PubMed · 10999200
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A W Song, C A Popp, J Mao, W T Dixon. 2000. fMRI: methodology--acquisition and processing.. https://pubmed.ncbi.nlm.nih.gov/10999200/
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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 detection of the pyrolysis product anhydroecgonine methyl ester (AEME, methylecgonidine) after cocaine smoking using gas chromatography-mass spectrometry is hampered by the artifactual production of AEME. The amount of AEME increases with the amount of cocaine used producing false positive values in authentic samples. A method for the correction of quantitative values was established using calibration of pyrolysis and estimation of the artifactual AEME. Authentic AEME in serum was differentiated from the artifact above 3.5 microg/l, 99% prediction limits of the quantitation were +/-3.1 microg/l. In 16 serum samples and five postmortem blood samples, cocaine and AEME were detected, but after application of the correction method only ten were truly positive for AEME.
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