 (lr) for non-adherent cells. These values were determined at 6000 and 5000 (lr) included genes, respectively.

Mentions: The proposed method for the determination of the relative mRNA proportions is demonstrated using the data of patient 2. Figure 2 shows how the means and standard deviations of the computed relative mRNA proportions of macrophages (pM), fibroblasts (pF), and non-adherent cells (pN) depend on the number k of genes included in the trimmed regression approach (Methods section, subsection Mathematical model). The mean is variable for small k. settles to a constant value for intermediate k and shows a slow and incomplete convergence towards an equal distribution (pM = pF = pN = 1/3) for large k. The respective standard deviations approach zero at an intermediate number of included genes. This number was used to determine the mRNA proportions from the respective mean values. The solid curves correspond to data, for which an additional local regression normalization was performed for the measured and reconstituted tissue profiles in each algorithmic step (Methods section, subsection Data preparation). The decrease of the standard deviation is faster for additional local regression normalization in this case, but the resulting mRNA proportions are similar.


Robust computational reconstitution - a new method for the comparative analysis of gene expression in tissues and isolated cell fractions.

Hoffmann M, Pohlers D, Koczan D, Thiesen HJ, Wölfl S, Kinne RW - BMC Bioinformatics (2006)

Results for patient 2. Mean and standard deviation of the computed relative mRNA proportions for macrophages (macro), fibroblasts (fibro) and non-adherent cells (nadc) for patient 2 as a function of the number k of genes included in the trimmed regression approach (Methods section, subsection Mathematical model). The measured expression profiles are MAS-S probe set summaries normalized by a symmetric trimmed mean (t) (Methods section, subsection Data preparation). The reconstituted tissue profile is either not normalized (dashed) or normalized by stepwise local regression (lr, solid) (Methods section, Data preparation). The proportions are determined from the respective means at the number of genes for which the standard deviations approach zero. The chosen numbers are indicated by the dashed and solid (lr) vertical lines, respectively. The convergence is faster for additional local regression (lr), but the resulting proportions are similar. The mRNA proportions are estimated to be pM = 0.82/0.79 (lr) for macrophages, pF = 0.09/0.06 (lr) for fibroblasts and pN = 0.10/0.15 (lr) for non-adherent cells. These values were determined at 6000 and 5000 (lr) included genes, respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1574358&req=5

Figure 2: Results for patient 2. Mean and standard deviation of the computed relative mRNA proportions for macrophages (macro), fibroblasts (fibro) and non-adherent cells (nadc) for patient 2 as a function of the number k of genes included in the trimmed regression approach (Methods section, subsection Mathematical model). The measured expression profiles are MAS-S probe set summaries normalized by a symmetric trimmed mean (t) (Methods section, subsection Data preparation). The reconstituted tissue profile is either not normalized (dashed) or normalized by stepwise local regression (lr, solid) (Methods section, Data preparation). The proportions are determined from the respective means at the number of genes for which the standard deviations approach zero. The chosen numbers are indicated by the dashed and solid (lr) vertical lines, respectively. The convergence is faster for additional local regression (lr), but the resulting proportions are similar. The mRNA proportions are estimated to be pM = 0.82/0.79 (lr) for macrophages, pF = 0.09/0.06 (lr) for fibroblasts and pN = 0.10/0.15 (lr) for non-adherent cells. These values were determined at 6000 and 5000 (lr) included genes, respectively.
Mentions: The proposed method for the determination of the relative mRNA proportions is demonstrated using the data of patient 2. Figure 2 shows how the means and standard deviations of the computed relative mRNA proportions of macrophages (pM), fibroblasts (pF), and non-adherent cells (pN) depend on the number k of genes included in the trimmed regression approach (Methods section, subsection Mathematical model). The mean is variable for small k. settles to a constant value for intermediate k and shows a slow and incomplete convergence towards an equal distribution (pM = pF = pN = 1/3) for large k. The respective standard deviations approach zero at an intermediate number of included genes. This number was used to determine the mRNA proportions from the respective mean values. The solid curves correspond to data, for which an additional local regression normalization was performed for the measured and reconstituted tissue profiles in each algorithmic step (Methods section, subsection Data preparation). The decrease of the standard deviation is faster for additional local regression normalization in this case, but the resulting mRNA proportions are similar.

Bottom Line: Genes that were either regulated (i.e. differentially-expressed in tissue and isolated cell fractions) or robustly-expressed in all patients were identified using different test statistics.Robust Computational Reconstitution uses an intermediate number of robustly-expressed genes to estimate the relative mRNA proportions.This avoids both the exclusive dependence on the robust expression of individual, highly cell type-specific marker genes and the bias towards an equal distribution upon inclusion of all genes for computation.

View Article: PubMed Central - HTML - PubMed

Affiliation: Leibniz Institute for Natural Products Research and Infection Biology - Hans Knöll Institute, Beutenbergstr, 11a, Jena, Germany. martin.hoffmann@hki-jena.de

ABSTRACT

Background: Biological tissues consist of various cell types that differentially contribute to physiological and pathophysiological processes. Determining and analyzing cell type-specific gene expression under diverse conditions is therefore a central aim of biomedical research. The present study compares gene expression profiles in whole tissues and isolated cell fractions purified from these tissues in patients with rheumatoid arthritis and osteoarthritis.

Results: The expression profiles of the whole tissues were compared to computationally reconstituted expression profiles that combine the expression profiles of the isolated cell fractions (macrophages, fibroblasts, and non-adherent cells) according to their relative mRNA proportions in the tissue. The mRNA proportions were determined by trimmed robust regression using only the most robustly-expressed genes (1/3 to 1/2 of all measured genes), i.e. those showing the most similar expression in tissue and isolated cell fractions. The relative mRNA proportions were determined using several different chip evaluation methods, among which the MAS 5.0 signal algorithm appeared to be most robust. The computed mRNA proportions agreed well with the cell proportions determined by immunohistochemistry except for a minor number of outliers. Genes that were either regulated (i.e. differentially-expressed in tissue and isolated cell fractions) or robustly-expressed in all patients were identified using different test statistics.

Conclusion: Robust Computational Reconstitution uses an intermediate number of robustly-expressed genes to estimate the relative mRNA proportions. This avoids both the exclusive dependence on the robust expression of individual, highly cell type-specific marker genes and the bias towards an equal distribution upon inclusion of all genes for computation.

Show MeSH
Related in: MedlinePlus