https://amitjavilaventura.github.io/tags/rna-seq/ Recent content in RNA-Seq on Adrià Mitjavila Ventura Hugo en-us Tue, 31 May 2022 00:00:00 +0000 https://amitjavilaventura.github.io/posts/011-deseq2-normalization/ Tue, 31 May 2022 00:00:00 +0000 https://amitjavilaventura.github.io/posts/011-deseq2-normalization/ <script src="https://amitjavilaventura.github.io/posts/011-deseq2-normalization/index_files/kePrint/kePrint.js"></script> <link href="https://amitjavilaventura.github.io/posts/011-deseq2-normalization/index_files/lightable/lightable.css" rel="stylesheet" /> <script src="https://amitjavilaventura.github.io/posts/011-deseq2-normalization/index_files/kePrint/kePrint.js"></script> <link href="https://amitjavilaventura.github.io/posts/011-deseq2-normalization/index_files/lightable/lightable.css" rel="stylesheet" /> <script src="https://amitjavilaventura.github.io/posts/011-deseq2-normalization/index_files/kePrint/kePrint.js"></script> <link href="https://amitjavilaventura.github.io/posts/011-deseq2-normalization/index_files/lightable/lightable.css" rel="stylesheet" /> <script src="https://amitjavilaventura.github.io/posts/011-deseq2-normalization/index_files/kePrint/kePrint.js"></script> <link href="https://amitjavilaventura.github.io/posts/011-deseq2-normalization/index_files/lightable/lightable.css" rel="stylesheet" /> <script src="https://amitjavilaventura.github.io/posts/011-deseq2-normalization/index_files/kePrint/kePrint.js"></script> <link href="https://amitjavilaventura.github.io/posts/011-deseq2-normalization/index_files/lightable/lightable.css" rel="stylesheet" /> <style> p{text-align: justify;} caption{color:darkblue; font-size:12px} </style> <center> __Adrià Mitjavila__ on *May 31, 2022* </center> <center> Reposted from [BioBit](https://biobit.netlify.app/post/006-deseq2-normalization/). </center> <hr> <br> <center>  </center> <br> <hr> <p>When studying RNA-seq data, one of the most common approaches is to perform differential expression analysis (or <strong>DE analysis</strong>), a method in which two different biological conditions (i.e. different developmental stages or disease status) are compared in order to see if the expression of genes changes between them. <em>Counts</em>, which are sequencing reads (for single-end sequencing) or fragments (for paired-end sequencing) that map to a given genomic feature (i.e. exons), are a proxy to estimate gene expression but, in order to compare the expression of the genes across different condition or even to compare different genes within the same condition, we have to normalize these counts.</p> https://amitjavilaventura.github.io/posts/010-rnaseq-normalizations/ Mon, 30 May 2022 00:00:00 +0000 https://amitjavilaventura.github.io/posts/010-rnaseq-normalizations/ <center> __Adrià Mitjavila__ on *May 31, 2022* </center> <center> Reposted from [BioBit](https://biobit.netlify.app/post/005-rnaseq-normalizations/). </center> <hr> <br> <center>  </center> <br> <hr> <p>When analyzing RNA-seq data and studying gene expression levels, we usually start from <strong>counts</strong>, which are sequencing reads mapped to genomic features such as genes. However, using counts as they are to compare different samples (<em>gene A in sample 1 vs gene A in sample 2</em>) or even genes within a sample (gene is not a good option, since the amount of counts in a gene will vary a lot depending on how much the sample has been sequenced (<strong>sequencing depth</strong>; different between samples) and the <strong>length of the gene</strong> (different between genes): the more reads mapped in a sample, the more counts the genes in that sample will have, and the longer a gene is, the more counts will it have. For this reason, there is a need to normalize the raw counts for the sequencing depth and the gene length to make them comparable among genes within a sample and different samples.</p>