What is the point of gene set or pathway enrichment analysis?

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Gene set or pathway enrichment analysis is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states. There are many different ways to perform gene set or pathway enrichment analysis, from hypergeometric distribution tests to bootstrap based approaches. Gene set or pathway enrichment

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Introduction to CyTOF

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Motivation: Characterizing large quantities of single cells is important to address clinically and biologically relevant questions. Traditionally, bulk measurements have been used to characterize the functional states of diseased vs. normal immune cells, livers, brains, and tissues or organs of interest to identify various interesting mechanistic differences. However, these bulk measurements average information across many

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mRNA Library Preparation for RNA-seq

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RNA-seq allows for quantiative transcriptomic profiling of biological samples. RNA-seq takes advantage of technological advancements in high-throughput sequencing by converting RNAs of interest to a library of cDNA fragments that can then be sequenced using standard DNA-sequencing technologies. Preparing a sample for RNA-seq typically begins with either rRNA depletion of mRNA enrichment. This is due

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Genome amplification approaches: DOP-PCR, MDA, MALBAC

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DOP-PCR = degenerate oligonucleotide priming polymerase chain reaction Method Overview: In the first cycling stage (5-8 cycles), low-temperature annealing and extension occur at many binding sites in the genome and become tagged with the DOP primer. In the second cycling stage (>25 cycles), annealing temperature is raised, increasing priming specificity during amplification of the tagged

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SNP vs. SNV vs. Mutation

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SNP = single nucleotide polymorphism. As its name suggests, a SNP is a DNA change at the single nucleotide level. By virtue of being a polymorphism, a SNP must occur commonly within a population, with “common” typically being understood as having a minor allele frequency of >1%. The commonality of SNPs generally implies that SNPs

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Simple English Elevator Conversation Explanations: SVM

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SVM stands for support vector machine. An SVM is a supervised machine learning algorithm that’s used to analyze and recognize patterns in data for classification. This machine learning algorithm is “supervised” in the sense that we train the SVM on a set of known labeled truths, and then apply the model to predict for the

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