PubMed · 9397479
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A DeGaetano. Framed!. https://pubmed.ncbi.nlm.nih.gov/9397479/
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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.
PCR amplification of DNA from a single initiating genomic molecule or low-copy template often requires two sequential amplification reactions with nested primer pairs to achieve the necessary specificity and sensitivity. Residual outer primers can result in undesired primer activity during the inner nested cycles. To circumvent this problem, we have used dU-containing primers for first round amplification and then uracil N-glycosylase (UNG) to degrade them and the ends of their dU-primer-containing amplified DNA products. We have applied this method to the detection of an exon 11 mutation in the HEXA gene. We have merged the step of a single-tube PCR amplification with outer dU primers with a tandem amplification using non-dU-nested primers (hence, the term merged tandem-nested or M/T-nested PCR). Serial dilutions of genomic DNA showed that this method could amplify a specific target from as few as three haploid genome equivalents of template DNA. Specific products were obtained from the DNA of single cells in 19 of 20 replicates, using 12 outer and 28 inner nested PCR cycles, with an intervening UNG digestion step. When coupled with heteroduplex mutational analysis, this method reliably distinguished mutant versus wild-type HEXA gene fragments amplified from single cells without primer artifact.
We review the pros and cons of analog and digital computation. We propose that computation that is most efficient in its use of resources is neither analog computation nor digital computation but, rather, a mixture of the two forms. For maximum efficiency, the information and information-processing resources of the hybrid form must be distributed over many wires, with an optimal signal-to-noise ratio per wire. Our results suggest that it is likely that the brain computes in a hybrid fashion and that an underappreciated and important reason for the efficiency of the human brain, which consumes only 12 W, is the hybrid and distributed nature of its architecture.