2018年1月5日 · if LogFC if 0.05, then your actual fold change is 1.0353... which is effectively 1, or rather, no significant change. To convert a logFC value, simply use it as the exponent of two: 2 logFC. In Excel, use the function "=2 x". To convert a FC value, take the log2. In Excel, use function: "=log(x,2). (where x = the cell with your data). Hope that ...

2023年8月26日 · Increasing log2(?)-FC from 0.5 to 2 will diminish the number of genes, not increase. Most tend to go with logFC of 0.26-0.58 which represents a FC of 1.2 and 1.5 respectively. I personally go with 1.2 because a 20% change in expression could reasonably result in biologically meaningful effects.

2020年5月30日 · If i take logFc > 1 , there is a significant number . But how can i say that those genes are differentially expressed, they can be in their normal range of expression. Just because I'm changing my cutoff from 1.5 to 1 it would mean something or it will be another mistake I'll be doing to consider them differentially expressed where in case they ...

2022年5月18日 · If you don’t have access to the code and someone else ran in it then you can try the following: use the normalized table, take the mean for each group to get 2 vectors, log2 transform the mean vectors, do disease - healthy, then plot the logfc values against what you just computed. They shouldn’t be exact but you should see a trend.

2023年3月13日 · This is the correct answer. MA plots were initially developed for microarrays, where M is a symbol for log ratio (or log2 fold change) and A is the mean average of log-transformed counts/signal.

2022年8月1日 · I’ve used this metric, but more recently switched over to using -log(raw p value)*sign(logFC) because I generally have a lot of ties in the adjP values (which leads to arbitrary rankings). I recall reading this being recommended by the author of the fgsea package but I can’t find the biostars post.

2021年12月1日 · Hello, Previously when using fgsea I have ran it using genes based on a composite score: -log10(padj) * log2fc.

2021年6月28日 · This is available under lFC.Av, but under logFC, I get back another value. According to the test.results definition, it is the "log fold change estimated by fitting the given GLM model. The reference level of the main factor is taken as control".

2018年1月5日 · Posted by u/AnonBiomed - 5 votes and 7 comments

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