Explanation of limma Output Columns

The output file of limma typically includes the following columns:

  1. logFC (logarithm of fold change):
    • This column represents the logarithm of the fold change in gene expression between two groups. It indicates the magnitude and direction of the change in expression. A positive logFC value suggests upregulation, while a negative value suggests downregulation.
  2. AveExpr (average expression):
    • AveExpr represents the average expression level of a gene across all samples. It provides information about the overall expression level of the gene, irrespective of group membership.
  3. t (t-statistic):
    • The t column contains the t-statistic, which is a measure of the difference in gene expression between groups normalized by the variability in the data. Higher absolute values of the t-statistic indicate greater evidence of differential expression.
  4. P.Value (p-value):
    • P.Value is the p-value associated with the t-statistic. It quantifies the probability of observing the observed level of differential expression or more extreme under the null hypothesis (i.e., no differential expression). Smaller p-values indicate stronger evidence against the null hypothesis.
  5. adj.P.Val (adjusted p-value):
    • The adj.P.Val column contains the adjusted p-values, which are corrected for multiple testing. Multiple testing correction is important when conducting multiple hypothesis tests to control the overall false discovery rate (FDR). The adjusted p-values help mitigate the risk of false positives.
  6. B (log-odds of differential expression):
    • B represents the log-odds of differential expression and is related to the adjusted p-value. It is used in the context of the empirical Bayes moderation of the standard errors. Positive B values indicate evidence in favor of differential expression.

In summary, these columns provide information about the fold change, average expression, statistical significance, and direction of differential expression for each gene in the limma analysis output. Researchers often use these values to prioritize and interpret the results of gene expression studies.

Explanation of DESeq2 Output Columns

The output file of DESeq2 typically includes the following columns:

  1. baseMean:
    • The baseMean represents the average expression level of a gene across all samples after accounting for library size differences. It is a measure of the average expression level and serves as a baseline for comparisons.
  2. log2FoldChange:
    • The log2FoldChange column contains the logarithm (base 2) of the fold change in gene expression between two groups. It indicates the magnitude and direction of the change in expression. A positive value suggests upregulation, while a negative value suggests downregulation.
  3. lfcSE (log2 fold change standard error):
    • The lfcSE column represents the standard error of the log2FoldChange. It quantifies the uncertainty or variability associated with the estimated fold change. Smaller lfcSE values indicate more precise estimates.
  4. stat (Wald statistic):
    • The stat column contains the Wald statistic, a measure of the difference in gene expression normalized by its standard error. It is used to test the null hypothesis that the log2FoldChange is equal to zero. Larger absolute values indicate stronger evidence against the null hypothesis.
  5. pvalue:
    • The pvalue column represents the unadjusted p-value associated with the Wald statistic. It quantifies the probability of observing the estimated log2FoldChange or more extreme under the null hypothesis. Smaller p-values indicate stronger evidence against the null hypothesis.
  6. padj (adjusted p-value):
    • The padj column contains the adjusted p-values, which are corrected for multiple testing using methods like the Benjamini-Hochberg procedure. Smaller padj values indicate stronger evidence of differential expression while accounting for multiple comparisons.

In summary, these columns provide information about the average expression, fold change, statistical significance, and direction of differential expression for each gene in the DESeq2 analysis output. Researchers often use these values to prioritize and interpret the results of differential expression analyses.

Comparison of limma and DESeq2 Output Terms

When analyzing differential gene expression, both limma and DESeq2 generate outputs with similar information, but the column names may differ. Here’s a comparison between the output terms:

1. Fold Change

limma (limma)

  • logFC: Logarithm of the fold change in gene expression between two groups. Sign indicates the direction of change.

DESeq2

  • log2FoldChange: Logarithm (base 2) of the fold change in gene expression between two groups. Sign indicates the direction of change.

2. Average Expression

limma

  • AveExpr: Average expression level of a gene across all samples.

DESeq2

  • baseMean: Average expression level of a gene across all samples after accounting for library size differences.

3. Statistical Test Statistic

limma

  • t: t-statistic, a measure of the difference in gene expression normalized by the variability in the data.

DESeq2

  • stat: Wald statistic, a measure of the difference in gene expression normalized by its standard error.

4. P-Value

limma

  • P.Value: p-value associated with the t-statistic.

DESeq2

  • pvalue: Unadjusted p-value associated with the Wald statistic.

5. Adjusted P-Value

limma

  • adj.P.Val: Adjusted p-values, corrected for multiple testing.

DESeq2

  • padj: Adjusted p-values, corrected for multiple testing using methods like the Benjamini-Hochberg procedure.

6. Log-Odds of Differential Expression

limma

  • B: Log-odds of differential expression, related to the adjusted p-value.

DESeq2

  • Not directly provided. Focus is often on log2FoldChange and associated statistics.

In summary, although the column names differ, the terms in limma and DESeq2 output serve similar purposes, providing information about fold change, average expression, statistical significance, and direction of differential expression for each gene in the analysis output.

Limma and DESeq2 are both widely used tools for the analysis of differential gene expression in high-throughput sequencing data, but they differ in their underlying statistical methods and assumptions. Here are some key differences and similarities between the output from limma and DESeq2:

Differences:

  1. Statistical Methods:
    • limma: Uses a linear modeling approach and empirical Bayes moderation to estimate gene-wise variances and test for differential expression. It is based on a normal distribution assumption.
    • DESeq2: Utilizes a negative binomial distribution model and employs shrinkage estimators for dispersion. It incorporates a method called variance stabilizing transformation to stabilize the variance across the mean expression values.
  2. Data Assumptions:
    • limma: Assumes that the variances of gene expression are similar across samples.
    • DESeq2: Does not assume equal variances but models the variance as a function of the mean expression.
  3. Normalization:
    • limma: Often applied to normalized counts or log-transformed counts.
    • DESeq2: Includes normalization steps to account for differences in sequencing depth and composition biases.
  4. Fold Change Interpretation:
    • limma: Provides log-fold changes in gene expression.
    • DESeq2: Provides log2-fold changes in gene expression.
  5. Handling of Low Counts:
    • limma: Employs a moderated t-statistic that borrows information across genes to provide stable estimates even for genes with low counts.
    • DESeq2: Utilizes a negative binomial distribution that is well-suited for handling count data, especially when dealing with genes with low expression.

Similarities:

  1. Output Structure:
    • Both limma and DESeq2 produce similar output structures, including columns such as log-fold change, p-values, and adjusted p-values.
  2. Adjustment for Multiple Testing:
    • Both methods incorporate adjustments for multiple testing, such as the Benjamini-Hochberg correction, to control the false discovery rate (FDR).
  3. Application:
    • Both are widely used for the analysis of RNA-seq and high-throughput sequencing data to identify differentially expressed genes.
  4. Bioconductor Packages:
    • Both limma and DESeq2 are R packages available on the Bioconductor platform.

It’s important to note that the choice between limma and DESeq2 may depend on the specific characteristics of the dataset, the assumptions that align with the data, and the preferences of the researcher. In practice, it is not uncommon for researchers to apply both methods to their data and compare results to gain more confidence in the findings.