Tutorial for the analysis of RNAseq data. We will be going through quality control of the reads, alignment of the reads to the reference genome, conversion of the files to raw counts, analysis of the counts with DeSeq2, and finally annotation of the reads using Biomart. The factor of interest Set up the DESeqDataSet, run the DESeq2 pipeline. "/> Using publicly available RNA-seq data from 63 cervical cancer patients, we investigated the expression of ERVs in cervical cancers. This information can be found on line 142 of our merged csv file. Introduction. We get a merged .csv file with our original output from DESeq2 and the Biomart data: Visualizing Differential Expression with IGV: To visualize how genes are differently expressed between treatments, we can use the Broad Institutes Interactive Genomics Viewer (IGV), which can be downloaded from here: IGV, We will be using the .bam files we created previously, as well as the reference genome file in order to view the genes in IGV. RNA sequencing (RNA-seq) is one of the most widely used technologies in transcriptomics as it can reveal the relationship between the genetic alteration and complex biological processes and has great value in . Differential gene expression analysis using DESeq2. Sleuth was designed to work on output from Kallisto (rather than count tables, like DESeq2, or BAM files, like CuffDiff2), so we need to run Kallisto first. If there are no replicates, DESeq can manage to create a theoretical dispersion but this is not ideal. Freely(available(tools(for(QC( FastQC(- hep://www.bioinformacs.bbsrc.ac.uk/projects/fastqc/ (- Nice(GUIand(command(line(interface # In the above plot, highlighted in red are genes which has an adjusted p-values less than 0.1. The DGE In this ordination method, the data points (i.e., here, the samples) are projected onto the 2D plane such that they spread out optimally. You could also use a file of normalized counts from other RNA-seq differential expression tools, such as edgeR or DESeq2. Differential expression analysis is a common step in a Single-cell RNA-Seq data analysis workflow. By removing the weakly-expressed genes from the input to the FDR procedure, we can find more genes to be significant among those which we keep, and so improved the power of our test. The .bam output files are also stored in this directory. Here, we provide a detailed protocol for three differential analysis methods: limma, EdgeR and DESeq2. Abstract. . rnaseq-de-tutorial. edgeR: DESeq2 limma : microarray RNA-seq As a solution, DESeq2 offers transformations for count data that stabilize the variance across the mean.- the regularized-logarithm transformation or rlog (Love, Huber, and Anders 2014). The following optimal threshold and table of possible values is stored as an attribute of the results object. The assembly file, annotation file, as well as all of the files created from indexing the genome can be found in, /common/RNASeq_Workshop/Soybean/gmax_genome. Unlike microarrays, which profile predefined transcript through . To view the purposes they believe they have legitimate interest for, or to object to this data processing use the vendor list link below. Typically, we have a table with experimental meta data for our samples. The tutorial starts from quality control of the reads using FastQC and Cutadapt . The steps we used to produce this object were equivalent to those you worked through in the previous Section, except that we used the complete set of samples and all reads. The package DESeq2 provides methods to test for differential expression analysis. Using select, a function from AnnotationDbi for querying database objects, we get a table with the mapping from Entrez IDs to Reactome Path IDs : The next code chunk transforms this table into an incidence matrix. sz. README.md. Lets create the sample information (you can Simon Anders and Wolfgang Huber, Experiments: Review, Tutorial, and Perspectives Hyeongseon Jeon1,2,*, Juan Xie1,2,3 . We hence assign our sample table to it: We can extract columns from the colData using the $ operator, and we can omit the colData to avoid extra keystrokes. RNA was extracted at 24 hours and 48 hours from cultures under treatment and control. Such filtering is permissible only if the filter criterion is independent of the actual test statistic. The packages well be using can be found here: Page by Dister Deoss. This approach is known as independent filtering. Figure 1 explains the basic structure of the SummarizedExperiment class. # plot to show effect of transformation treatment effect while considering differences in subjects. By removing the weakly-expressed genes from the input to the FDR procedure, we can find more genes to be significant among those which we keep, and so improved the power of our test. How to Perform Welch's t-Test in R - Statology We investigated the. cds = estimateDispersions ( cds ) plotDispEsts ( cds ) It tells us how much the genes expression seems to have changed due to treatment with DPN in comparison to control. Id be very grateful if youd help it spread by emailing it to a friend, or sharing it on Twitter, Facebook or Linked In. of RNA sequencing technology. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. Here, for demonstration, let us select the 35 genes with the highest variance across samples: The heatmap becomes more interesting if we do not look at absolute expression strength but rather at the amount by which each gene deviates in a specific sample from the genes average across all samples. The -f flag designates the input file, -o is the output file, -q is our minimum quality score and -l is the minimum read length. We can see from the above PCA plot that the samples from separate in two groups as expected and PC1 explain the highest variance in the data. The meta data contains the sample characteristics, and has some typo which i corrected manually (Check the above download link). A convenience function has been implemented to collapse, which can take an object, either SummarizedExperiment or DESeqDataSet, and a grouping factor, in this case the sample name, and return the object with the counts summed up for each unique sample. I am interested in all kinds of small RNAs (miRNA, tRNA fragments, piRNAs, etc.). Call row and column names of the two data sets: Finally, check if the rownames and column names fo the two data sets match using the below code. par(mar) manipulation is used to make the most appealing figures, but these values are not the same for every display or system or figure. The column p value indicates wether the observed difference between treatment and control is significantly different. The reference level can set using ref parameter. Differential gene expression (DGE) analysis is commonly used in the transcriptome-wide analysis (using RNA-seq) for studying the changes in gene or transcripts expressions under different conditions (e.g. More at http://bioconductor.org/packages/release/BiocViews.html#___RNASeq. This analysis was performed using R (ver. Quality Control on the Reads Using Sickle: Step one is to perform quality control on the reads using Sickle. The following section describes how to extract other comparisons. Generally, contrast takes three arguments viz. DeSEQ2 for small RNAseq data. The below codes run the the model, and then we extract the results for all genes. Once you have everything loaded onto IGV, you should be able to zoom in and out and scroll around on the reference genome to see differentially expressed regions between our six samples. Its crucial to identify the major sources of variation in the data set, and one can control for them in the DESeq statistical model using the design formula, which tells the software sources of variation to control as well as the factor of interest to test in the differential expression analysis. We want to make sure that these sequence names are the same style as that of the gene models we will obtain in the next section. Hi all, I am approaching the analysis of single-cell RNA-seq data. DESeq2 manual. HISAT2 is a fast and sensitive alignment program for mapping next-generation sequencing reads (both DNA and RNA) to a population of human genomes (as well as to a single reference genome). is a de facto method for quantifying the transcriptome-wide gene or transcript expressions and performing DGE analysis. In RNA-Seq data, however, variance grows with the mean. ``` {r make-groups-edgeR} group <- substr (colnames (data_clean), 1, 1) group y <- DGEList (counts = data_clean, group = group) y. edgeR normalizes the genes counts using the method . We use the gene sets in the Reactome database: This database works with Entrez IDs, so we will need the entrezid column that we added earlier to the res object. mRNA-seq with agnostic splice site discovery for nervous system transcriptomics tested in chronic pain. Perform genome alignment to identify the origination of the reads. Note: DESeq2 does not support the analysis without biological replicates ( 1 vs. 1 comparison). It will be convenient to make sure that Control is the first level in the treatment factor, so that the default log2 fold changes are calculated as treatment over control and not the other way around. Plot the count distribution boxplots with. We now use Rs data command to load a prepared SummarizedExperiment that was generated from the publicly available sequencing data files associated with the Haglund et al. In this tutorial, we explore the differential gene expression at first and second time point and the difference in the fold change between the two time points. Thus, the number of methods and softwares for differential expression analysis from RNA-Seq data also increased rapidly. Perform the DGE analysis using DESeq2 for read count matrix. This plot is helpful in looking at how different the expression of all significant genes are between sample groups. # 2) rlog stabilization and variance stabiliazation [25] lattice_0.20-29 locfit_1.5-9.1 RCurl_1.95-4.3 rmarkdown_0.3.3 rtracklayer_1.24.2 sendmailR_1.2-1 The function rlog returns a SummarizedExperiment object which contains the rlog-transformed values in its assay slot: To show the effect of the transformation, we plot the first sample against the second, first simply using the log2 function (after adding 1, to avoid taking the log of zero), and then using the rlog-transformed values. If you are trying to search through other datsets, simply replace the useMart() command with the dataset of your choice. comparisons of other conditions will be compared against this reference i.e, the log2 fold changes will be calculated However, there is no consensus . Furthermore, removing low count genes reduce the load of multiple hypothesis testing corrections. Continue with Recommended Cookies, The standard workflow for DGE analysis involves the following steps. Of course, this estimate has an uncertainty associated with it, which is available in the column lfcSE, the standard error estimate for the log2 fold change estimate. 2015. As we discuss during the talk we can use different approach and different tools. The correct identification of differentially expressed genes (DEGs) between specific conditions is a key in the understanding phenotypic variation. We call the function for all Paths in our incidence matrix and collect the results in a data frame: This is a list of Reactome Paths which are significantly differentially expressed in our comparison of DPN treatment with control, sorted according to sign and strength of the signal: Many common statistical methods for exploratory analysis of multidimensional data, especially methods for clustering (e.g., principal-component analysis and the like), work best for (at least approximately) homoskedastic data; this means that the variance of an observable quantity (i.e., here, the expression strength of a gene) does not depend on the mean. We here present a relatively simplistic approach, to demonstrate the basic ideas, but note that a more careful treatment will be needed for more definitive results. Plot the mean versus variance in read count data. The function relevel achieves this: A quick check whether we now have the right samples: In order to speed up some annotation steps below, it makes sense to remove genes which have zero counts for all samples. # Otherwise, the filtering would invalidate the test and consequently the assumptions of the BH procedure. They can be found in results 13 through 18 of the following NCBI search: http://www.ncbi.nlm.nih.gov/sra/?term=SRP009826, The script for downloading these .SRA files and converting them to fastq can be found in. This tutorial is inspired by an exceptional RNA seq course at the Weill Cornell Medical College compiled by Friederike Dndar, Luce Skrabanek, and Paul Zumbo and by tutorials produced by Bjrn Grning (@bgruening) for Freiburg Galaxy instance. Such a clustering can also be performed for the genes. This tutorial will serve as a guideline for how to go about analyzing RNA sequencing data when a reference genome is available. Informatics for RNA-seq: A web resource for analysis on the cloud. 1. DESeq2 is an R package for analyzing count-based NGS data like RNA-seq. The BAM files for a number of sequencing runs can then be used to generate count matrices, as described in the following section. This ensures that the pipeline runs on AWS, has sensible . # variance stabilization is very good for heatmaps, etc. Here we see that this object already contains an informative colData slot. The function plotDispEsts visualizes DESeq2s dispersion estimates: The black points are the dispersion estimates for each gene as obtained by considering the information from each gene separately. Determine the size factors to be used for normalization using code below: Plot column sums according to size factor. #Design specifies how the counts from each gene depend on our variables in the metadata #For this dataset the factor we care about is our treatment status (dex) #tidy=TRUE argument, which tells DESeq2 to output the results table with rownames as a first #column called 'row. The colData slot, so far empty, should contain all the meta data. preserving large differences, Creative Commons Attribution 4.0 International License, Two-pass alignment of RNA-seq reads with STAR, Aligning RNA-seq reads with STAR (Complete tutorial), Survival analysis in R (KaplanMeier, Cox proportional hazards, and Log-rank test methods). DISCLAIMER: The postings expressed in this site are my own and are NOT shared, supported, or endorsed by any individual or organization. After all, the test found them to be non-significant anyway. 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