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Statistics for Genomics: Probe Effects
Statistics for Genomics: Introduction to Statistics
Statistics for Genomics: Intro to Next Generation Sequencing
Statistics for Genomics: RMA and fRMA
Microarray Data Preprocessing Explained | Background Correction, Aggregation & Normalization
Statistics for Genomics: Normalization
Statistcs for Genomics: Base Calling in Next Gen Data
RNA-seq Evaluating Several Custom Microarrays Background Correction and Gene Expression Data
Statistics for Genomics Lab: Distances and Clustering
BOSC2022 S3aa Lauren Sanders Evaluation of batch effect correction methods for space biology RNA seq
Statistics for Genomics: Useful plots and bad plots
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Last Updated: September 30, 2026
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Summary
I motivate and describe the normal+exponential model used by RMA to I describe the batch effect in some detail. Jeff Leek then explains some solutions. Laurent Excoffier, University of Bern Computation-Intensive Probabilistic and We describe the probe effect and potential pitfalls if you ignore it. We also describe the basic ideas behind the gene expression ... In this lecture I introduce the basics of In this video (recorded live in class) I give a brief introduction to next generation sequencing. I describe the technology and some ... The models behind RMA and fRMA explained. At the end quality assessment comes up and I show some examples of the utility of ... In this video, we break down the three essential steps of microarray The need for normalization is motivated and the solutions are described. Specifically, quantile normalization and loess ... I describe base calling for next generation sequencing RNA-seq Evaluating Several Custom Microarrays This lab shows some simple R commands for creating dendrograms and MDS plots. Presented at BOSC 2022, part of ISMB, in Madison, WI. For the first 8 minutes, we cover useful techniques the MA plot and the volcano plot. We then go through Karl Broman's slides ...
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