PubMed · 9170189
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A guide for technical query contributors.. https://pubmed.ncbi.nlm.nih.gov/9170189/
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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.
Selectivities of solvent polymeric membrane ion-selective electrodes (ISEs) are quantitatively related to equilibria at the interface between the sample and the electrode membrane. However, only correctly determined selectivity coefficients allow accurate predictions of ISE responses to real-world samples. Moreover, they are also required for the optimization of ionophore structures and membrane compositions. Best suited for such purposes are potentiometric selectivity coefficients as defined already in the 1960s. This paper briefly reviews the basic relationships and focuses on possible biases in the determination of selectivity coefficients. The traditional methods to determine selectivity coefficients (separate solution method, fixed interference method) are still the same as those originally proposed by IUPAC in 1976. However, several precautions are needed to obtain meaningful data. For example, errors arise when the response to a weakly interfering ion is also influenced by the primary ion leaching from the membrane. Wrong selectivity coefficients may be also obtained when the interfering agent is highly preferred and the electrode shows counterion interference. Recent advances show how such pitfalls can be avoided. A detailed recipe to determine correct potentiometric selectivity coefficients unaffected by such biases is presented.
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