Limma logfc interpretation. logFC: Identifies genes with la...

  • Limma logfc interpretation. logFC: Identifies genes with largest fold-changes, but may include unstable estimates from low-expressed genes. Limma-voom is our tool of choice for DE analyses because it: Allows for incredibly flexible model specification Overview limma is a very popular package for analyzing microarray and RNA-seq data. wt), or refer to treatment (3-fold upregulation in stimulated vs. 3 the former example. This will be a section of the Limma User's Guide in the next release. 在生物信息学中，limma（线性模型用于微阵列数据）是一个常用的R包，用于差异表达分析。 logFC（对数折叠变化）是limma输出的一个重要指标，它表示在不同条件下基因表达水平的变化。 以下是limma中logFC的计算方法和相关概念： 1. If your data is already log transformed then the logFC (log of fold change not fold change of the logs) is computed as ave_prog - ave_stable. Your volcano plot looking "normal" (logFC -2 to 2 range) is a good sign—extreme logFCs (>4–5) are rarer in well-powered RNA-seq, especially for subtle effects. > > When I convert the FC values (test/blank) to foldchanges using IF function > I get lesser number of genes to be deregulated. It consists of two components: the left component is an average Fold Change value across all case samples; the right one indicates the interval with the majority of fold changes within each sample 2 Regarding my initial question ? That the case both intervals to be >1 if a logFC > 0 or <1 if logFC <0 is "incorrect" ? And i should primarily see my undjusted p-value ( as also initial adjusted p-value with a logFC of course # than zero), in order to make any comments of the confidence intervals ? Using limma-trend will result in a much closer relationship between the raw group means and the output logFC, because limma-trend will fit linear models directly to the values that you supply, instead of trying to reprocess them as if they were counts. Perhaps unsurprisingly, limma contains functionality for fitting a broad class of statistical models called “linear models”. The limma computation is generally closer to the true log-fold-change (in the sense of minimising the expected squared error) than is the estimator one would get by logging your formula for the FC. The value is then elevated to 2 and log transform, to address correctly the difference between the groups. Normally you want to use much smaller values for lfc than 2, something like lfc=log2 (1. 0. , CPM or similar). Hi all, Being new to 450k data analysis, there are certain output terms that I don't understand. A very direct interpretation is to take the values as positive, independent of their sign (i. This page covers models for two color arrays in terms of log-ratios or for single-channel arrays in terms of log-intensities. Regards, Joern Ingrid H. unstimulated), or to both (upregulation in mutant-vs-wt AND stimulated-vs-unstimulated)? Nov 6, 2025 · Quick Recap of limma-voom and logFC Interpretation voom transformation: In limma-voom (from the limma package), your input is typically raw or normalized counts (e. I believe this is what limma does. limma MultidimensionalScaling • 3. whether the gene is being up regulated or down regulated from pre to post or post to pre. Our alternative hypothesis is: (H1) The mean protein abundance differs across cell cycle stages. voom then applies a log2 transformation (roughly log2 (count + offset)) to stabilize variance across expression levels. t-statistic: Pure statistical evidence without Bayesian prior assumptions. Is LIMMA used wrongly? And how to I combine the different data from each coefficient, sind they result in different row numbers Thank you very much! LogFC is log2 > of FC values. Would that logFC value refer to genotype (3-fold upregulation in mutant vs. AveExpr – this column shows the average normalised expression level across all samples in the design matrix. The logFC of dose 100 vs dose 0 is a sort of overall logFC because it measures the effect of the highest dose vs the lowest. I've run a paired limma analysis trying to look at differentially expressed genes in a pre-post experiment design. 首先，我们要理解foldchange的意义，如果case是平均表达量是8，control是2，那么foldchange就是4，logFC就是2咯 那么在limma包里面，输入的时候case的平均表达量被log后是3，control是1，那么差值是2，就是说logFC就是2。 Whatever the linear you fit, the logFC is the estimated coefficient for that gene and that covariate. logFC is the estimated value, whereas lfc gives the threshold against which logFC is tested. > > The LogFC (2) is similar to LogFC (1), for example 3. I compared the logFC generated by LIMMA of some genes with the logFC that i have calculated before (which are definitely right) and they are different. G. P-value: Emphasizes statistical significance, may include small but very stable fold-changes. 2015. limma computes logFC by fitting a linear model to the log-expression values. ?stensen wrote: > Hi > > I am using limma to analyze Illumina expression data (two groups), and this time I got some really high logFC values for some genes and "low" for others. g. Please see the Statistical analysis section for more details about limma and its application in quantitative proteomics. 5) is common. limma is an R package that was originally developed for differential expression (DE) analysis of gene expression microarray data. . Hi All, a question about the interpretation of the logFC in limma. We will use limma to perform the statistical tests using an empirical Bayes-moderated linear model. > 2- Is it possible to run multiple regressions in limma? The purpose of this tutorial is to demonstrate how to perform differential expression on count data with limma-voom. In other words contrast A-B is log (A/B), regardless of the sign. Using limma-trend will result in a much closer relationship between the raw group means and the output logFC, because limma-trend will fit linear models directly to the values that you supply, instead of trying to reprocess them as if they were counts. Follow Limma procedures, I had a list from topTable with LogFC (1) and other columns. Regarding my initial question ? That the case both intervals to be >1 if a logFC > 0 or <1 if logFC <0 is "incorrect" ? And i should primarily see my undjusted p-value ( as also initial adjusted p-value with a logFC of course # than zero), in order to make any comments of the confidence intervals ? This is to respond to a number of questions about the interpretation of the moderated t and B-statistics in limma. In addition, I produced LogFC for each individual arrays from MAlist, the average LogFC (2) were then taken from 7 arrays to compare LogFC (1). I have a microarray experiment with a common reference (Cy5) design. Hi, I'm using limma package to do differential expression analysis on a microarray data. Finally, note that limma doesn’t do anything different from a difference of means when it computes logFC; all the statistical improvements centers on computing better t-statistics and p-values. Why not limma? Complexity Reliance on normal theory Can't t linear mixed models Can't handle multiple levels of technical replication Using limma-trend will result in a much closer relationship between the raw group means and the output logFC, because limma-trend will fit linear models directly to the values that you supply, instead of trying to reprocess them as if they were counts. Are you sure that you need all of those covariates in your design formula? XIST comes up likely due to gender-specific effects. However, I have checked the logFC that appears in the topTable obtained with the functions in limma and the logFC seems to be something else. Since running limma requires memorizing several commands that are not easy to remember, here we define a simple wrapper function that will relief us from remembering them all. 2 years ago by Katharina • 0 3 Gordon Smyth 53k limma-voom often pairs with edgeR's TMM (trimmed mean of M-values) for between-sample normalization, which adjusts for library size/composition without altering the logFC interpretation. Together they allow fast, flexible, and powerful analyses of RNA-Seq data. , up-regulated in A vs B), and vice versa. e. The file used here was generated from limma-voom but you could use a file from any RNA-seq differential expression tool, such as edgeR or DESeq2, as long as it has the required columns (see below). I don't know what kind of data preprocessing you performed but you may want to make sure that you feed log-transformed data into lmFit. logFC logarithmic Fold Change value − indicates the difference of gene expression in a case vs Controls sample groups. LIMMA stands for “linear models for microarray data”. Can someone please explain me what we can infer from logFC, AvgExp and B in 450k methylation result output while using limma and how these values are calculated from Beta/M-values? Do these values have any significance while inferring the 450K result? Quick Recap of limma-voom and logFC Interpretation voom transformation: In limma-voom (from the limma package), your input is typically raw or normalized counts (e. If it's positive, it means the average for group A is larger than group B (e. The logFC of log2 transformed values are (approximately) the difference in the group means, which is indeed ~1. voom is a function in the limma package that modifies RNA-Seq data for use with limma. The design is ~0+ treatment, which seems quite normal, any possible reasons? It does work well for other data, quite strange Many thanks! limma-voom often pairs with edgeR's TMM (trimmed mean of M-values) for between-sample normalization, which adjusts for library size/composition without altering the logFC interpretation. Your email sounds that like you instead wanted to fit a linear trend to the log expression values as a function of dose (or of log dose perhaps). 2, but they're not exactly the same. Even the title of the package tells you that! The linear model computation takes into account precision weights, inter-gene correlation, missing values and multiple model terms. The LogFC (2) is similar to LogFC (1), for example 3. 12 vs 3. Dec 8, 2025 · Incorporates prior information about the proportion of DE genes. limma-voom often pairs with edgeR's TMM (trimmed mean of M-values) for between-sample normalization, which adjusts for library size/composition without altering the logFC interpretation. If Limma output is a true LogFC, using R, how can I convert a LogFC into Log2FC? I am also very new to this type of analysis so any short or deep answers will be greatly appreciated. This page gives an overview of the LIMMA functions available to fit linear models and to interpret the results. While most of the functionality of limma has been The logFC of log2 transformed values are (approximately) the difference in the group means, which is indeed ~1. the "absolute values"). I'm trying to understand what direction my logFC values represent i. The criteria was =>2 > foldchanges and =<-2 fold changes. Mouse mammary gland dataset The data for this tutorial comes from a Nature Cell Biology paper by Fu et al. 2k views ADD COMMENT • link updated 17 months ago by James W. BTW, 'logFC' in the topTreat table and 'lfc' are both abbreviations for the same thing, log2-fold-change. Intro limma is an R package that was originally developed for differential expression (DE) analysis of microarray data. 35 in your last example and ~7. Here, the logFC represents the difference between the mean of the groups. The sign of the statistic doesn't change the relationship. You can use what limma reports for everything, including volcano plots. How to generate counts from reads (FASTQs) is covered in the accompanying tutorial RNA-seq reads to counts. May I know how the logFC is computed in the package? Dear guys, Do you have any experience when using limma for one batch of proteomics data, the log2FC is surprisingly high (1500-3000), but when checking the expression matrix, the value among the samples are pretty similar. In the post you link to I describe in a comment why this is done in every package that I know. > > My question is that why aren't these two columns of value the same? The logFC of log2 transformed values are (approximately) the difference in the group means, which is indeed ~1. MacDonald 68k • written 3. Examples of such models include linear regression and analysis of variance. I don't know the exact calculations that are being used by limma, though. If logFC is a vector of log fold-changes, then abs (logFC) will return the A logFC=1 indicates a 2-fold upregulation, logFC=2 is a 4-fold upregulation, logFC=(-3) is an 8-fold down-regulation etc. e0pq, 1r5ob, fxhkl, afwbg, k1swa, mrrk, ou5hi, n4so, drrie, vkfhjq,