差异分析，我该如何选择？limma DESeq edger

When it comes to choosing a differential expression analysis method, such as limma, DESeq, or edgeR, there are several factors to consider, including the nature of your data and the assumptions made by each method. Here's a brief overview of these methods to help you decide:

1. limma:

   • limma (Linear Models for Microarray Data) is widely used for analyzing differential expression in microarray and RNA-seq data.
   • It is robust for small sample sizes and can handle complex experimental designs.
   • limma uses a linear model approach and empirical Bayes methods to estimate gene-specific variances and to shrink variance estimates.
   • It is particularly useful when you have few replicates or a small sample size.

2. DESeq:

   • DESeq is specifically designed for RNA-seq data and is suitable for count-based data with biological replicates.
   • It uses a negative binomial distribution to model count data and estimate dispersion.
   • DESeq applies a normalization procedure to account for differences in sequencing depth between samples.
   • DESeq is recommended when you are dealing with RNA-seq data and need to account for the inherent variability in sequencing counts.

3. edgeR:

   • edgeR (Empirical analysis of Digital Gene Expression in R) is also tailored for RNA-seq data and works well with biological replicates.
   • It uses the negative binomial distribution and applies the TMM (Trimmed Mean of M values) normalization.
   • edgeR provides robust statistical methods for detecting differential expression and handles both common and tagwise dispersion estimates.
   • It is recommended for RNA-seq data, especially when you have few replicates.

In summary, the choice of differential expression analysis method depends on your data type (microarray or RNA-seq), the number of replicates, and your specific research question. If you have RNA-seq data with biological replicates, DESeq and edgeR are good choices. If you have microarray data or limited replicates, limma could be a suitable option.

It is also a good practice to explore the results obtained from different methods and consider their consistency to gain more confidence in the identified differentially expressed genes. Additionally, there are other R packages and methods available, so it's essential to stay up-to-date with the latest developments and publications in the field.

The limma package uses a t-test-like approach for testing differential expression in microarray data. However, it is important to note that limma uses a moderated t-statistic, which incorporates information from all genes to provide more stable and robust estimates of differential expression.

The moderated t-statistic in limma is based on the empirical Bayes method, where information is borrowed across genes to estimate the variance and to increase the power of the tests, especially for genes with low sample sizes. This makes the analysis more reliable in situations where there are a limited number of samples or replicates.

In contrast, DESeq and edgeR use count-based statistics with a negative binomial distribution to model the RNA-seq data. They do not rely on a t-test directly. Instead, they estimate gene-wise dispersion and perform likelihood ratio tests (LRTs) to identify differentially expressed genes.

Each of these methods has its advantages and is suitable for different types of data and experimental designs. If you have microarray data and are interested in using a t-test-based approach with improved performance for small sample sizes, limma is a good choice. On the other hand, if you have RNA-seq data, DESeq and edgeR are more appropriate, as they are specifically designed for count-based data and account for the unique characteristics of RNA-seq experiments.

The three methods, limma, DESeq, and edgeR, are based on different statistical principles and assumptions due to the nature of the data they are designed to handle (microarray or RNA-seq) and the models they employ. Here's a brief explanation of their key differences:

1. limma (Linear Models for Microarray Data):

   • limma is primarily designed for analyzing microarray data, but it can also be used for RNA-seq data.
   • It uses a linear model approach to estimate the fold changes and test for differential expression.
   • Empirical Bayes methods are used to shrink the variance estimates, especially for genes with low sample sizes.
   • limma assumes that the gene expression data follow a normal distribution.
   • It is well-suited for small sample sizes and complex experimental designs, such as those with multiple factors or covariates.

2. DESeq (Differential Expression analysis for SEQuencing data):

   • DESeq is specifically designed for RNA-seq data, which typically involves count-based data from high-throughput sequencing experiments.
   • It uses a negative binomial distribution to model the count data, which is more appropriate for dealing with the over-dispersion commonly observed in RNA-seq data.
   • DESeq applies normalization procedures, such as size factors and dispersion estimation, to account for differences in library size and to stabilize variance estimates.
   • It assumes that the gene counts follow a negative binomial distribution, and it is particularly well-suited for detecting differential expression with limited biological replicates.

3. edgeR (Empirical analysis of Digital Gene Expression in R):

   • edgeR, like DESeq, is tailored for RNA-seq data and uses count-based statistics.
   • It also employs a negative binomial model for count data and applies TMM normalization to account for library size differences.
   • edgeR offers a robust statistical framework to estimate dispersion, and it allows for both common and tagwise dispersion estimation, providing flexibility in handling various data scenarios.
   • Similar to DESeq, it assumes that the gene counts follow a negative binomial distribution and is suitable for RNA-seq experiments with biological replicates.

In summary, the key differences among these methods lie in the models used to represent the data (normal distribution for limma vs. negative binomial distribution for DESeq and edgeR) and the normalization procedures applied to account for technical variations. Choosing the appropriate method depends on the data type (microarray or RNA-seq) and the specific characteristics of your dataset, such as the number of replicates and the level of dispersion observed in the data. It is recommended to explore and compare the results obtained from different methods to gain confidence in the identified differentially expressed genes.

In proteinomics, the choice of differential expression analysis method depends on the type of data and the experimental design. If you are analyzing proteomics data generated from mass spectrometry-based techniques, such as shotgun proteomics or data-dependent acquisition (DDA), the data are typically represented as peptide or protein abundances. In such cases, the appropriate methods to use are often limma or edgeR.

1. Limma: Limma is commonly used for analyzing proteomics data when it is transformed into a gene-like format, such as using protein-level summarization or using unique peptides to represent proteins. Limma's moderated t-statistic and empirical Bayes approach can be beneficial for handling the challenges of limited sample sizes and providing more stable and reliable estimates of differential expression.

2. EdgeR: EdgeR is another popular choice for analyzing proteomics data, especially when there are complex experimental designs, multiple factors, and batch effects. EdgeR uses a negative binomial model to account for overdispersion often observed in count-based data, and it can be adapted for proteomics data with appropriate normalization and transformation.

It is important to note that while both limma and edgeR can be used for proteomics data, the data preprocessing and normalization steps may differ from those used for RNA-seq data. Additionally, other specialized software or workflows may also be available for analyzing proteomics data, depending on the specific platform or experimental setup.

For more accurate guidance, it is recommended to consult published literature and bioinformatics resources that are specific to proteomics data analysis in your field of interest.