Whole-genome amplification is a crucial first step in nearly all single-cell genomic analyses, with the following steps focused on its products. Bias and variance caused by the whole-genome amplification process add numerous challenges to the world of single-cell genomics. Short tandem repeats are sensitive genomic markers used widely in population genetics, forensics, and retrospective lineage tracing. A previous evaluation of common whole-genome amplification targeting ~1000 non-autosomal short tandem repeat loci is extended here to ~12,000 loci across the entire genome via duplex molecular inversion probes. Other than its improved scale and reduced noise, this system detects an abundance of heterogeneous short tandem repeat loci, allowing the allelic balance to be reported. We show here that while the best overall yield is obtained using RepliG-SC, the maximum uniformity between alleles and reproducibility across cells are maximized by Ampli1, rendering it the best candidate for the comparative heterozygous analysis of single-cell genomes.
In this work, we present methods for voice emotion classification using deep learning techniques. To processing audio signals, our method leverages spectral features of voice recordings, which are known to serve as powerful representations of temporal signals. To tackling the classification task, we consider two approaches to processing spectral features: as temporal signals and as spatial/2D signals. For each processing method, we use different neural network architectures that fit the approach. Classification results are analyzed and insights are presented.
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