Topic Brief: Stephanie Hicks* from the Department of Biostatistics at Johns Hopkins Bloomberg School of ... In this video (recorded live in class) I give a brief introduction to next generation sequencing.

Statistics And Genomics Background Correction -

Stephanie Hicks* from the Department of Biostatistics at Johns Hopkins Bloomberg School of ... In this video (recorded live in class) I give a brief introduction to next generation sequencing. The last two decades have seen an exponential growth in the quantity of DNA sequencing

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  • Stephanie Hicks* from the Department of Biostatistics at Johns Hopkins Bloomberg School of ...
  • In this video (recorded live in class) I give a brief introduction to next generation sequencing.
  • The last two decades have seen an exponential growth in the quantity of DNA sequencing
  • At the end quality assessment comes up and I show some examples of the utility of ...
  • July 26, 2017 presentation at UCLA for CGSI 2017 Slides not available.

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Supporting Images

Statistics and Genomics: Background correction
Statistics for Genomics: Probe Effects
R-Ladies Riverside (English) - Analyzing Genomics Data in R with Bioconductor
Statistics for Genomics: Batch Effects
Statistics for Genomics: RMA and fRMA
New statistical methods for the analysis of genome variation data - Richard Durbin
Microarray Data Preprocessing Explained | Background Correction, Aggregation & Normalization
Statistics for Genomics: Intro to Next Generation Sequencing
Noah Zaitlen: "Covariate adjustment in genetics and genomics"
Statistics for Genomics: Introduction to Statistics
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Statistics and Genomics: Background correction

Statistics and Genomics: Background correction

I motivate and describe the normal+exponential model used by RMA to

Statistics for Genomics: Probe Effects

Statistics for Genomics: Probe Effects

We describe the probe effect and potential pitfalls if you ignore it. We also describe the basic ideas behind the gene expression ...

R-Ladies Riverside (English) - Analyzing Genomics Data in R with Bioconductor

R-Ladies Riverside (English) - Analyzing Genomics Data in R with Bioconductor

Big thanks to our guest speaker *Dr. Stephanie Hicks* from the Department of Biostatistics at Johns Hopkins Bloomberg School of ...

Statistics for Genomics: Batch Effects

Statistics for Genomics: Batch Effects

I describe the batch effect in some detail. Jeff Leek then explains some solutions.

Statistics for Genomics: RMA and fRMA

Statistics for Genomics: RMA and fRMA

The models behind RMA and fRMA explained. At the end quality assessment comes up and I show some examples of the utility of ...

New statistical methods for the analysis of genome variation data - Richard Durbin

New statistical methods for the analysis of genome variation data - Richard Durbin

The last two decades have seen an exponential growth in the quantity of DNA sequencing

Microarray Data Preprocessing Explained | Background Correction, Aggregation & Normalization

Microarray Data Preprocessing Explained | Background Correction, Aggregation & Normalization

In this video, we break down the three essential steps of microarray

Statistics for Genomics: Intro to Next Generation Sequencing

Statistics for Genomics: Intro to Next Generation Sequencing

In this video (recorded live in class) I give a brief introduction to next generation sequencing. I describe the technology and some ...

Noah Zaitlen: "Covariate adjustment in genetics and genomics"

Noah Zaitlen: "Covariate adjustment in genetics and genomics"

July 26, 2017 presentation at UCLA for CGSI 2017 Slides not available.

Statistics for Genomics: Introduction to Statistics

Statistics for Genomics: Introduction to Statistics

Read more details and related context about Statistics for Genomics: Introduction to Statistics.