Description Usage Arguments Details Value Author(s) References See Also Examples
This function produces a MD (mean-difference) plot that the user can search for genes in, with a second panel showing either the individual expression values or the trend of variability versus abundance (SA plot).
1 2 3 |
object |
|
y |
a |
groups |
character vector or factor specifying the experimental groups (used to separate expression values for plotting). |
genes |
data.frame of gene annotation information |
p.value |
numeric value between 0 and 1 giving the desired size of the test. |
lfc |
minimum log2-fold-change required to highlight points. |
adjust.method |
method used to adjust the p-values for multiple testing. Options, in increasing conservatism, include |
labels |
character vector specifying sample labels. |
coef |
numeric scalar indicating which coefficient from the linear model to make an MA plot of (defaults to first coefficient). |
baseURL |
character string specifying URL to use to link out to external gene ID information. |
searchBy |
character string specifying column from gene annotation data.frame to allow searching by in plot. |
linkBy |
character string specifying the column name from the gene annotation data.frame to use as key at specified |
dir |
character string indicating the output directory for the plot. |
launch |
|
main |
character string specifying the title of the html page. |
This function generates an interactive MD plot.
A html page with a searchable MD plot, with a second panel displaying either the expression values for a selected gene or an SA plot summarising variability as a function of abundance with the particular gene of interest highlighted. A searchable table of results for the top genes ranked genes is also displayed below the 2 plot panels.
Shian Su, Matt Ritchie
Ritchie, M.E., Phipson, B., Wu, D., Hu, Y., Law, C.W., Shi, W., and Smyth, G.K. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research 43, doi: 10.1093/nar/gkv007.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | ## Not run:
library(Glimma)
library(edgeR)
load("x.rda")
# RNA-seq data set available from GEO under accession number GSE64099
# filter out genes with low read counts
sel = rowSums(cpm(x$counts)>0.5)>=3
x = x[sel,]
x$genes = x$genes[,c(1,3)]
des = model.matrix(~x$samples$group)
colnames(des)[2] = "Smchd1nullvsWt"
x = calcNormFactors(x, method="TMM")
genotype = x$samples$group
# See how replicate sample cluster
interactiveMDSplot(x, labels=1:7, col=as.numeric(genotype), main="MDS plot for Smchd1 experiment")
# Apply voom with sample quality weights
v = voomWithQualityWeights(x, design=des, normalization="none", plot=TRUE)
vfit = lmFit(v)
vfit = eBayes(vfit)
topTable(vfit,coef=2,sort.by="P")
# Make interactive MA plot of results for coefficient of interest (Smchd1 null vs Wt)
interactiveMDplot(vfit, v, groups=genotype, coef=2, main="Smchd1 null vs Wt")
## End(Not run)
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