PubMed · 15272326
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Y Z Zanganah. 2004-07-24. Incidental finding.. https://doi.org/10.1038/sj.bdj.4811502
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MOTIVATION: 10x Genomics VisiumHD enables spatial transcriptomics at 2 µm × 2 µm resolution but exhibits slide-specific, non-periodic striping artifacts due to lane-width variability. These multiplicative row/column effects distort bin total counts and can bias downstream analyses. The state-of-the-art destriping approach is the normalization procedure used as a preprocessing step in bin2cell; it applies sequential high-quantile row- then column-wise normalization, which is asymmetric and can introduce edge effects/macro-stripes and distortions of large-scale total-count structure. RESULTS: We propose a statistical destriping approach that leverages nuclei segmentation from the co-registered H&E image. Assuming transcript abundance is constant within each nucleus, we model bin counts with a negative binomial distribution whose mean is a product of a nucleus-specific concentration and row- and column-specific stripe-factors reflecting lane-width variation. We fit all parameters in a generalized linear modeling framework with cross-validated regularization on stripe-factors and iterative dispersion estimation, and use the fitted parameters to correct the observed counts into a destriped image. On synthetic data with known ground truth, our method improves stripe-factor estimation accuracy and reduces error in corrected counts relative to bin2cell and bin2cell-derived baselines. Across four public VisiumHD slides, it consistently lowers striping intensity while substantially better preserving biological signal present in the large-scale global count structure and avoiding the artifacts introduced by other methods. AVAILABILITY AND IMPLEMENTATION: All source code and links to publicly available data used for this study are available at https://github.com/paolamalsot/destriping-GLM.
The problem of selecting a threshold for the statistical parameter maps in functional MRI (fMRI) is a delicate issue. The use of advanced test statistics and/or the complex dependence structure of fMRI noise may preclude parametric statistical methods for finding appropriate thresholds. Non-parametric statistical methodology has been presented as a feasible alternative. In this paper, we discuss resampling methods for finding thresholds in single subject fMRI analysis. It is shown that the presence of a BOLD response in the time series biases the estimation of the temporal autocorrelation, which in turn leads to biased thresholds. Therefore, proposed resampling methods based on Fourier and wavelet transforms, which employ implicit and weak models of the temporal noise characteristic, may produce erroneous thresholds. In contrast, resampling based on a pre-whitening transform, which is driven by an explicit noise model, is robust to the presence of a BOLD response. The size of the bias is, however, largely dependent on the complexity of the experimental design. While blocked designs can induce large biases, event-related designs generate significantly smaller biases. Results supporting these claims are provided.
OBJECTIVE: C-reactive protein (CRP) has been proposed to be an independent risk factor for cardiovascular disease. In vitro studies investigating the mechanism behind this have used purified commercial CRP (cCRP) and endothelial cells. We investigated the role of contaminants in cCRP preparations. METHODS AND RESULTS: Human umbilical vein endothelial cells and the human endothelial cell line EA.hy926 were incubated with Escherichia coli-derived cCRP, in-house-generated azide-free recombinant, and ascites-purified CRP, azide, or lipopolysaccharide (LPS) equivalent to the concentration present in cCRP preparations. Cells were investigated for change in cell proliferation, morphology, apoptosis, and expression of endothelial NO synthase and intercellular adhesion molecule-1. Cell supernatants were assessed for monocyte chemoattractant protein-1 (MCP-1), interleukin-8, von Willebrand factor secretion, and pH change. Only cCRP was able to induce all activation events analyzed; however, this ability was lost on extensive dialysis, suggesting that low molecular weight contaminants were responsible for these events. Indeed, the effects of cCRP were mirrored by azide or LPS. CONCLUSIONS: We investigated a wide range of effects on endothelial cells ascribed to CRP; however, azide and LPS, but never CRP itself, were responsible for the cell activation events. We conclude that CRP, per se, does not activate endothelial cells.