Matthew E. Law, Wei Shi, Gordon K. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data.

Recently, the capabilities of limma have been significantly expanded in two important directions.

limma workflow

First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing RNA-seq data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures.

This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

Gene expression technologies are used frequently in molecular biology research to gain a snapshot of transcriptional activity in different tissues or populations of cells. These profiles are then compared to identify gene expression changes associated with a treatment condition or phenotype of interest. Gene expression studies may be randomized designed experiments in which a biological system is perturbed, for example by a gene knock-out or by applying a specified stressor.

Such experiments are amongst the most powerful tools in functional genomics, providing insights into normal cellular processes as well as disease pathogenesis. Or they may be observational studies in which different phenotypes are compared, diseased and normal tissue for example or cells from different populations.

Such studies are common in cancer research and in the study of cell development. In either case, the study design can range from simple two group comparisons to complex set-ups with several experimental factors varying over multiple levels.

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Researchers might be interested for example in whether a particular gene facilitates or blocks the action of a particular drug, in which case knock-down and wild-type samples both with and without drug treatment would be profiled. Observational studies may involve multiple batch effects and covariates that must be accounted for in the analysis.The updated workflow makes use of current versions of software: R version 3.

The filtering strategy has been relaxed, using default settings in the filterByExpr function from the edgeR package which retains approximately more genes than the previous version. Output downstream of filtering has been updated, including adjustment to the vertical dotted line in Figure 1 marking the new log-CPM threshold.

Glimma MD plot and heatmap now uses lcpm values to represent expression. The reference for Glimma has been updated, and the id. Placement of some figures have been adjusted so that they appear around its associated text, which previously affected the pdf version of the article. Xueyi Dong also updated the workflow to Bioconductor 3.

The ability to easily and efficiently analyse RNA-sequencing data is a key strength of the Bioconductor project. Starting with counts summarised at the gene-level, a typical analysis involves pre-processing, exploratory data analysis, differential expression testing and pathway analysis with the results obtained informing future experiments and validation studies. In this workflow article, we analyse RNA-sequencing data from the mouse mammary gland, demonstrating use of the popular edgeR package to import, organise, filter and normalise the data, followed by the limma package with its voom method, linear modelling and empirical Bayes moderation to assess differential expression and perform gene set testing.

This pipeline is further enhanced by the Glimma package which enables interactive exploration of the results so that individual samples and genes can be examined by the user. The complete analysis offered by these three packages highlights the ease with which researchers can turn the raw counts from an RNA-sequencing experiment into biological insights using Bioconductor.

limma workflow

RNA-sequencing RNA-seq has become the primary technology used for gene expression profiling, with the genome-wide detection of differentially expressed genes between two or more conditions of interest one of the most commonly asked questions by researchers. The edgeR 1 and limma packages 2 available from the Bioconductor project 3 offer a well-developed suite of statistical methods for dealing with this question for RNA-seq data.

In this article, we describe an edgeR - limma workflow for analysing RNA-seq data that takes gene-level counts as its input, and moves through pre-processing and exploratory data analysis before obtaining lists of differentially expressed DE genes and gene signatures. This analysis is enhanced through the use of interactive graphics from the Glimma package 4that allows for a more detailed exploration of the data at both the sample and gene-level than is possible using static R plots.

The experiment analysed in this workflow is from Sheridan et al. RNA samples were sequenced across three batches on an Illumina HiSeq to obtain base-pair single-end reads.

The analysis outlined in this article assumes that reads obtained from an RNA-seq experiment have been aligned to an appropriate reference genome and summarised into counts associated with gene-specific regions.

In this instance, reads were aligned to the mouse reference genome mm10 using the R based pipeline available in the Rsubread package specifically the align function 6 followed by featureCounts 7 for gene-level summarisation based on the in-built mm10 RefSeq-based annotation. Further information on experimental design and sample preparation is also available from GEO under this accession number.

Each of these text files contains the raw gene-level counts for a given sample. Note that our analysis only includes the basal, LP and ML samples from this experiment see associated file names below.

Whilst each of the nine text files can be read into R separately and combined into a matrix of counts, edgeR offers a convenient way to do this in one step using the readDGE function.

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The resulting DGEList-object contains a matrix of counts with 27, rows associated with unique Entrez gene identifiers IDs and nine columns associated with the individual samples in the experiment. For downstream analysis, sample-level information related to the experimental design needs to be associated with the columns of the counts matrix. This should include experimental variables, both biological and technical, that could have an effect on expression levels.

Examples include cell type basal, LP and ML in this experimentgenotype wild-type, knock-outphenotype disease status, sex, agesample treatment drug, control and batch information date experiment was performed if samples were collected and analysed at distinct time points to name just a few. Our DGEList-object contains a samples data frame that stores both cell type or group and batch sequencing lane information, each of which consists of three distinct levels.

A second data frame named genes in the DGEList-object is used to store gene-level information associated with rows of the counts matrix. This information can be retrieved using organism specific packages such as Mus.VK assisted in reproducible delivery of the workflow materials. Bioconductor has many packages which support analysis of high-throughput sequence data, including RNA sequencing RNA-seq. The packages which we will use in this workflow include core packages maintained by the Bioconductor core team for importing and processing raw sequencing data and loading gene annotations.

We will also use contributed packages for statistical analysis and visualization of sequencing data. The packages used in this workflow are loaded with the library function and can be installed by following the Bioconductor package installation instructions.

If you have questions about this workflow or any Bioconductor software, please post these to the Bioconductor support site. If the questions concern a specific package, you can tag the post with the name of the package, or for general questions about the workflow, tag the post with rnaseqgene. Note the posting guide for crafting an optimal question for the support site. The data used in this workflow is stored in the airway package that summarizes an RNA-seq experiment wherein airway smooth muscle cells were treated with dexamethasone, a synthetic glucocorticoid steroid with anti-inflammatory effects 1.

Glucocorticoids are used, for example, by people with asthma to reduce inflammation of the airways. In the experiment, four primary human airway smooth muscle cell lines were treated with 1 micromolar dexamethasone for 18 hours. For each of the four cell lines, we have a treated and an untreated sample.

The value in the i -th row and the j -th column of the matrix tells how many reads or fragments, for paired-end RNA-seq have been unambiguously assigned to gene i in sample j. Analogously, for other types of assays, the rows of the matrix might correspond e. The computational analysis of an RNA-seq experiment begins earlier: we first obtain a set of FASTQ files that contain the nucleotide sequence of each read and a quality score at each position.

These reads must first be aligned to a reference genome or transcriptome. It is important to know if the sequencing experiment was single-end or paired-end, as the alignment software will require the user to specify both FASTQ files for a paired-end experiment. A number of software programs exist to align reads to a reference genome, and the development is too rapid for this document to provide an up-to-date list. We recommend consulting benchmarking papers that discuss the advantages and disadvantages of each software, which include accuracy, sensitivity in aligning reads over splice junctions, speed, memory footprint, usability, and many other features.

The reads for this experiment were aligned to the Ensembl release 75 8 human reference genome using the STAR read aligner 9. In this example, we have a file in the current directory called files with each line containing an identifier for each experiment, and we have all the FASTQ files in a subdirectory fastq.

If you have performed a single-end experiment, you would only have one file per ID. We have also created a subdirectory, alignedwhere STAR will output its alignment files. The — flag can be used to allocate additional threads.

The BAM files for a number of sequencing runs can then be used to generate count matrices, as described in the following section. Besides the count matrix that we will use later, the airway package also contains eight files with a small subset of the total number of reads in the experiment.

The reads were selected which aligned to a small region of chromosome 1. We chose a subset of reads because the full alignment files are large a few gigabytes eachand because it takes between 10—30 minutes to count the fragments for each sample. We will use these files to demonstrate how a count matrix can be constructed from BAM files.

Afterwards, we will load the full count matrix corresponding to all samples and all data, which is already provided in the same package, and will continue the analysis with that full matrix. The R function system.

Here we ask for the full path to the extdata directory, where R packages store external data, that is part of the airway package. For your own project, you might create such a comma-separated value CSV file using a text editor or spreadsheet software such as Excel.This analysis operates under the assumption that biological replicates or batches within an individual in this case share similar correlation across genes.

Morever, the analysis permis negative correlation between replicates. For every single gene, we will fit a mixed model assuming differences between batches are not individual-specific as follows.

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For every single gene, we will fit a mixed model assuming differences between batches as homogeneous within individuals as follows. Note that limma does not accommodate fitting of nested random effect.

We will use other algorithms to remove unwanted variation under the nested model framework. Standardize the molecule counts to account for differences in sequencing depth. This is necessary because the sequencing depth affects the total molecule counts. Home About License GitHub. Mixed effect model for batch correction - limma Joyce Hsiao Setup source "functions. Remove unwanted variation First, we create a unique identifying ID for the 9 batches. Session information sessionInfo R version 3.All authors wrote and approved the final manuscript.

This version of the workflow contains a number of improvements based on the referees' comments. We have re-compiled the workflow using the latest packages from Bioconductor release 3. We have added a reference to the Bioconductor workflow page, which provides user-friendly instructions for installation and execution of the workflow. We have also moved cell cycle classification before gene filtering as this provides more precise cell cycle phase classifications.

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Some minor rewording and elaborations have also been performed in various parts of the article. This provides biological resolution that cannot be matched by bulk RNA sequencing, at the cost of increased technical noise and data complexity. The differences between scRNA-seq and bulk RNA-seq data mean that the analysis of the former cannot be performed by recycling bioinformatics pipelines for the latter.

Rather, dedicated single-cell methods are required at various steps to exploit the cellular resolution while accounting for technical noise. This article describes a computational workflow for low-level analyses of scRNA-seq data, based primarily on software packages from the open-source Bioconductor project. It covers basic steps including quality control, data exploration and normalization, as well as more complex procedures such as cell cycle phase assignment, identification of highly variable and correlated genes, clustering into subpopulations and marker gene detection.

RNA-Seq workflow: gene-level exploratory analysis and differential expression

Analyses were demonstrated on gene-level count data from several publicly available datasets involving haematopoietic stem cells, brain-derived cells, T-helper cells and mouse embryonic stem cells. This will provide a range of usage scenarios from which readers can construct their own analysis pipelines. Single-cell RNA sequencing scRNA-seq is widely used to measure the genome-wide expression profile of individual cells. This can be done using microfluidics platforms like the Fluidigm C1 Pollen et al.

The number of reads mapped to each gene is then used to quantify its expression in each cell. Alternatively, unique molecular identifiers UMIs can be used to directly measure the number of transcript molecules for each gene Islam et al. Count data are analyzed to detect highly variable genes HVGs that drive heterogeneity across cells in a population, to find correlations between genes and cellular phenotypes, or to identify new subpopulations via dimensionality reduction and clustering.

This provides biological insights at a single-cell resolution that cannot be achieved with conventional bulk RNA sequencing of cell populations. One technical reason is that scRNA-seq data are much noisier than bulk data Brennecke et al. Reliable capture i. This increases the frequency of drop-out events where none of the transcripts for a gene are captured.

limma workflow

Dedicated steps are required to deal with this noise during analysis, especially during quality control. In addition, scRNA-seq data can be used to study cell-to-cell heterogeneity, e. This is simply not possible with bulk data, meaning that custom methods are required to perform these analyses. This article describes a computational workflow for basic analysis of scRNA-seq data, using software packages from the open-source Bioconductor project release 3.

Starting from a count matrix, this workflow contains the steps required for quality control to remove problematic cells; normalization of cell-specific biases, with and without spike-ins; cell cycle phase classification from gene expression data; data exploration to identify putative subpopulations; and finally, HVG and marker gene identification to prioritize interesting genes.

The application of different steps in the workflow will be demonstrated on several public scRNA-seq datasets involving haematopoietic stem cells, brain-derived cells, T-helper cells and mouse embryonic stem cells, generated with a range of experimental protocols and platforms Buettner et al.

The aim is to provide a variety of modular usage examples that can be applied to construct custom analysis pipelines. To introduce most of the concepts of scRNA-seq data analysis, we use a relatively simple dataset from a study of haematopoietic stem cells HSCs Wilson et al.Below is my code for both the tests. Can someone tell me if what I am doing is correct or not? I do think that I am slightly mistaken in the voom code. Thank you, I still don't know if this is the best way i.

But at least this gives me some starting point. In general, I would only normalize against the spike-ins if absolutely needed there are definite cases where this is needed. They're generally less robust. Because the ERCC spike-ins are being used for normalization. This is needed in cases like single-cell sequencing or whenever else there might be transcriptional amplification.

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limma v1

Please log in to add an answer. Hi guys, I know this question mqybe look silly but it is taking me a lot of time. I have downoade Hello, I try to do the diff analysis by DESeq on two samples coming from two different conditio Hi, I have been provided 30 samples 15 pairs. Each pair has one cancer and one normal sample. I'm quite new to DESeq. I want to compare two treatments with only one replicate each. I have two conditi I only get two samples without replicates for the DEseq analysis,but the results look unnormal,mo The R code looks like this Hello I'm new to coding on Rstudio.

I'm doing a RNA seq analysis to test for differential gene Hi guys, fairly new to RNA-seq. It contains Use of this site constitutes acceptance of our User Agreement and Privacy Policy. Powered by Biostar version 2.It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes.

Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing RNA-seq data.

All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines.

Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences.

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This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described. Gene expression technologies are used frequently in molecular biology research to gain a snapshot of transcriptional activity in different tissues or populations of cells. These profiles are then compared to identify gene expression changes associated with a treatment condition or phenotype of interest.

Gene expression studies may be randomized designed experiments in which a biological system is perturbed, for example by a gene knock-out or by applying a specified stressor.

RNA-seq workflow: gene-level exploratory analysis and differential expression

Such experiments are amongst the most powerful tools in functional genomics, providing insights into normal cellular processes as well as disease pathogenesis. Or they may be observational studies in which different phenotypes are compared, diseased and normal tissue for example or cells from different populations. Such studies are common in cancer research and in the study of cell development. In either case, the study design can range from simple two group comparisons to complex set-ups with several experimental factors varying over multiple levels.

proteomics

Researchers might be interested for example in whether a particular gene facilitates or blocks the action of a particular drug, in which case knock-down and wild-type samples both with and without drug treatment would be profiled.

Observational studies may involve multiple batch effects and covariates that must be accounted for in the analysis. Despite the complexity, gene expression studies often involve only a small number of biological replicates. The small but complex nature of gene expression studies poses challenging statistical problems and motivates the use of a number of specialized statistical techniques in order to get the most out of each data set. We have developed the limma software over the past decade to provide a framework for analysing gene expression experiments from beginning to end in a flexible and statistically rigorous way.

The limma package is a core component of Bioconductor, an R-based open-source software development project in statistical genomics 12. It has proven a popular choice for the analysis of data from experiments involving microarrays 34high-throughput polymerase chain reaction PCR 5protein arrays 6 and other platforms.


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