PubMed · 9549768
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K Martensen. Positioning technique needs attention.. https://pubmed.ncbi.nlm.nih.gov/9549768/
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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.
In two recent studies, Smith et al. (Smith, D.A., Mar, C.M., Turoff, B.K., 1998. The structure of schizophrenic symptoms: a meta-analytic confirmatory factor analysis. Schizophr. Res. 31, 57-70) and Grube et al. (Grube, B.S., Bilder, R.M., Goldman, R.S., 1998. Meta-analysis of symptom factors in schizophrenia. Schizophr. Res. 31, 113-120) used meta-analysis to examine the syndromal structure of schizophrenia. A limitation of both these studies is that the nine subscale scores from Andreasen's Scales for Assessment of Positive and Negative symptoms formed the basis of the analyses. These nine ratings, only four of which represent positive symptoms, do not adequately respresent the diversity of positive symptoms. A review of studies that examined the correlation between the individual items of these scales failed to support the classification of symptoms into these nine subgroups. Studies that indicated low numbers of syndromes suffered from one or more of the following limitations: (1) samples that were restricted to chronic schizophrenia, (2) exclusion of many items from analysis, and (3) a poor fit of the symptom model to the data. Studies not limited in these ways indicated the presence of at least 11 major dimensions of schizophrenic symptomatology, not including affective symptoms. It is concluded that the three-syndrome model of schizophrenia is largely an artefact of inadequate measurement at the symptom level.
While using YIp356 and YEp356R lacZ reporter plasmids, we found lacZ expression driven by the ARG2 promoter to be much higher in cells grown on a non-glucose carbon source than in glucose-grown cells (5-10-fold higher on galactose and up to 40-fold higher on ethanol). Furthermore, expression increased 30-fold upon shifting from a high-glucose to a low-glucose medium. This carbon source regulation requires Snf1p and possibly Ssn6p. It appears, however, to be artefactually mediated by plasmid sequences located upstream from the multicloning site. This emerged from the following observations: (a) the derepressive effect disappears if any extra piece of DNA is inserted upstream from the ARG2 promoter; and (b) similar derepression on low glucose is observed with another yeast promoter (ARG11), provided that the flanking 5' region is short. We determined that the cis-elements responsible for this physiologically irrelevant glucose regulation are located between positions 636 and 879 of the pUC18 DNA sequence.