GeneExpressionAnalysisLimma
Thomas Källman
28 Apr 2015
Introduction
This files describes the analysis in R, going from a coount table that includes one column for each library and rows for each gene, like the one generated by the genome platform as part of their “best practice” analaysis. The analysis also requires a design matrix file that holds information on the sample name and treatment for each of these samples.
Loading needed packages and data
# Reading in the needed packages
library(limma) # For DE detection
library(edgeR) # For DE detection
library(biomaRt) # For intereacting with the biomart database via R. This makes it feasible to convert/extract info from the ensebl gene ids
rm(list = ls()) # remove any objects to avoid any issues downstreamscounts <- read.table("../../Data/count_table_new.txt") # reads in the new data and save the data frame as counts with ensembl id from BDGP5 as row.names
# Remove all genes that have 0 reads over all samples and hence are not expressed at all under these conditons note that this filtering can be further increased to looking at DE below as large number of lowlly expressed gene eg. CPM values below 0 is likely very noisy and not very rewarding to retain for identification of downstream targets.
A <- rowSums(counts)
isexpr <- A > 0
counts <- counts[isexpr,] # this drops 2696 genes that have 0 expression from the counts dataframe
design <- read.table("../designMatrix", header=T) # Create model matrix for Limma/edgeR analysis
ensembl = useMart("ENSEMBL_MART_ENSEMBL",host="feb2014.archive.ensembl.org") # use ensembl version that holds the BDGP5 version of the Dme genome annotation
ensembl = useDataset('dmelanogaster_gene_ensembl', mart=ensembl)
gene.list <- getBM(attributes = c("ensembl_gene_id", "external_gene_id", "chromosome_name", "start_position", "end_position", "strand"), filters = "ensembl_gene_id", values = row.names(counts), mart=ensembl) # NB! Needs internet access and collect all requested features from using the ensembl_gene_id as query
gene.list.ordered <- gene.list[order(gene.list$ensembl_gene_id),] # orders the gene.list to be identical to the counts and hence allow to bind them together using cbind. If not order one need to match the common columns so that orderd is inferred during merging see ?merge for more info
annotated.count.data <- cbind(gene.list.ordered, counts)
temp <- cpm(annotated.count.data[,7:28])
temp.log <- cpm(annotated.count.data[,7:28], log=TRUE) # get the counts per million using colsums of counts as library size and reports the log2 values here.
annotated.count.cpm <- cbind(annotated.count.data, temp, temp.log) # Save up all info in one large data frame and save the dataframe as an .csv file suitable to read into R
write.csv(annotated.count.cpm, file="Expression_matrix.csv", row.names=F)
y <- DGEList(counts=counts, group=design$Treatment) # Converts the count matrix to DGEList, objects that works nice in both Limma and edgeR packagesDetect differential gene expression
Limma - A first look at the data
The Limma package, which originally was developed for microrray analysis, but have been modifed to also work with RNA-seq count data, will be used. It uses linear models to assess differential gene expression and has in comparative tests proven to be very reliable. It also allows for analysis of more complex experimental designs and even with limited number of replicates it give robust results (as it shares information across genes for to increase power).
A <- rowSums(y$counts)
isexpr <- A > 22
# perform additional filtering not retaining any gene that over all samples have less than 22 reads mapping eg. average mapping of 1 read/library. In practice this removes genes that have lots of zeros in the count matrix
y.limma <- y[isexpr,,keep.lib.size = FALSE]
y.limma <- calcNormFactors(y.limma)
gene.names.limma <- gene.list.ordered[gene.list.ordered$ensembl_gene_id %in% row.names(y.limma),1:2]
# Create design matrix
lev <- unique(design$Treatment)
f <- factor(design$Treatment, levels=lev)
des <- model.matrix(~0+f)
colnames(des) <- lev
# Transform count data for Linear modelling using Limma
v <- voom(y.limma, des)
plotMDS(v, labels = design$Treatment) # Do a Multidimensional plot to look at differences in geneexpression between samples, can be interpreted in similar ways as a PCA plo, eg. clustering represents similarity
shortname <- c("empty", "empty", "WT", "WT", "WT", "K804", "K804", "K804", "YN", "YN", "YN", "empty", "empty", "WT", "WT", "WT", "K804", "K804", "K804", "YN", "YN", "YN")
plotMDS(v, dim=c(3,4), labels = shortname)
The first dimension (the largest distance) in plot one clearly highlight that samples ending with 100 looks different (focus on the differentiation seen along the x-axis). The second dimension seperates three main groups: The WT and YN without 100 in name, the WT and YN with 100 in name and the empty and K804R samples. In the second plot four clusters can be easily identified: Empty, WT, YN, K804 showing that there is a gene expression difference between these groups. All of this suggest that we should be able to detect differences in gene expression between some of the groups/treatments. We can also based on this hypothesiss that the largest effect is 100 and a few other contrast like the different “empty” types are very similar and unlikely to many signicant genes in terms of differential gene expression.
Limma - Model and detect differential gene expression
fit <- lmFit(v, design = des) # Uses a linear model to fit gene expression for all genes according to the design given in the design matrix named des
# contrast empty_dsGFP vs empty_dsBrm
empty_dsGFP.vs.empty_dsBrm <- makeContrasts(empty_dsGFP - empty_dsBrm, levels = des)
fit.empty_dsGFP.vs.empty_dsBrm <- contrasts.fit(fit, empty_dsGFP.vs.empty_dsBrm)
fit.empty_dsGFP.vs.empty_dsBrm <- eBayes(fit.empty_dsGFP.vs.empty_dsBrm)
fit.empty_dsGFP.vs.empty_dsBrm$genes <- gene.names.limma # add common gene names to the object
results.fit.empty_dsGFP.vs.empty_dsBrm <- decideTests(fit.empty_dsGFP.vs.empty_dsBrm)
table(results.fit.empty_dsGFP.vs.empty_dsBrm)## results.fit.empty_dsGFP.vs.empty_dsBrm
## -1 0 1
## 1 9599 1volcanoplot(fit.empty_dsGFP.vs.empty_dsBrm, highlight = 2, names = fit.empty_dsGFP.vs.empty_dsBrm$genes[,2], main = "empty_dsGFP vs empty_dsBrm")
topTable(fit.empty_dsGFP.vs.empty_dsBrm, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr
## FBgn0041604 FBgn0041604 dlp -1.4881236 5.165021
## FBgn0030596 FBgn0030596 CG12398 1.6285694 4.245533
## FBgn0000212 FBgn0000212 brm 1.3211861 10.532912
## FBgn0032192 FBgn0032192 CG5731 -1.3782049 3.423886
## FBgn0015371 FBgn0015371 chn 1.6680509 2.706613
## FBgn0040091 FBgn0040091 Ugt58Fa 0.7651813 8.240297
## FBgn0034117 FBgn0034117 CG7997 -1.5741785 4.847735
## FBgn0262983 FBgn0262983 CG43291 1.4025430 3.179732
## FBgn0040071 FBgn0040071 tara -1.4144868 6.038023
## FBgn0062978 FBgn0062978 CG31808 -1.0481532 2.805649
## t P.Value adj.P.Val B
## FBgn0041604 -7.612594 1.343560e-06 0.0105550 5.438877
## FBgn0030596 7.312381 2.198728e-06 0.0105550 4.959205
## FBgn0000212 5.598806 4.617562e-05 0.1068445 2.266381
## FBgn0032192 -5.370446 7.139490e-05 0.1068445 1.826236
## FBgn0015371 5.619156 4.443166e-05 0.1068445 1.820567
## FBgn0040091 5.288904 8.355173e-05 0.1068445 1.694920
## FBgn0034117 -5.115663 1.170153e-04 0.1068445 1.395807
## FBgn0262983 5.154997 1.083644e-04 0.1068445 1.348982
## FBgn0040071 -5.092210 1.225095e-04 0.1068445 1.342788
## FBgn0062978 -5.072508 1.273305e-04 0.1068445 1.318585write.fit(fit.empty_dsGFP.vs.empty_dsBrm , results = results.fit.empty_dsGFP.vs.empty_dsBrm , file="empty_dsGFP_vs_empty_dsBrm.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast empty_dsGFP vs WT_GFP
empty_dsGFP.vs.WT_dsGFP <- makeContrasts(empty_dsGFP - WT_dsGFP, levels = des)
fit.empty_dsGFP.vs.WT_dsGFP <- contrasts.fit(fit, empty_dsGFP.vs.WT_dsGFP)
fit.empty_dsGFP.vs.WT_dsGFP <- eBayes(fit.empty_dsGFP.vs.WT_dsGFP)
fit.empty_dsGFP.vs.WT_dsGFP$genes <- gene.names.limma # add common gene names to the object
results.fit.empty_dsGFP.vs.WT_dsGFP <- decideTests(fit.empty_dsGFP.vs.WT_dsGFP)
table(results.fit.empty_dsGFP.vs.WT_dsGFP)## results.fit.empty_dsGFP.vs.WT_dsGFP
## -1 0 1
## 471 8569 561topTable(fit.empty_dsGFP.vs.WT_dsGFP, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr t
## FBgn0051361 FBgn0051361 dpr17 -2.475920 5.651741 -17.58646
## FBgn0041183 FBgn0041183 TepI 6.179907 3.487013 17.54839
## FBgn0085446 FBgn0085446 CG34417 3.476569 5.274943 15.93526
## FBgn0261673 FBgn0261673 nemy 2.900370 6.498878 15.70986
## FBgn0038299 FBgn0038299 Spn88Eb 6.607302 3.644801 16.60457
## FBgn0261451 FBgn0261451 trol -3.351690 8.436969 -15.19561
## FBgn0013773 FBgn0013773 Cyp6a22 -3.453257 4.385441 -14.76692
## FBgn0033374 FBgn0033374 CG13741 7.759078 2.141350 17.58799
## FBgn0086251 FBgn0086251 del 2.563562 6.648684 12.82692
## FBgn0038098 FBgn0038098 CG7381 2.406314 4.958387 12.28652
## P.Value adj.P.Val B
## FBgn0051361 1.323371e-11 4.372299e-08 16.94719
## FBgn0041183 1.366201e-11 4.372299e-08 15.96965
## FBgn0085446 5.600247e-11 1.075359e-07 15.56825
## FBgn0261673 6.889957e-11 1.102508e-07 15.36401
## FBgn0038299 3.072549e-11 7.374887e-08 14.99770
## FBgn0261451 1.116701e-10 1.531635e-07 14.88935
## FBgn0013773 1.688914e-10 2.026908e-07 14.15714
## FBgn0033374 1.321682e-11 4.372299e-08 13.96245
## FBgn0086251 1.264742e-09 1.214279e-06 12.45990
## FBgn0038098 2.320199e-09 1.821569e-06 11.87951volcanoplot(fit.empty_dsGFP.vs.WT_dsGFP, highlight = 10, names = fit.empty_dsGFP.vs.WT_dsGFP$genes[,2], main = "empty_dsGFP vs WT_dsBrm")
write.fit(fit.empty_dsGFP.vs.WT_dsGFP , results = results.fit.empty_dsGFP.vs.WT_dsGFP , file="empty_dsGFP_vs_WT_dsGFP.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast empty_dsGFP vs WT_dsBrm
empty_dsGFP.vs.WT_dsBrm <- makeContrasts(empty_dsGFP - WT_dsBrm, levels = des)
fit.empty_dsGFP.vs.WT_dsBrm <- contrasts.fit(fit, empty_dsGFP.vs.WT_dsBrm)
fit.empty_dsGFP.vs.WT_dsBrm <- eBayes(fit.empty_dsGFP.vs.WT_dsBrm)
fit.empty_dsGFP.vs.WT_dsBrm$genes <- gene.names.limma # add common gene names to the object
results.fit.empty_dsGFP.vs.WT_dsBrm <- decideTests(fit.empty_dsGFP.vs.WT_dsBrm)
table(results.fit.empty_dsGFP.vs.WT_dsBrm)## results.fit.empty_dsGFP.vs.WT_dsBrm
## -1 0 1
## 534 8475 592topTable(fit.empty_dsGFP.vs.WT_dsBrm, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr t
## FBgn0051361 FBgn0051361 dpr17 -3.638798 5.651741 -25.78173
## FBgn0261673 FBgn0261673 nemy 3.424770 6.498878 17.07001
## FBgn0041183 FBgn0041183 TepI 5.685959 3.487013 17.42095
## FBgn0085446 FBgn0085446 CG34417 3.384459 5.274943 15.46238
## FBgn0261451 FBgn0261451 trol -3.343331 8.436969 -15.21919
## FBgn0013773 FBgn0013773 Cyp6a22 -3.663273 4.385441 -15.69928
## FBgn0033374 FBgn0033374 CG13741 7.011560 2.141350 17.53693
## FBgn0038299 FBgn0038299 Spn88Eb 7.096041 3.644801 16.12325
## FBgn0052626 FBgn0052626 CG32626 1.877783 8.488482 13.55076
## FBgn0036368 FBgn0036368 CG10738 -2.605751 4.304201 -13.37241
## P.Value adj.P.Val B
## FBgn0051361 4.426454e-14 4.249839e-10 22.08957
## FBgn0261673 2.049823e-11 4.920089e-08 16.49392
## FBgn0041183 1.520628e-11 4.866517e-08 16.00861
## FBgn0085446 8.677013e-11 1.190114e-07 15.13278
## FBgn0261451 1.091899e-10 1.310415e-07 14.91159
## FBgn0013773 6.957753e-11 1.113356e-07 14.88381
## FBgn0033374 1.379380e-11 4.866517e-08 14.31819
## FBgn0038299 4.720700e-11 9.064689e-08 14.19580
## FBgn0052626 5.797974e-10 6.185149e-07 13.24724
## FBgn0036368 7.003042e-10 6.723621e-07 13.05981volcanoplot(fit.empty_dsGFP.vs.WT_dsBrm, highlight = 10, names = fit.empty_dsGFP.vs.WT_dsBrm$genes[,2], main = "empty_dsGFP vs WT_dsBrm")
write.fit(fit.empty_dsGFP.vs.WT_dsBrm , results = results.fit.empty_dsGFP.vs.WT_dsBrm , file="empty_dsGFP_vs_WT_dsBrm.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast empty_dsGFP vs WT_dsBrm_100
empty_dsGFP.vs.WT_dsBrm_100 <- makeContrasts(empty_dsGFP - WT_dsBrm_100, levels = des)
fit.empty_dsGFP.vs.WT_dsBrm_100 <- contrasts.fit(fit, empty_dsGFP.vs.WT_dsBrm_100)
fit.empty_dsGFP.vs.WT_dsBrm_100 <- eBayes(fit.empty_dsGFP.vs.WT_dsBrm_100)
fit.empty_dsGFP.vs.WT_dsBrm_100$genes <- gene.names.limma # add common gene names to the object
results.fit.empty_dsGFP.vs.WT_dsBrm_100 <- decideTests(fit.empty_dsGFP.vs.WT_dsBrm_100)
table(results.fit.empty_dsGFP.vs.WT_dsBrm_100)## results.fit.empty_dsGFP.vs.WT_dsBrm_100
## -1 0 1
## 1145 7489 967topTable(fit.empty_dsGFP.vs.WT_dsBrm_100, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr t
## FBgn0000212 FBgn0000212 brm -5.493298 10.532912 -18.75590
## FBgn0041183 FBgn0041183 TepI 4.094520 3.487013 17.27251
## FBgn0051361 FBgn0051361 dpr17 -2.386931 5.651741 -16.92780
## FBgn0038299 FBgn0038299 Spn88Eb 4.889160 3.644801 17.14230
## FBgn0085446 FBgn0085446 CG34417 2.982768 5.274943 14.20551
## FBgn0035763 FBgn0035763 CG8602 -2.283702 9.088558 -14.19505
## FBgn0033374 FBgn0033374 CG13741 7.823408 2.141350 17.20933
## FBgn0086251 FBgn0086251 del 2.768554 6.648684 13.44719
## FBgn0003231 FBgn0003231 ref(2)P -2.426474 8.521526 -13.41069
## FBgn0013773 FBgn0013773 Cyp6a22 -3.218282 4.385441 -13.61625
## P.Value adj.P.Val B
## FBgn0000212 5.123013e-12 3.699556e-08 17.75307
## FBgn0041183 1.724212e-11 3.699556e-08 16.51037
## FBgn0051361 2.317188e-11 3.707887e-08 16.39235
## FBgn0038299 1.926652e-11 3.699556e-08 16.14844
## FBgn0085446 2.950815e-10 3.579004e-07 13.92473
## FBgn0035763 2.982193e-10 3.579004e-07 13.91489
## FBgn0033374 1.819465e-11 3.699556e-08 13.39329
## FBgn0086251 6.468231e-10 5.379484e-07 13.14534
## FBgn0003231 6.723655e-10 5.379484e-07 13.10426
## FBgn0013773 5.412472e-10 5.379484e-07 13.05588volcanoplot(fit.empty_dsGFP.vs.WT_dsBrm_100, highlight = 10, names = fit.empty_dsGFP.vs.WT_dsBrm_100$genes[,2], main = "empty_dsGFP vs WT_dsBrm_100")
write.fit(fit.empty_dsGFP.vs.WT_dsBrm_100 , results = results.fit.empty_dsGFP.vs.WT_dsBrm_100 , file="empty_dsGFP_vs_WT_dsBrm_100.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast empty_dsGFP vs K804R_dsGFP
empty_dsGFP.vs.K804R_dsGFP <- makeContrasts(empty_dsGFP - K804R_dsGFP, levels = des)
fit.empty_dsGFP.vs.K804R_dsGFP <- contrasts.fit(fit, empty_dsGFP.vs.K804R_dsGFP)
fit.empty_dsGFP.vs.K804R_dsGFP <- eBayes(fit.empty_dsGFP.vs.K804R_dsGFP)
fit.empty_dsGFP.vs.K804R_dsGFP$genes <- gene.names.limma # add common gene names to the object
results.fit.empty_dsGFP.vs.K804R_dsGFP <- decideTests(fit.empty_dsGFP.vs.K804R_dsGFP)
table(results.fit.empty_dsGFP.vs.K804R_dsGFP)## results.fit.empty_dsGFP.vs.K804R_dsGFP
## -1 0 1
## 415 8792 394topTable(fit.empty_dsGFP.vs.K804R_dsGFP, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr t
## FBgn0261451 FBgn0261451 trol -4.086234 8.436969 -18.17496
## FBgn0085446 FBgn0085446 CG34417 4.376563 5.274943 17.54882
## FBgn0041183 FBgn0041183 TepI 4.814501 3.487013 17.54408
## FBgn0033374 FBgn0033374 CG13741 4.279285 2.141350 17.67218
## FBgn0010043 FBgn0010043 GstD7 -3.942124 5.637142 -15.44701
## FBgn0085412 FBgn0085412 CG34383 -2.719829 5.528909 -13.32276
## FBgn0060296 FBgn0060296 pain -2.169251 4.977743 -13.22442
## FBgn0260011 FBgn0260011 nimC4 -3.404814 7.769583 -13.01375
## FBgn0052207 FBgn0052207 CR32207 -2.910486 3.297262 -12.77192
## FBgn0036806 FBgn0036806 Cyp12c1 -3.407019 5.002296 -12.27895
## P.Value adj.P.Val B
## FBgn0261451 8.151421e-12 3.291074e-08 17.41281
## FBgn0085446 1.365705e-11 3.291074e-08 16.79776
## FBgn0041183 1.371138e-11 3.291074e-08 16.39734
## FBgn0033374 1.232055e-11 3.291074e-08 16.24049
## FBgn0010043 8.803163e-11 1.690383e-07 15.02415
## FBgn0085412 7.383819e-10 1.125282e-06 12.97341
## FBgn0060296 8.204329e-10 1.125282e-06 12.87205
## FBgn0260011 1.030514e-09 1.236746e-06 12.68332
## FBgn0052207 1.343986e-09 1.433735e-06 12.27599
## FBgn0036806 2.340364e-09 2.246984e-06 11.79890volcanoplot(fit.empty_dsGFP.vs.K804R_dsGFP, highlight = 10, names = fit.empty_dsGFP.vs.K804R_dsGFP$genes[,2], main = "empty_dsGFP vs K804R_dsGFP")
write.fit(fit.empty_dsGFP.vs.K804R_dsGFP , results = results.fit.empty_dsGFP.vs.K804R_dsGFP , file="empty_dsGFP_vs_K804R_dsGFP.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast empty_dsGFP vs K804R_dsBrm
empty_dsGFP.vs.K804R_dsBrm <- makeContrasts(empty_dsGFP - K804R_dsBrm, levels = des)
fit.empty_dsGFP.vs.K804R_dsBrm <- contrasts.fit(fit, empty_dsGFP.vs.K804R_dsBrm)
fit.empty_dsGFP.vs.K804R_dsBrm <- eBayes(fit.empty_dsGFP.vs.K804R_dsBrm)
fit.empty_dsGFP.vs.K804R_dsBrm$genes <- gene.names.limma # add common gene names to the object
results.fit.empty_dsGFP.vs.K804R_dsBrm <- decideTests(fit.empty_dsGFP.vs.K804R_dsBrm)
table(results.fit.empty_dsGFP.vs.K804R_dsBrm)## results.fit.empty_dsGFP.vs.K804R_dsBrm
## -1 0 1
## 478 8622 501topTable(fit.empty_dsGFP.vs.K804R_dsBrm, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr t
## FBgn0261451 FBgn0261451 trol -4.056676 8.436969 -17.93596
## FBgn0085446 FBgn0085446 CG34417 4.291218 5.274943 17.95562
## FBgn0041183 FBgn0041183 TepI 5.038556 3.487013 18.36436
## FBgn0033374 FBgn0033374 CG13741 4.156648 2.141350 18.41740
## FBgn0010043 FBgn0010043 GstD7 -3.570151 5.637142 -13.98950
## FBgn0260011 FBgn0260011 nimC4 -3.482725 7.769583 -13.20255
## FBgn0085412 FBgn0085412 CG34383 -2.621600 5.528909 -12.88055
## FBgn0259740 FBgn0259740 CG42394 4.230548 5.710746 12.73885
## FBgn0038299 FBgn0038299 Spn88Eb 2.335372 3.644801 12.59677
## FBgn0052207 FBgn0052207 CR32207 -2.869864 3.297262 -12.68191
## P.Value adj.P.Val B
## FBgn0261451 9.906886e-12 2.377900e-08 17.23161
## FBgn0085446 9.748381e-12 2.377900e-08 17.16537
## FBgn0041183 6.995671e-12 2.377900e-08 16.99686
## FBgn0033374 6.704200e-12 2.377900e-08 16.95494
## FBgn0010043 3.676269e-10 7.059171e-07 13.67176
## FBgn0260011 8.399624e-10 1.344080e-06 12.88661
## FBgn0085412 1.192222e-09 1.568179e-06 12.51967
## FBgn0259740 1.394144e-09 1.568179e-06 12.37996
## FBgn0038299 1.633350e-09 1.568179e-06 12.22367
## FBgn0052207 1.485223e-09 1.568179e-06 12.20181volcanoplot(fit.empty_dsGFP.vs.K804R_dsBrm, highlight = 10, names = fit.empty_dsGFP.vs.K804R_dsBrm$genes[,2], main = "empty_dsGFP vs K804R_dsBrm")
write.fit(fit.empty_dsGFP.vs.K804R_dsBrm , results = results.fit.empty_dsGFP.vs.K804R_dsBrm , file="empty_dsGFP_vs_K804R_dsBrm.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast empty_dsGFP vs K804R_dsBrm_100
empty_dsGFP.vs.K804R_dsBrm_100 <- makeContrasts(empty_dsGFP - K804R_dsBrm_100, levels = des)
fit.empty_dsGFP.vs.K804R_dsBrm_100 <- contrasts.fit(fit, empty_dsGFP.vs.K804R_dsBrm_100)
fit.empty_dsGFP.vs.K804R_dsBrm_100 <- eBayes(fit.empty_dsGFP.vs.K804R_dsBrm_100)
fit.empty_dsGFP.vs.K804R_dsBrm_100$genes <- gene.names.limma # add common gene names to the object
results.fit.empty_dsGFP.vs.K804R_dsBrm_100 <- decideTests(fit.empty_dsGFP.vs.K804R_dsBrm_100)
table(results.fit.empty_dsGFP.vs.K804R_dsBrm_100)## results.fit.empty_dsGFP.vs.K804R_dsBrm_100
## -1 0 1
## 745 8257 599topTable(fit.empty_dsGFP.vs.K804R_dsBrm_100, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr t
## FBgn0085446 FBgn0085446 CG34417 4.368885 5.274943 18.04586
## FBgn0041183 FBgn0041183 TepI 4.564556 3.487013 18.08563
## FBgn0033374 FBgn0033374 CG13741 4.983479 2.141350 18.24992
## FBgn0261451 FBgn0261451 trol -3.391852 8.436969 -15.35762
## FBgn0010043 FBgn0010043 GstD7 -3.874557 5.637142 -15.16689
## FBgn0060296 FBgn0060296 pain -2.222327 4.977743 -13.64258
## FBgn0032123 FBgn0032123 Oatp30B -2.814895 5.169766 -13.58155
## FBgn0052207 FBgn0052207 CR32207 -3.072861 3.297262 -13.62431
## FBgn0085412 FBgn0085412 CG34383 -2.645741 5.528909 -12.96205
## FBgn0038299 FBgn0038299 Spn88Eb 2.375980 3.644801 12.63246
## P.Value adj.P.Val B
## FBgn0085446 9.054396e-12 2.897708e-08 17.22782
## FBgn0041183 8.765463e-12 2.897708e-08 16.98344
## FBgn0033374 7.671472e-12 2.897708e-08 16.30596
## FBgn0261451 9.576214e-11 1.836524e-07 15.03882
## FBgn0010043 1.147708e-10 1.836524e-07 14.78646
## FBgn0060296 5.265226e-10 5.988079e-07 13.30914
## FBgn0032123 5.613239e-10 5.988079e-07 13.25932
## FBgn0052207 5.366929e-10 5.988079e-07 13.15023
## FBgn0085412 1.090338e-09 1.046833e-06 12.60696
## FBgn0038299 1.569443e-09 1.369838e-06 12.26497volcanoplot(fit.empty_dsGFP.vs.K804R_dsBrm_100, highlight = 10, names = fit.empty_dsGFP.vs.K804R_dsBrm_100$genes[,2], main = "empty_dsGFP vs K804R_dsBrm_100")
write.fit(fit.empty_dsGFP.vs.K804R_dsBrm_100 , results = results.fit.empty_dsGFP.vs.K804R_dsBrm_100 , file="empty_dsGFP_vs_K804R_dsBrm_100.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast empty_dsGFP vs YN_dsGFP
empty_dsGFP.vs.YN_dsGFP <- makeContrasts(empty_dsGFP - YN_dsGFP, levels = des)
fit.empty_dsGFP.vs.YN_dsGFP <- contrasts.fit(fit, empty_dsGFP.vs.YN_dsGFP)
fit.empty_dsGFP.vs.YN_dsGFP <- eBayes(fit.empty_dsGFP.vs.YN_dsGFP)
fit.empty_dsGFP.vs.YN_dsGFP$genes <- gene.names.limma # add common gene names to the object
results.fit.empty_dsGFP.vs.YN_dsGFP <- decideTests(fit.empty_dsGFP.vs.YN_dsGFP)
table(results.fit.empty_dsGFP.vs.YN_dsGFP)## results.fit.empty_dsGFP.vs.YN_dsGFP
## -1 0 1
## 507 8485 609topTable(fit.empty_dsGFP.vs.YN_dsGFP, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr t
## FBgn0261451 FBgn0261451 trol -4.411528 8.436969 -19.26069
## FBgn0036368 FBgn0036368 CG10738 -3.740761 4.304201 -19.39774
## FBgn0085446 FBgn0085446 CG34417 4.024759 5.274943 17.52402
## FBgn0041183 FBgn0041183 TepI 3.934357 3.487013 17.55145
## FBgn0038299 FBgn0038299 Spn88Eb 4.508791 3.644801 17.52079
## FBgn0060296 FBgn0060296 pain -2.729480 4.977743 -17.07736
## FBgn0085412 FBgn0085412 CG34383 -3.174748 5.528909 -15.75547
## FBgn0033374 FBgn0033374 CG13741 2.716904 2.141350 15.62993
## FBgn0010389 FBgn0010389 htl -4.264200 6.156398 -15.47189
## FBgn0013773 FBgn0013773 Cyp6a22 -3.632162 4.385441 -15.65187
## P.Value adj.P.Val B
## FBgn0261451 3.458420e-12 1.660215e-08 18.19952
## FBgn0036368 3.113591e-12 1.660215e-08 18.09477
## FBgn0085446 1.394392e-11 2.684769e-08 16.84270
## FBgn0041183 1.362706e-11 2.684769e-08 16.72252
## FBgn0038299 1.398172e-11 2.684769e-08 16.51003
## FBgn0060296 2.036920e-11 3.259412e-08 16.34268
## FBgn0085412 6.605572e-11 7.915586e-08 15.24942
## FBgn0033374 7.420089e-11 7.915586e-08 15.18398
## FBgn0010389 8.599961e-11 8.256822e-08 15.09448
## FBgn0013773 7.270401e-11 7.915586e-08 14.74237volcanoplot(fit.empty_dsGFP.vs.YN_dsGFP, highlight = 10, names = fit.empty_dsGFP.vs.YN_dsGFP$genes[,2], main = "empty_dsGFP vs YN_dsGFP")
write.fit(fit.empty_dsGFP.vs.YN_dsGFP , results = results.fit.empty_dsGFP.vs.YN_dsGFP , file="empty_dsGFP_vs_YN_dsGFP.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast empty_dsGFP vs YN_dsBrm
empty_dsGFP.vs.YN_dsBrm <- makeContrasts(empty_dsGFP - YN_dsBrm, levels = des)
fit.empty_dsGFP.vs.YN_dsBrm <- contrasts.fit(fit, empty_dsGFP.vs.YN_dsBrm)
fit.empty_dsGFP.vs.YN_dsBrm <- eBayes(fit.empty_dsGFP.vs.YN_dsBrm)
fit.empty_dsGFP.vs.YN_dsBrm$genes <- gene.names.limma # add common gene names to the object
results.fit.empty_dsGFP.vs.YN_dsBrm <- decideTests(fit.empty_dsGFP.vs.YN_dsBrm)
table(results.fit.empty_dsGFP.vs.YN_dsBrm)## results.fit.empty_dsGFP.vs.YN_dsBrm
## -1 0 1
## 637 8322 642topTable(fit.empty_dsGFP.vs.YN_dsBrm, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr t
## FBgn0036368 FBgn0036368 CG10738 -3.956618 4.304201 -20.49990
## FBgn0261451 FBgn0261451 trol -4.471794 8.436969 -19.75604
## FBgn0060296 FBgn0060296 pain -3.042067 4.977743 -18.95371
## FBgn0041183 FBgn0041183 TepI 4.163317 3.487013 16.69666
## FBgn0010389 FBgn0010389 htl -4.454298 6.156398 -16.23147
## FBgn0085446 FBgn0085446 CG34417 3.833156 5.274943 16.22814
## FBgn0085412 FBgn0085412 CG34383 -3.309114 5.528909 -16.33061
## FBgn0034438 FBgn0034438 CG9416 -4.252531 8.658978 -15.83963
## FBgn0038299 FBgn0038299 Spn88Eb 5.123759 3.644801 16.51666
## FBgn0260011 FBgn0260011 nimC4 -4.047398 7.769583 -15.27018
## P.Value adj.P.Val B
## FBgn0036368 1.370609e-12 1.139415e-08 18.81961
## FBgn0261451 2.373534e-12 1.139415e-08 18.56286
## FBgn0060296 4.386877e-12 1.403947e-08 17.71578
## FBgn0041183 2.833795e-11 5.154268e-08 15.91983
## FBgn0010389 4.281932e-11 5.154268e-08 15.76237
## FBgn0085446 4.294776e-11 5.154268e-08 15.76069
## FBgn0085412 3.917891e-11 5.154268e-08 15.72647
## FBgn0034438 6.113012e-11 6.521226e-08 15.46934
## FBgn0038299 3.320444e-11 5.154268e-08 15.38942
## FBgn0260011 1.040251e-10 8.416750e-08 14.94840volcanoplot(fit.empty_dsGFP.vs.YN_dsBrm, highlight = 10, names = fit.empty_dsGFP.vs.YN_dsBrm$genes[,2], main = "empty_dsGFP vs YN_dsBrm")
write.fit(fit.empty_dsGFP.vs.YN_dsBrm , results = results.fit.empty_dsGFP.vs.YN_dsBrm , file="empty_dsGFP_vs_YN_dsBrm.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast empty_dsGFP vs YN_dsBrm_100
empty_dsGFP.vs.YN_dsBrm_100 <- makeContrasts(empty_dsGFP - YN_dsBrm_100, levels = des)
fit.empty_dsGFP.vs.YN_dsBrm_100 <- contrasts.fit(fit, empty_dsGFP.vs.YN_dsBrm_100)
fit.empty_dsGFP.vs.YN_dsBrm_100 <- eBayes(fit.empty_dsGFP.vs.YN_dsBrm_100)
fit.empty_dsGFP.vs.YN_dsBrm_100$genes <- gene.names.limma # add common gene names to the object
results.fit.empty_dsGFP.vs.YN_dsBrm_100 <- decideTests(fit.empty_dsGFP.vs.YN_dsBrm_100)
table(results.fit.empty_dsGFP.vs.YN_dsBrm_100)## results.fit.empty_dsGFP.vs.YN_dsBrm_100
## -1 0 1
## 1450 6974 1177topTable(fit.empty_dsGFP.vs.YN_dsBrm_100, adjust = "BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr t
## FBgn0000212 FBgn0000212 brm -6.090874 10.532912 -20.79623
## FBgn0035763 FBgn0035763 CG8602 -2.827441 9.088558 -17.38791
## FBgn0060296 FBgn0060296 pain -2.816611 4.977743 -17.46353
## FBgn0085446 FBgn0085446 CG34417 3.707376 5.274943 15.98980
## FBgn0033374 FBgn0033374 CG13741 3.218187 2.141350 15.90196
## FBgn0036368 FBgn0036368 CG10738 -3.020514 4.304201 -15.55789
## FBgn0085412 FBgn0085412 CG34383 -3.099603 5.528909 -15.24598
## FBgn0033205 FBgn0033205 CG2064 -2.833732 8.015501 -14.78318
## FBgn0038299 FBgn0038299 Spn88Eb 3.337314 3.644801 14.80904
## FBgn0031589 FBgn0031589 CG3714 -2.359358 7.154859 -14.51042
## P.Value adj.P.Val B
## FBgn0000212 1.107014e-12 1.062844e-08 18.92556
## FBgn0035763 1.563625e-11 5.004122e-08 16.73622
## FBgn0060296 1.467070e-11 5.004122e-08 16.53111
## FBgn0085446 5.328438e-11 9.238504e-08 15.52320
## FBgn0033374 5.773464e-11 9.238504e-08 15.15348
## FBgn0036368 7.935018e-11 1.088344e-07 15.06313
## FBgn0085412 1.064430e-10 1.277450e-07 14.73228
## FBgn0033205 1.662305e-10 1.450890e-07 14.48627
## FBgn0038299 1.620886e-10 1.450890e-07 14.38388
## FBgn0031589 2.174264e-10 1.605777e-07 14.22652volcanoplot(fit.empty_dsGFP.vs.YN_dsBrm_100, highlight = 10, names = fit.empty_dsGFP.vs.YN_dsBrm_100$genes[,2], main = "empty_dsGFP vs YN_dsBrm_100")
write.fit(fit.empty_dsGFP.vs.YN_dsBrm_100 , results = results.fit.empty_dsGFP.vs.YN_dsBrm_100, file = "empty_dsGFP_vs_YN_dsBrm_100.txt", digits=30, dec = ",", adjust = "BH") # save the results to file
# contrast empty_dsBrm vs WT_GFP
empty_dsBrm.vs.WT_dsGFP <- makeContrasts(empty_dsBrm - WT_dsGFP, levels = des)
fit.empty_dsBrm.vs.WT_dsGFP <- contrasts.fit(fit, empty_dsBrm.vs.WT_dsGFP)
fit.empty_dsBrm.vs.WT_dsGFP <- eBayes(fit.empty_dsBrm.vs.WT_dsGFP)
fit.empty_dsBrm.vs.WT_dsGFP$genes <- gene.names.limma # add common gene names to the object
results.fit.empty_dsBrm.vs.WT_dsGFP <- decideTests(fit.empty_dsBrm.vs.WT_dsGFP)
table(results.fit.empty_dsBrm.vs.WT_dsGFP)## results.fit.empty_dsBrm.vs.WT_dsGFP
## -1 0 1
## 604 8252 745topTable(fit.empty_dsBrm.vs.WT_dsGFP, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr t
## FBgn0041183 FBgn0041183 TepI 5.905460 3.487013 16.76539
## FBgn0261451 FBgn0261451 trol -3.597941 8.436969 -15.56444
## FBgn0261673 FBgn0261673 nemy 2.855993 6.498878 15.53426
## FBgn0038299 FBgn0038299 Spn88Eb 6.747943 3.644801 16.95803
## FBgn0051361 FBgn0051361 dpr17 -2.161719 5.651741 -15.29800
## FBgn0085446 FBgn0085446 CG34417 3.279444 5.274943 15.22930
## FBgn0086251 FBgn0086251 del 2.754531 6.648684 13.81929
## FBgn0000212 FBgn0000212 brm -3.501695 10.532912 -13.78016
## FBgn0033374 FBgn0033374 CG13741 7.647252 2.141350 17.30795
## FBgn0013773 FBgn0013773 Cyp6a22 -3.527952 4.385441 -13.86515
## P.Value adj.P.Val B
## FBgn0041183 2.668479e-11 8.540022e-08 15.25566
## FBgn0261451 7.886696e-11 1.483287e-07 15.22050
## FBgn0261673 8.112145e-11 1.483287e-07 15.19323
## FBgn0038299 2.257394e-11 8.540022e-08 15.00449
## FBgn0051361 1.013176e-10 1.483287e-07 14.95891
## FBgn0085446 1.081451e-10 1.483287e-07 14.91467
## FBgn0086251 4.380539e-10 3.981692e-07 13.53340
## FBgn0000212 4.561881e-10 3.981692e-07 13.49458
## FBgn0033374 1.673102e-11 8.540022e-08 13.43714
## FBgn0013773 4.177734e-10 3.981692e-07 13.12508volcanoplot(fit.empty_dsBrm.vs.WT_dsGFP, highlight = 10, names = fit.empty_dsBrm.vs.WT_dsGFP$genes[,2], main = "empty_dsBrm vs WT_dsBrm")
write.fit(fit.empty_dsBrm.vs.WT_dsGFP , results = results.fit.empty_dsBrm.vs.WT_dsGFP , file="empty_dsBrm_vs_WT_dsGFP.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast empty_dsBrm vs WT_dsBrm
empty_dsBrm.vs.WT_dsBrm <- makeContrasts(empty_dsBrm - WT_dsBrm, levels = des)
fit.empty_dsBrm.vs.WT_dsBrm <- contrasts.fit(fit, empty_dsBrm.vs.WT_dsBrm)
fit.empty_dsBrm.vs.WT_dsBrm <- eBayes(fit.empty_dsBrm.vs.WT_dsBrm)
fit.empty_dsBrm.vs.WT_dsBrm$genes <- gene.names.limma # add common gene names to the object
results.fit.empty_dsBrm.vs.WT_dsBrm <- decideTests(fit.empty_dsBrm.vs.WT_dsBrm)
table(results.fit.empty_dsBrm.vs.WT_dsBrm)## results.fit.empty_dsBrm.vs.WT_dsBrm
## -1 0 1
## 533 8421 647topTable(fit.empty_dsBrm.vs.WT_dsBrm, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr t
## FBgn0051361 FBgn0051361 dpr17 -3.324597 5.651741 -23.46900
## FBgn0261673 FBgn0261673 nemy 3.380392 6.498878 16.90850
## FBgn0041183 FBgn0041183 TepI 5.411512 3.487013 16.57585
## FBgn0261451 FBgn0261451 trol -3.589582 8.436969 -15.58559
## FBgn0085446 FBgn0085446 CG34417 3.187334 5.274943 14.75191
## FBgn0038299 FBgn0038299 Spn88Eb 7.236681 3.644801 16.44283
## FBgn0033374 FBgn0033374 CG13741 6.899733 2.141350 17.22507
## FBgn0013773 FBgn0013773 Cyp6a22 -3.737967 4.385441 -14.71767
## FBgn0086251 FBgn0086251 del 2.840318 6.648684 13.92686
## FBgn0260011 FBgn0260011 nimC4 -3.409449 7.769583 -12.32250
## P.Value adj.P.Val B
## FBgn0051361 1.817417e-13 1.744902e-09 20.85353
## FBgn0261673 2.356228e-11 6.807234e-08 16.36210
## FBgn0041183 3.151304e-11 6.807234e-08 15.40850
## FBgn0261451 7.732678e-11 1.237357e-07 15.24632
## FBgn0085446 1.713882e-10 2.127021e-07 14.46404
## FBgn0038299 3.545065e-11 6.807234e-08 14.38916
## FBgn0033374 1.795214e-11 6.807234e-08 14.13504
## FBgn0013773 1.772333e-10 2.127021e-07 13.91508
## FBgn0086251 3.920373e-10 4.182167e-07 13.64356
## FBgn0260011 2.226810e-09 1.825286e-06 11.91449volcanoplot(fit.empty_dsBrm.vs.WT_dsBrm, highlight = 10, names = fit.empty_dsBrm.vs.WT_dsBrm$genes[,2], main = "empty_dsBrm vs WT_dsBrm")
write.fit(fit.empty_dsBrm.vs.WT_dsBrm , results = results.fit.empty_dsBrm.vs.WT_dsBrm , file="empty_dsBrm_vs_WT_dsBrm.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast empty_dsBrm vs WT_dsBrm_100
empty_dsBrm.vs.WT_dsBrm_100 <- makeContrasts(empty_dsBrm - WT_dsBrm_100, levels = des)
fit.empty_dsBrm.vs.WT_dsBrm_100 <- contrasts.fit(fit, empty_dsBrm.vs.WT_dsBrm_100)
fit.empty_dsBrm.vs.WT_dsBrm_100 <- eBayes(fit.empty_dsBrm.vs.WT_dsBrm_100)
fit.empty_dsBrm.vs.WT_dsBrm_100$genes <- gene.names.limma # add common gene names to the object
results.fit.empty_dsBrm.vs.WT_dsBrm_100 <- decideTests(fit.empty_dsBrm.vs.WT_dsBrm_100)
table(results.fit.empty_dsBrm.vs.WT_dsBrm_100)## results.fit.empty_dsBrm.vs.WT_dsBrm_100
## -1 0 1
## 1151 7393 1057topTable(fit.empty_dsBrm.vs.WT_dsBrm_100, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr t
## FBgn0000212 FBgn0000212 brm -6.814484 10.532912 -23.85295
## FBgn0038299 FBgn0038299 Spn88Eb 5.029801 3.644801 17.63547
## FBgn0041183 FBgn0041183 TepI 3.820073 3.487013 16.10696
## FBgn0035763 FBgn0035763 CG8602 -2.413299 9.088558 -15.13130
## FBgn0051361 FBgn0051361 dpr17 -2.072730 5.651741 -14.64542
## FBgn0086251 FBgn0086251 del 2.959524 6.648684 14.41096
## FBgn0003231 FBgn0003231 ref(2)P -2.456175 8.521526 -13.66908
## FBgn0033374 FBgn0033374 CG13741 7.711581 2.141350 16.93887
## FBgn0085446 FBgn0085446 CG34417 2.785644 5.274943 13.45525
## FBgn0261673 FBgn0261673 nemy 2.231337 6.498878 12.83474
## P.Value adj.P.Val B
## FBgn0000212 1.424761e-13 1.367913e-09 21.01954
## FBgn0038299 1.270315e-11 6.098148e-08 16.53631
## FBgn0041183 4.790784e-11 9.199264e-08 15.58446
## FBgn0035763 1.187418e-10 1.900067e-07 14.82856
## FBgn0051361 1.902671e-10 2.609649e-07 14.35541
## FBgn0086251 2.400533e-10 2.880940e-07 14.13061
## FBgn0003231 5.121281e-10 5.463268e-07 13.37329
## FBgn0033374 2.295097e-11 7.295909e-08 13.34995
## FBgn0085446 6.413226e-10 6.157339e-07 13.14409
## FBgn0261673 1.253882e-09 9.354958e-07 12.46842volcanoplot(fit.empty_dsBrm.vs.WT_dsBrm_100, highlight = 10, names = fit.empty_dsBrm.vs.WT_dsBrm_100$genes[,2], main = "empty_dsBrm vs WT_dsBrm_100")
write.fit(fit.empty_dsBrm.vs.WT_dsBrm_100 , results = results.fit.empty_dsBrm.vs.WT_dsBrm_100 , file="empty_dsBrm_vs_WT_dsBrm_100.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast empty_dsBrm vs K804R_dsGFP
empty_dsBrm.vs.K804R_dsGFP <- makeContrasts(empty_dsBrm - K804R_dsGFP, levels = des)
fit.empty_dsBrm.vs.K804R_dsGFP <- contrasts.fit(fit, empty_dsBrm.vs.K804R_dsGFP)
fit.empty_dsBrm.vs.K804R_dsGFP <- eBayes(fit.empty_dsBrm.vs.K804R_dsGFP)
fit.empty_dsBrm.vs.K804R_dsGFP$genes <- gene.names.limma # add common gene names to the object
results.fit.empty_dsBrm.vs.K804R_dsGFP <- decideTests(fit.empty_dsBrm.vs.K804R_dsGFP)
table(results.fit.empty_dsBrm.vs.K804R_dsGFP)## results.fit.empty_dsBrm.vs.K804R_dsGFP
## -1 0 1
## 405 8736 460topTable(fit.empty_dsBrm.vs.K804R_dsGFP, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr t
## FBgn0261451 FBgn0261451 trol -4.332485 8.436969 -18.41804
## FBgn0085446 FBgn0085446 CG34417 4.179439 5.274943 16.92620
## FBgn0033374 FBgn0033374 CG13741 4.167458 2.141350 17.12335
## FBgn0041183 FBgn0041183 TepI 4.540054 3.487013 16.53801
## FBgn0010043 FBgn0010043 GstD7 -3.847158 5.637142 -14.49406
## FBgn0260011 FBgn0260011 nimC4 -3.660353 7.769583 -13.15550
## FBgn0038299 FBgn0038299 Spn88Eb 2.352340 3.644801 12.55783
## FBgn0036806 FBgn0036806 Cyp12c1 -3.935987 5.002296 -11.96312
## FBgn0060296 FBgn0060296 pain -1.981094 4.977743 -11.78555
## FBgn0085412 FBgn0085412 CG34383 -2.391090 5.528909 -11.67740
## P.Value adj.P.Val B
## FBgn0261451 6.700748e-12 6.433388e-08 17.56789
## FBgn0085446 2.320403e-11 7.426063e-08 16.31653
## FBgn0033374 1.958172e-11 7.426063e-08 15.85569
## FBgn0041183 3.258340e-11 7.820830e-08 15.65772
## FBgn0010043 2.209878e-10 4.243407e-07 14.13012
## FBgn0260011 8.836589e-10 1.414002e-06 12.84040
## FBgn0038299 1.706250e-09 2.340243e-06 12.18360
## FBgn0036806 3.371714e-09 3.779364e-06 11.31830
## FBgn0060296 4.154467e-09 3.779364e-06 11.30003
## FBgn0085412 4.723713e-09 3.779364e-06 11.17913volcanoplot(fit.empty_dsBrm.vs.K804R_dsGFP, highlight = 10, names = fit.empty_dsBrm.vs.K804R_dsGFP$genes[,2], main = "empty_dsBrm vs K804R_dsGFP")
write.fit(fit.empty_dsBrm.vs.K804R_dsGFP , results = results.fit.empty_dsBrm.vs.K804R_dsGFP , file="empty_dsBrm_vs_K804R_dsGFP.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast empty_dsBrm vs K804R_dsBrm
empty_dsBrm.vs.K804R_dsBrm <- makeContrasts(empty_dsBrm - K804R_dsBrm, levels = des)
fit.empty_dsBrm.vs.K804R_dsBrm <- contrasts.fit(fit, empty_dsBrm.vs.K804R_dsBrm)
fit.empty_dsBrm.vs.K804R_dsBrm <- eBayes(fit.empty_dsBrm.vs.K804R_dsBrm)
fit.empty_dsBrm.vs.K804R_dsBrm$genes <- gene.names.limma # add common gene names to the object
results.fit.empty_dsBrm.vs.K804R_dsBrm <- decideTests(fit.empty_dsBrm.vs.K804R_dsBrm)
table(results.fit.empty_dsBrm.vs.K804R_dsBrm)## results.fit.empty_dsBrm.vs.K804R_dsBrm
## -1 0 1
## 354 8805 442topTable(fit.empty_dsBrm.vs.K804R_dsBrm, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr t
## FBgn0261451 FBgn0261451 trol -4.302927 8.436969 -18.19273
## FBgn0085446 FBgn0085446 CG34417 4.094093 5.274943 17.31782
## FBgn0033374 FBgn0033374 CG13741 4.044821 2.141350 17.81771
## FBgn0041183 FBgn0041183 TepI 4.764109 3.487013 17.35778
## FBgn0038299 FBgn0038299 Spn88Eb 2.476012 3.644801 13.35547
## FBgn0260011 FBgn0260011 nimC4 -3.738264 7.769583 -13.33811
## FBgn0010043 FBgn0010043 GstD7 -3.475184 5.637142 -13.09270
## FBgn0052207 FBgn0052207 CR32207 -2.726379 3.297262 -11.60256
## FBgn0036806 FBgn0036806 Cyp12c1 -3.777365 5.002296 -11.49136
## FBgn0062978 FBgn0062978 CG31808 3.402407 2.805649 11.38066
## P.Value adj.P.Val B
## FBgn0261451 8.034857e-12 3.982408e-08 17.39360
## FBgn0085446 1.659164e-11 3.982408e-08 16.66755
## FBgn0033374 1.092014e-11 3.982408e-08 16.51028
## FBgn0041183 1.603952e-11 3.982408e-08 16.27035
## FBgn0038299 7.130557e-10 1.162325e-06 13.05162
## FBgn0260011 7.263777e-10 1.162325e-06 13.03413
## FBgn0010043 9.457809e-10 1.297206e-06 12.74295
## FBgn0052207 5.165596e-09 5.510543e-06 11.00266
## FBgn0036806 5.904700e-09 5.669102e-06 10.80871
## FBgn0062978 6.752505e-09 5.893709e-06 10.74964volcanoplot(fit.empty_dsBrm.vs.K804R_dsBrm, highlight = 10, names = fit.empty_dsBrm.vs.K804R_dsBrm$genes[,2], main = "empty_dsBrm vs K804R_dsBrm")
write.fit(fit.empty_dsBrm.vs.K804R_dsBrm , results = results.fit.empty_dsBrm.vs.K804R_dsBrm , file="empty_dsBrm_vs_K804R_dsBrm.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast empty_dsBrm vs K804R_dsBrm_100
empty_dsBrm.vs.K804R_dsBrm_100 <- makeContrasts(empty_dsBrm - K804R_dsBrm_100, levels = des)
fit.empty_dsBrm.vs.K804R_dsBrm_100 <- contrasts.fit(fit, empty_dsBrm.vs.K804R_dsBrm_100)
fit.empty_dsBrm.vs.K804R_dsBrm_100 <- eBayes(fit.empty_dsBrm.vs.K804R_dsBrm_100)
fit.empty_dsBrm.vs.K804R_dsBrm_100$genes <- gene.names.limma # add common gene names to the object
results.fit.empty_dsBrm.vs.K804R_dsBrm_100 <- decideTests(fit.empty_dsBrm.vs.K804R_dsBrm_100)
table(results.fit.empty_dsBrm.vs.K804R_dsBrm_100)## results.fit.empty_dsBrm.vs.K804R_dsBrm_100
## -1 0 1
## 717 8242 642topTable(fit.empty_dsBrm.vs.K804R_dsBrm_100, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr t
## FBgn0085446 FBgn0085446 CG34417 4.171760 5.274943 17.41488
## FBgn0041183 FBgn0041183 TepI 4.290109 3.487013 16.99095
## FBgn0033374 FBgn0033374 CG13741 4.871652 2.141350 17.76935
## FBgn0261451 FBgn0261451 trol -3.638103 8.436969 -15.71948
## FBgn0010043 FBgn0010043 GstD7 -3.779590 5.637142 -14.22619
## FBgn0000212 FBgn0000212 brm -3.529204 10.532912 -13.87784
## FBgn0038299 FBgn0038299 Spn88Eb 2.516621 3.644801 13.38030
## FBgn0052207 FBgn0052207 CR32207 -2.929376 3.297262 -12.50505
## FBgn0035763 FBgn0035763 CG8602 -1.915686 9.088558 -12.20194
## FBgn0060296 FBgn0060296 pain -2.034169 4.977743 -12.18172
## P.Value adj.P.Val B
## FBgn0085446 1.528436e-11 7.022031e-08 16.71171
## FBgn0041183 2.194156e-11 7.022031e-08 16.10723
## FBgn0033374 1.136583e-11 7.022031e-08 15.84991
## FBgn0261451 6.828909e-11 1.360911e-07 15.34803
## FBgn0010043 2.889825e-10 4.624201e-07 13.86049
## FBgn0000212 4.123378e-10 5.655508e-07 13.59384
## FBgn0038299 6.944454e-10 8.334213e-07 13.07878
## FBgn0052207 1.810582e-09 1.931489e-06 11.95491
## FBgn0035763 2.556426e-09 2.093497e-06 11.77376
## FBgn0060296 2.616599e-09 2.093497e-06 11.74254volcanoplot(fit.empty_dsBrm.vs.K804R_dsBrm_100, highlight = 10, names = fit.empty_dsBrm.vs.K804R_dsBrm_100$genes[,2], main = "empty_dsBrm vs K804R_dsBrm_100")
write.fit(fit.empty_dsBrm.vs.K804R_dsBrm_100 , results = results.fit.empty_dsBrm.vs.K804R_dsBrm_100 , file="empty_dsBrm_vs_K804R_dsBrm_100.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast empty_dsBrm vs YN_dsGFP
empty_dsBrm.vs.YN_dsGFP <- makeContrasts(empty_dsBrm - YN_dsGFP, levels = des)
fit.empty_dsBrm.vs.YN_dsGFP <- contrasts.fit(fit, empty_dsBrm.vs.YN_dsGFP)
fit.empty_dsBrm.vs.YN_dsGFP <- eBayes(fit.empty_dsBrm.vs.YN_dsGFP)
fit.empty_dsBrm.vs.YN_dsGFP$genes <- gene.names.limma # add common gene names to the object
results.fit.empty_dsBrm.vs.YN_dsGFP <- decideTests(fit.empty_dsBrm.vs.YN_dsGFP)
table(results.fit.empty_dsBrm.vs.YN_dsGFP)## results.fit.empty_dsBrm.vs.YN_dsGFP
## -1 0 1
## 617 8169 815topTable(fit.empty_dsBrm.vs.YN_dsGFP, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr t
## FBgn0261451 FBgn0261451 trol -4.657778 8.436969 -19.46722
## FBgn0036368 FBgn0036368 CG10738 -3.612059 4.304201 -18.11321
## FBgn0038299 FBgn0038299 Spn88Eb 4.649432 3.644801 18.06738
## FBgn0085446 FBgn0085446 CG34417 3.827635 5.274943 16.86301
## FBgn0040091 FBgn0040091 Ugt58Fa -2.474163 8.240297 -16.25689
## FBgn0041183 FBgn0041183 TepI 3.659910 3.487013 16.31827
## FBgn0060296 FBgn0060296 pain -2.541323 4.977743 -15.49627
## FBgn0000212 FBgn0000212 brm -3.987694 10.532912 -15.32840
## FBgn0010389 FBgn0010389 htl -4.392536 6.156398 -14.89474
## FBgn0033374 FBgn0033374 CG13741 2.605077 2.141350 14.84059
## P.Value adj.P.Val B
## FBgn0261451 2.952900e-12 2.835079e-08 18.33891
## FBgn0036368 8.570763e-12 2.847283e-08 17.18653
## FBgn0038299 8.896834e-12 2.847283e-08 16.96664
## FBgn0085446 2.451053e-11 5.883140e-08 16.33782
## FBgn0040091 4.185306e-11 6.697187e-08 15.85515
## FBgn0041183 3.961334e-11 6.697187e-08 15.77803
## FBgn0060296 8.405629e-11 1.152892e-07 15.04428
## FBgn0000212 9.844380e-11 1.181449e-07 15.00272
## FBgn0010389 1.491295e-10 1.496074e-07 14.55136
## FBgn0033374 1.571843e-10 1.496074e-07 14.49015volcanoplot(fit.empty_dsBrm.vs.YN_dsGFP, highlight = 10, names = fit.empty_dsBrm.vs.YN_dsGFP$genes[,2], main = "empty_dsBrm vs YN_dsGFP")
write.fit(fit.empty_dsBrm.vs.YN_dsGFP , results = results.fit.empty_dsBrm.vs.YN_dsGFP , file="empty_dsBrm_vs_YN_dsGFP.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast empty_dsBrm vs YN_dsBrm
empty_dsBrm.vs.YN_dsBrm <- makeContrasts(empty_dsBrm - YN_dsBrm, levels = des)
fit.empty_dsBrm.vs.YN_dsBrm <- contrasts.fit(fit, empty_dsBrm.vs.YN_dsBrm)
fit.empty_dsBrm.vs.YN_dsBrm <- eBayes(fit.empty_dsBrm.vs.YN_dsBrm)
fit.empty_dsBrm.vs.YN_dsBrm$genes <- gene.names.limma # add common gene names to the object
results.fit.empty_dsBrm.vs.YN_dsBrm <- decideTests(fit.empty_dsBrm.vs.YN_dsBrm)
table(results.fit.empty_dsBrm.vs.YN_dsBrm)## results.fit.empty_dsBrm.vs.YN_dsBrm
## -1 0 1
## 545 8456 600topTable(fit.empty_dsBrm.vs.YN_dsBrm, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr t
## FBgn0261451 FBgn0261451 trol -4.718045 8.436969 -19.93372
## FBgn0036368 FBgn0036368 CG10738 -3.827916 4.304201 -19.18064
## FBgn0060296 FBgn0060296 pain -2.853910 4.977743 -17.33337
## FBgn0040091 FBgn0040091 Ugt58Fa -2.502999 8.240297 -16.66277
## FBgn0038299 FBgn0038299 Spn88Eb 5.264400 3.644801 16.97007
## FBgn0085446 FBgn0085446 CG34417 3.636031 5.274943 15.56570
## FBgn0010389 FBgn0010389 htl -4.582634 6.156398 -15.59795
## FBgn0041183 FBgn0041183 TepI 3.888870 3.487013 15.58918
## FBgn0260011 FBgn0260011 nimC4 -4.302937 7.769583 -15.28813
## FBgn0034438 FBgn0034438 CG9416 -3.997675 8.658978 -14.86558
## P.Value adj.P.Val B
## FBgn0261451 2.078167e-12 1.765828e-08 18.63440
## FBgn0036368 3.678425e-12 1.765828e-08 17.88652
## FBgn0060296 1.637449e-11 5.240381e-08 16.49690
## FBgn0040091 2.919272e-11 5.605585e-08 16.20071
## FBgn0038299 2.234054e-11 5.362287e-08 15.70281
## FBgn0085446 7.877417e-11 9.453885e-08 15.18541
## FBgn0010389 7.644129e-11 9.453885e-08 15.16467
## FBgn0041183 7.706854e-11 9.453885e-08 15.01327
## FBgn0260011 1.022693e-10 1.090986e-07 14.94566
## FBgn0034438 1.534114e-10 1.472903e-07 14.56887volcanoplot(fit.empty_dsBrm.vs.YN_dsBrm, highlight = 10, names = fit.empty_dsBrm.vs.YN_dsBrm$genes[,2], main = "empty_dsBrm vs YN_dsBrm")
write.fit(fit.empty_dsBrm.vs.YN_dsBrm , results = results.fit.empty_dsBrm.vs.YN_dsBrm , file="empty_dsBrm_vs_YN_dsBrm.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast empty_dsBrm vs YN_dsBrm_100
empty_dsBrm.vs.YN_dsBrm_100 <- makeContrasts(empty_dsBrm - YN_dsBrm_100, levels = des)
fit.empty_dsBrm.vs.YN_dsBrm_100 <- contrasts.fit(fit, empty_dsBrm.vs.YN_dsBrm_100)
fit.empty_dsBrm.vs.YN_dsBrm_100 <- eBayes(fit.empty_dsBrm.vs.YN_dsBrm_100)
fit.empty_dsBrm.vs.YN_dsBrm_100$genes <- gene.names.limma # add common gene names to the object
results.fit.empty_dsBrm.vs.YN_dsBrm_100 <- decideTests(fit.empty_dsBrm.vs.YN_dsBrm_100)
table(results.fit.empty_dsBrm.vs.YN_dsBrm_100)## results.fit.empty_dsBrm.vs.YN_dsBrm_100
## -1 0 1
## 1381 6950 1270topTable(fit.empty_dsBrm.vs.YN_dsBrm_100, adjust = "BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr t
## FBgn0000212 FBgn0000212 brm -7.412060 10.532912 -25.94467
## FBgn0035763 FBgn0035763 CG8602 -2.957038 9.088558 -18.33994
## FBgn0060296 FBgn0060296 pain -2.628453 4.977743 -15.89014
## FBgn0040091 FBgn0040091 Ugt58Fa -2.328580 8.240297 -15.59627
## FBgn0038299 FBgn0038299 Spn88Eb 3.477954 3.644801 15.43320
## FBgn0085446 FBgn0085446 CG34417 3.510251 5.274943 15.31540
## FBgn0033374 FBgn0033374 CG13741 3.106360 2.141350 15.23863
## FBgn0261451 FBgn0261451 trol -3.260204 8.436969 -14.34764
## FBgn0003231 FBgn0003231 ref(2)P -2.558977 8.521526 -14.31482
## FBgn0036368 FBgn0036368 CG10738 -2.891812 4.304201 -14.41046
## P.Value adj.P.Val B
## FBgn0000212 4.025736e-14 3.865109e-10 21.81769
## FBgn0035763 7.134409e-12 3.424873e-08 17.50692
## FBgn0060296 5.836280e-11 1.286406e-07 15.30288
## FBgn0040091 7.656130e-11 1.286406e-07 15.25413
## FBgn0038299 8.918115e-11 1.286406e-07 14.96880
## FBgn0085446 9.966162e-11 1.286406e-07 14.95453
## FBgn0033374 1.071893e-10 1.286406e-07 14.64271
## FBgn0261451 2.557490e-10 2.306955e-07 14.06143
## FBgn0003231 2.643111e-10 2.306955e-07 14.03208
## FBgn0036368 2.401745e-10 2.306955e-07 14.03022volcanoplot(fit.empty_dsBrm.vs.YN_dsBrm_100, highlight = 10, names = fit.empty_dsBrm.vs.YN_dsBrm_100$genes[,2], main = "empty_dsBrm vs YN_dsBrm_100")
write.fit(fit.empty_dsBrm.vs.YN_dsBrm_100 , results = results.fit.empty_dsBrm.vs.YN_dsBrm_100, file = "empty_dsBrm_vs_YN_dsBrm_100.txt", digits=30, dec = ",", adjust = "BH") # save the results to file# contrast WT_dsGFP vs WT_dsBrm
WT_dsGFP.vs.WT_dsBrm <- makeContrasts(WT_dsGFP - WT_dsBrm, levels = des)
fit.WT_dsGFP.vs.WT_dsBrm <- contrasts.fit(fit, WT_dsGFP.vs.WT_dsBrm)
fit.WT_dsGFP.vs.WT_dsBrm <- eBayes(fit.WT_dsGFP.vs.WT_dsBrm)
fit.WT_dsGFP.vs.WT_dsBrm$genes <- gene.names.limma # add common gene names to the object
results.fit.WT_dsGFP.vs.WT_dsBrm <- decideTests(fit.WT_dsGFP.vs.WT_dsBrm)
table(results.fit.WT_dsGFP.vs.WT_dsBrm)## results.fit.WT_dsGFP.vs.WT_dsBrm
## -1 0
## 3 9598topTable(fit.WT_dsGFP.vs.WT_dsBrm, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr
## FBgn0051361 FBgn0051361 dpr17 -1.1628780 5.6517414
## FBgn0030332 FBgn0030332 CG9360 -1.4490027 4.8311070
## FBgn0259715 FBgn0259715 CG42369 -2.2638820 3.7240859
## FBgn0001089 FBgn0001089 Gal -1.8930300 3.6688653
## FBgn0028985 FBgn0028985 Spn4 -0.8232741 7.0166994
## FBgn0003319 FBgn0003319 Sb -2.1482146 -0.5706676
## FBgn0083959 FBgn0083959 trpm -0.6716413 7.8503244
## FBgn0014163 FBgn0014163 fax 0.9160993 8.6853082
## FBgn0050323 FBgn0050323 CG30323 -1.0172569 5.4612285
## FBgn0027929 FBgn0027929 nimB1 -1.6056953 2.8396444
## t P.Value adj.P.Val B
## FBgn0051361 -9.814923 5.074296e-08 0.0004871832 8.699161
## FBgn0030332 -6.732507 5.890185e-06 0.0282758336 4.210743
## FBgn0259715 -6.372453 1.111369e-05 0.0355675112 3.603429
## FBgn0001089 -5.899694 2.628090e-05 0.0630807321 2.718084
## FBgn0028985 -5.474002 5.854342e-05 0.0802964757 2.002353
## FBgn0003319 -5.572588 4.852773e-05 0.0802964757 1.846502
## FBgn0083959 -5.352215 7.394406e-05 0.0816299582 1.775025
## FBgn0014163 5.334440 7.652011e-05 0.0816299582 1.766296
## FBgn0050323 -5.196991 9.985428e-05 0.0948568344 1.516480
## FBgn0027929 -5.032118 1.378352e-04 0.0971903120 1.237865volcanoplot(fit.WT_dsGFP.vs.WT_dsBrm, highlight = 10, names = fit.WT_dsGFP.vs.WT_dsBrm$genes[,2], main = "WT_dsGFP vs WT_dsBrm")
write.fit(fit.WT_dsGFP.vs.WT_dsBrm , results = results.fit.WT_dsGFP.vs.WT_dsBrm , file="WT_dsGFP_vs_WT_dsBrm.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast WT_dsGFP vs WT_dsBrm_100
WT_dsGFP.vs.WT_dsBrm_100 <- makeContrasts(WT_dsGFP - WT_dsBrm_100, levels = des)
fit.WT_dsGFP.vs.WT_dsBrm_100 <- contrasts.fit(fit, WT_dsGFP.vs.WT_dsBrm_100)
fit.WT_dsGFP.vs.WT_dsBrm_100 <- eBayes(fit.WT_dsGFP.vs.WT_dsBrm_100)
fit.WT_dsGFP.vs.WT_dsBrm_100$genes <- gene.names.limma # add common gene names to the object
results.fit.WT_dsGFP.vs.WT_dsBrm_100 <- decideTests(fit.WT_dsGFP.vs.WT_dsBrm_100)
table(results.fit.WT_dsGFP.vs.WT_dsBrm_100)## results.fit.WT_dsGFP.vs.WT_dsBrm_100
## -1 0 1
## 656 8669 276topTable(fit.WT_dsGFP.vs.WT_dsBrm_100, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr
## FBgn0003231 FBgn0003231 ref(2)P -2.293445 8.5215263
## FBgn0053192 FBgn0053192 MtnD -9.998707 -0.6745924
## FBgn0000212 FBgn0000212 brm -3.312789 10.5329124
## FBgn0037750 FBgn0037750 Whamy -4.470245 4.1745421
## FBgn0053462 FBgn0053462 CG33462 -2.823511 7.2315289
## FBgn0040260 FBgn0040260 Ugt36Bc -3.471470 2.9430625
## FBgn0010041 FBgn0010041 GstD5 -3.377748 7.4410796
## FBgn0042106 FBgn0042106 CG18754 -4.867774 2.8874345
## FBgn0005660 FBgn0005660 Ets21C -2.160937 4.3591115
## FBgn0028516 FBgn0028516 ZnT35C -8.409795 -1.8247788
## t P.Value adj.P.Val B
## FBgn0003231 -12.660485 1.521106e-09 7.302071e-06 12.296409
## FBgn0053192 -16.620858 3.028810e-11 2.907960e-07 11.101693
## FBgn0000212 -10.766906 1.448627e-08 3.177880e-05 10.078582
## FBgn0037750 -10.542329 1.931799e-08 3.177880e-05 9.789946
## FBgn0053462 -10.520944 1.985968e-08 3.177880e-05 9.713021
## FBgn0040260 -9.834191 4.942757e-08 5.272824e-05 8.874122
## FBgn0010041 -9.878793 4.651878e-08 5.272824e-05 8.839304
## FBgn0042106 -9.863942 4.746673e-08 5.272824e-05 8.568102
## FBgn0005660 -9.448603 8.422525e-08 8.086466e-05 8.331365
## FBgn0028516 -11.539627 5.570991e-09 1.782903e-05 8.199299volcanoplot(fit.WT_dsGFP.vs.WT_dsBrm_100, highlight = 10, names = fit.WT_dsGFP.vs.WT_dsBrm_100$genes[,2], main = "WT_dsGFP vs WT_dsBrm_100")
write.fit(fit.WT_dsGFP.vs.WT_dsBrm_100 , results = results.fit.WT_dsGFP.vs.WT_dsBrm_100 , file="WT_dsGFP_vs_WT_dsBrm_100.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast WT_dsGFP vs K804R_dsGFP
WT_dsGFP.vs.K804R_dsGFP <- makeContrasts(WT_dsGFP - K804R_dsGFP, levels = des)
fit.WT_dsGFP.vs.K804R_dsGFP <- contrasts.fit(fit, WT_dsGFP.vs.K804R_dsGFP)
fit.WT_dsGFP.vs.K804R_dsGFP <- eBayes(fit.WT_dsGFP.vs.K804R_dsGFP)
fit.WT_dsGFP.vs.K804R_dsGFP$genes <- gene.names.limma # add common gene names to the object
results.fit.WT_dsGFP.vs.K804R_dsGFP <- decideTests(fit.WT_dsGFP.vs.K804R_dsGFP)
table(results.fit.WT_dsGFP.vs.K804R_dsGFP)## results.fit.WT_dsGFP.vs.K804R_dsGFP
## -1 0 1
## 292 9095 214topTable(fit.WT_dsGFP.vs.K804R_dsGFP, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr t
## FBgn0086251 FBgn0086251 del -2.859810 6.648684 -14.29144
## FBgn0263041 FBgn0263041 CG43336 -4.066900 3.702348 -14.45890
## FBgn0036368 FBgn0036368 CG10738 5.613905 4.304201 14.81655
## FBgn0052207 FBgn0052207 CR32207 -3.643306 3.297262 -13.64020
## FBgn0261673 FBgn0261673 nemy -2.388745 6.498878 -12.99920
## FBgn0051361 FBgn0051361 dpr17 1.690615 5.651741 12.87240
## FBgn0034139 FBgn0034139 CG4927 -3.084744 3.472933 -12.71088
## FBgn0050090 FBgn0050090 CG30090 -2.951461 5.714388 -12.19476
## FBgn0053462 FBgn0053462 CG33462 -3.244070 7.231529 -12.01037
## FBgn0259896 FBgn0259896 nimC1 5.149448 4.568158 11.32005
## P.Value adj.P.Val B
## FBgn0086251 2.705938e-10 8.659904e-07 14.00884
## FBgn0263041 2.288508e-10 8.659904e-07 13.89729
## FBgn0036368 1.609071e-10 8.659904e-07 13.44793
## FBgn0052207 5.278343e-10 1.266934e-06 13.02222
## FBgn0261673 1.046994e-09 1.924939e-06 12.66011
## FBgn0051361 1.202961e-09 1.924939e-06 12.51843
## FBgn0034139 1.438120e-09 1.972484e-06 12.28991
## FBgn0050090 2.577623e-09 3.093470e-06 11.77975
## FBgn0053462 3.190906e-09 3.403988e-06 11.53531
## FBgn0259896 7.270398e-09 5.569653e-06 10.72360volcanoplot(fit.WT_dsGFP.vs.K804R_dsGFP, highlight = 10, names = fit.WT_dsGFP.vs.K804R_dsGFP$genes[,2], main = "WT_dsGFP vs K804R_dsGFP")
write.fit(fit.WT_dsGFP.vs.K804R_dsGFP , results = results.fit.WT_dsGFP.vs.K804R_dsGFP , file="WT_dsGFP_vs_K804R_dsGFP.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast WT_dsGFP vs K804R_dsBrm
WT_dsGFP.vs.K804R_dsBrm <- makeContrasts(WT_dsGFP - K804R_dsBrm, levels = des)
fit.WT_dsGFP.vs.K804R_dsBrm <- contrasts.fit(fit, WT_dsGFP.vs.K804R_dsBrm)
fit.WT_dsGFP.vs.K804R_dsBrm <- eBayes(fit.WT_dsGFP.vs.K804R_dsBrm)
fit.WT_dsGFP.vs.K804R_dsBrm$genes <- gene.names.limma # add common gene names to the object
results.fit.WT_dsGFP.vs.K804R_dsBrm <- decideTests(fit.WT_dsGFP.vs.K804R_dsBrm)
table(results.fit.WT_dsGFP.vs.K804R_dsBrm)## results.fit.WT_dsGFP.vs.K804R_dsBrm
## -1 0 1
## 415 8836 350topTable(fit.WT_dsGFP.vs.K804R_dsBrm, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr t
## FBgn0263041 FBgn0263041 CG43336 -4.277276 3.702348 -15.29190
## FBgn0030596 FBgn0030596 CG12398 4.165383 4.245533 14.49582
## FBgn0036368 FBgn0036368 CG10738 5.526149 4.304201 15.42192
## FBgn0050090 FBgn0050090 CG30090 -3.362595 5.714388 -13.91881
## FBgn0000212 FBgn0000212 brm 3.498311 10.532912 13.73255
## FBgn0053462 FBgn0053462 CG33462 -3.765139 7.231529 -13.68149
## FBgn0052207 FBgn0052207 CR32207 -3.602684 3.297262 -13.55672
## FBgn0034139 FBgn0034139 CG4927 -3.190312 3.472933 -13.27019
## FBgn0086251 FBgn0086251 del -2.604826 6.648684 -13.02441
## FBgn0031470 FBgn0031470 CG18557 -2.337607 7.706933 -11.94797
## P.Value adj.P.Val B
## FBgn0263041 1.019049e-10 4.891946e-07 14.58150
## FBgn0030596 2.206008e-10 7.059961e-07 14.01734
## FBgn0036368 9.013231e-11 4.891946e-07 13.98216
## FBgn0050090 3.952989e-10 7.902621e-07 13.62574
## FBgn0000212 4.793267e-10 7.902621e-07 13.44398
## FBgn0053462 5.055334e-10 7.902621e-07 13.39211
## FBgn0052207 5.761728e-10 7.902621e-07 12.91101
## FBgn0034139 7.810982e-10 9.374155e-07 12.85199
## FBgn0086251 1.018626e-09 1.086648e-06 12.68600
## FBgn0031470 3.431970e-09 3.295034e-06 11.44788volcanoplot(fit.WT_dsGFP.vs.K804R_dsBrm, highlight = 10, names = fit.WT_dsGFP.vs.K804R_dsBrm$genes[,2], main = "WT_dsGFP vs K804R_dsBrm")
write.fit(fit.WT_dsGFP.vs.K804R_dsBrm , results = results.fit.WT_dsGFP.vs.K804R_dsBrm , file="WT_dsGFP_vs_K804R_dsBrm.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast WT_dsGFP vs K804R_dsBrm_100
WT_dsGFP.vs.K804R_dsBrm_100 <- makeContrasts(WT_dsGFP - K804R_dsBrm_100, levels = des)
fit.WT_dsGFP.vs.K804R_dsBrm_100 <- contrasts.fit(fit, WT_dsGFP.vs.K804R_dsBrm_100)
fit.WT_dsGFP.vs.K804R_dsBrm_100 <- eBayes(fit.WT_dsGFP.vs.K804R_dsBrm_100)
fit.WT_dsGFP.vs.K804R_dsBrm_100$genes <- gene.names.limma # add common gene names to the object
results.fit.WT_dsGFP.vs.K804R_dsBrm_100 <- decideTests(fit.WT_dsGFP.vs.K804R_dsBrm_100)
table(results.fit.WT_dsGFP.vs.K804R_dsBrm_100)## results.fit.WT_dsGFP.vs.K804R_dsBrm_100
## -1 0 1
## 734 8366 501topTable(fit.WT_dsGFP.vs.K804R_dsBrm_100, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr t
## FBgn0050090 FBgn0050090 CG30090 -4.081250 5.714388 -16.79433
## FBgn0053462 FBgn0053462 CG33462 -4.510629 7.231529 -16.09919
## FBgn0263041 FBgn0263041 CG43336 -4.261842 3.702348 -15.21125
## FBgn0036368 FBgn0036368 CG10738 4.656788 4.304201 15.05780
## FBgn0030596 FBgn0030596 CG12398 3.959030 4.245533 14.11033
## FBgn0052207 FBgn0052207 CR32207 -3.805681 3.297262 -14.35523
## FBgn0020269 FBgn0020269 mspo -2.548623 5.504223 -13.73911
## FBgn0052626 FBgn0052626 CG32626 -1.779458 8.488482 -12.79552
## FBgn0031470 FBgn0031470 CG18557 -2.491755 7.706933 -12.70690
## FBgn0086251 FBgn0086251 del -2.527504 6.648684 -12.66256
## P.Value adj.P.Val B
## FBgn0050090 2.601955e-11 2.316038e-07 16.23630
## FBgn0053462 4.824577e-11 2.316038e-07 15.70009
## FBgn0263041 1.100180e-10 2.446536e-07 14.51274
## FBgn0036368 1.274105e-10 2.446536e-07 14.10832
## FBgn0030596 3.249776e-10 4.457299e-07 13.68474
## FBgn0052207 2.538127e-10 4.061426e-07 13.63190
## FBgn0020269 4.760641e-10 5.713364e-07 13.45254
## FBgn0052626 1.309351e-09 1.214200e-06 12.42929
## FBgn0031470 1.444497e-09 1.214200e-06 12.32425
## FBgn0086251 1.517591e-09 1.214200e-06 12.28462volcanoplot(fit.WT_dsGFP.vs.K804R_dsBrm_100, highlight = 10, names = fit.WT_dsGFP.vs.K804R_dsBrm_100$genes[,2], main = "WT_dsGFP vs K804R_dsBrm_100")
write.fit(fit.WT_dsGFP.vs.K804R_dsBrm_100 , results = results.fit.WT_dsGFP.vs.K804R_dsBrm_100 , file="WT_dsGFP_vs_K804R_dsBrm_100.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast WT_dsGFP vs YN_dsGFP
WT_dsGFP.vs.YN_dsGFP <- makeContrasts(WT_dsGFP - YN_dsGFP, levels = des)
fit.WT_dsGFP.vs.YN_dsGFP <- contrasts.fit(fit, WT_dsGFP.vs.YN_dsGFP)
fit.WT_dsGFP.vs.YN_dsGFP <- eBayes(fit.WT_dsGFP.vs.YN_dsGFP)
fit.WT_dsGFP.vs.YN_dsGFP$genes <- gene.names.limma # add common gene names to the object
results.fit.WT_dsGFP.vs.YN_dsGFP <- decideTests(fit.WT_dsGFP.vs.YN_dsGFP)
table(results.fit.WT_dsGFP.vs.YN_dsGFP)## results.fit.WT_dsGFP.vs.YN_dsGFP
## -1 0 1
## 146 9287 168topTable(fit.WT_dsGFP.vs.YN_dsGFP, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr
## FBgn0261673 FBgn0261673 nemy -2.268099 6.498878
## FBgn0060296 FBgn0060296 pain -1.691117 4.977743
## FBgn0261089 FBgn0261089 Sytalpha 3.598308 4.150789
## FBgn0031327 FBgn0031327 CG5397 -2.722804 3.658995
## FBgn0051361 FBgn0051361 dpr17 1.331277 5.651741
## FBgn0033374 FBgn0033374 CG13741 -5.042175 2.141350
## FBgn0052206 FBgn0052206 CG32206 2.628444 3.700089
## FBgn0028491 FBgn0028491 CG2930 1.478370 7.447522
## FBgn0053630 FBgn0053630 CG33630 6.741226 -1.505934
## FBgn0036368 FBgn0036368 CG10738 -1.456382 4.304201
## t P.Value adj.P.Val B
## FBgn0261673 -12.344885 2.170718e-09 8.628522e-06 11.925365
## FBgn0060296 -12.211111 2.529630e-09 8.628522e-06 11.786440
## FBgn0261089 11.828547 3.948773e-09 8.628522e-06 11.353404
## FBgn0031327 -11.784404 4.160122e-09 8.628522e-06 11.278687
## FBgn0051361 10.738807 1.501363e-08 1.630317e-05 9.933732
## FBgn0033374 -11.077483 9.803846e-09 1.568779e-05 9.424550
## FBgn0052206 9.958996 4.173356e-08 3.124792e-05 9.036613
## FBgn0028491 9.948825 4.231049e-08 3.124792e-05 8.852938
## FBgn0053630 11.719375 4.493554e-09 8.628522e-06 8.660556
## FBgn0036368 -9.713551 5.829920e-08 3.998076e-05 8.529238volcanoplot(fit.WT_dsGFP.vs.YN_dsGFP, highlight = 10, names = fit.WT_dsGFP.vs.YN_dsGFP$genes[,2], main = "WT_dsGFP vs YN_dsGFP")
write.fit(fit.WT_dsGFP.vs.YN_dsGFP , results = results.fit.WT_dsGFP.vs.YN_dsGFP , file="WT_dsGFP_vs_YN_dsGFP.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast WT_dsGFP vs YN_dsBrm
WT_dsGFP.vs.YN_dsBrm <- makeContrasts(WT_dsGFP - YN_dsBrm, levels = des)
fit.WT_dsGFP.vs.YN_dsBrm <- contrasts.fit(fit, WT_dsGFP.vs.YN_dsBrm)
fit.WT_dsGFP.vs.YN_dsBrm <- eBayes(fit.WT_dsGFP.vs.YN_dsBrm)
fit.WT_dsGFP.vs.YN_dsBrm$genes <- gene.names.limma # add common gene names to the object
results.fit.WT_dsGFP.vs.YN_dsBrm <- decideTests(fit.WT_dsGFP.vs.YN_dsBrm)
table(results.fit.WT_dsGFP.vs.YN_dsBrm)## results.fit.WT_dsGFP.vs.YN_dsBrm
## -1 0 1
## 268 9093 240topTable(fit.WT_dsGFP.vs.YN_dsBrm, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr
## FBgn0060296 FBgn0060296 pain -2.003704 4.977743
## FBgn0031327 FBgn0031327 CG5397 -3.003205 3.658995
## FBgn0030596 FBgn0030596 CG12398 3.334857 4.245533
## FBgn0261673 FBgn0261673 nemy -2.301942 6.498878
## FBgn0036368 FBgn0036368 CG10738 -1.672239 4.304201
## FBgn0261089 FBgn0261089 Sytalpha 3.551268 4.150789
## FBgn0001114 FBgn0001114 Glt 2.634787 10.599846
## FBgn0033374 FBgn0033374 CG13741 -4.796072 2.141350
## FBgn0052206 FBgn0052206 CG32206 2.816228 3.700089
## FBgn0028491 FBgn0028491 CG2930 1.438272 7.447522
## t P.Value adj.P.Val B
## FBgn0060296 -14.388001 2.456234e-10 2.358230e-06 14.103744
## FBgn0031327 -12.978278 1.071174e-09 4.461144e-06 12.645791
## FBgn0030596 12.530323 1.759803e-09 4.461144e-06 12.126854
## FBgn0261673 -12.481827 1.858617e-09 4.461144e-06 12.089097
## FBgn0036368 -11.137816 9.096899e-09 1.718795e-05 10.446261
## FBgn0261089 10.792723 1.401900e-08 1.922806e-05 10.102650
## FBgn0001114 10.478875 2.097272e-08 2.516989e-05 9.663186
## FBgn0033374 -10.351271 2.477075e-08 2.642489e-05 8.592764
## FBgn0052206 9.515447 7.670542e-08 4.657182e-05 8.443429
## FBgn0028491 9.607713 6.746869e-08 4.657182e-05 8.394181volcanoplot(fit.WT_dsGFP.vs.YN_dsBrm, highlight = 10, names = fit.WT_dsGFP.vs.YN_dsBrm$genes[,2], main = "WT_dsGFP vs YN_dsBrm")
write.fit(fit.WT_dsGFP.vs.YN_dsBrm , results = results.fit.WT_dsGFP.vs.YN_dsBrm , file="WT_dsGFP_vs_YN_dsBrm.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast WT_dsGFP vs YN_dsBrm_100
WT_dsGFP.vs.YN_dsBrm_100 <- makeContrasts(WT_dsGFP - YN_dsBrm_100, levels = des)
fit.WT_dsGFP.vs.YN_dsBrm_100 <- contrasts.fit(fit, WT_dsGFP.vs.YN_dsBrm_100)
fit.WT_dsGFP.vs.YN_dsBrm_100 <- eBayes(fit.WT_dsGFP.vs.YN_dsBrm_100)
fit.WT_dsGFP.vs.YN_dsBrm_100$genes <- gene.names.limma # add common gene names to the object
results.fit.WT_dsGFP.vs.YN_dsBrm_100 <- decideTests(fit.WT_dsGFP.vs.YN_dsBrm_100)
table(results.fit.WT_dsGFP.vs.YN_dsBrm_100)## results.fit.WT_dsGFP.vs.YN_dsBrm_100
## -1 0 1
## 1317 7390 894topTable(fit.WT_dsGFP.vs.YN_dsBrm_100, adjust = "BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr t
## FBgn0261673 FBgn0261673 nemy -2.469523 6.498878 -13.42050
## FBgn0003231 FBgn0003231 ref(2)P -2.396247 8.521526 -13.29526
## FBgn0053462 FBgn0053462 CG33462 -3.529354 7.231529 -13.02801
## FBgn0010041 FBgn0010041 GstD5 -4.480750 7.441080 -12.76402
## FBgn0000212 FBgn0000212 brm -3.910366 10.532912 -12.70909
## FBgn0060296 FBgn0060296 pain -1.778247 4.977743 -12.68666
## FBgn0030596 FBgn0030596 CG12398 3.482117 4.245533 12.71624
## FBgn0035996 FBgn0035996 CG3448 -1.946589 4.273045 -12.01645
## FBgn0035868 FBgn0035868 CG7194 -4.059683 5.369298 -11.79196
## FBgn0001114 FBgn0001114 Glt 2.898145 10.599846 11.59841
## P.Value adj.P.Val B
## FBgn0261673 6.653985e-10 1.773056e-06 13.12094
## FBgn0003231 7.604127e-10 1.773056e-06 12.98842
## FBgn0053462 1.014638e-09 1.773056e-06 12.70316
## FBgn0010041 1.355784e-09 1.773056e-06 12.41484
## FBgn0000212 1.440984e-09 1.773056e-06 12.33741
## FBgn0060296 1.477393e-09 1.773056e-06 12.33151
## FBgn0030596 1.429581e-09 1.773056e-06 12.26416
## FBgn0035996 3.168398e-09 3.379976e-06 11.57672
## FBgn0035868 4.123120e-09 3.951378e-06 11.31828
## FBgn0001114 5.191297e-09 3.951378e-06 11.08555volcanoplot(fit.WT_dsGFP.vs.YN_dsBrm_100, highlight = 10, names = fit.WT_dsGFP.vs.YN_dsBrm_100$genes[,2], main = "WT_dsGFP vs YN_dsBrm_100")
write.fit(fit.WT_dsGFP.vs.YN_dsBrm_100 , results = results.fit.WT_dsGFP.vs.YN_dsBrm_100, file = "WT_dsGFP_vs_YN_dsBrm_100.txt", digits=30, dec = ",", adjust = "BH") # save the results to file# contrast WT_dsBrm vs WT_dsBrm_100
WT_dsBrm.vs.WT_dsBrm_100 <- makeContrasts(WT_dsBrm - WT_dsBrm_100, levels = des)
fit.WT_dsBrm.vs.WT_dsBrm_100 <- contrasts.fit(fit, WT_dsBrm.vs.WT_dsBrm_100)
fit.WT_dsBrm.vs.WT_dsBrm_100 <- eBayes(fit.WT_dsBrm.vs.WT_dsBrm_100)
fit.WT_dsBrm.vs.WT_dsBrm_100$genes <- gene.names.limma # add common gene names to the object
results.fit.WT_dsBrm.vs.WT_dsBrm_100 <- decideTests(fit.WT_dsBrm.vs.WT_dsBrm_100)
table(results.fit.WT_dsBrm.vs.WT_dsBrm_100)## results.fit.WT_dsBrm.vs.WT_dsBrm_100
## -1 0 1
## 636 8677 288topTable(fit.WT_dsBrm.vs.WT_dsBrm_100, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr
## FBgn0000212 FBgn0000212 brm -4.238685 10.5329124
## FBgn0053192 FBgn0053192 MtnD -8.655070 -0.6745924
## FBgn0003231 FBgn0003231 ref(2)P -2.143773 8.5215263
## FBgn0051361 FBgn0051361 dpr17 1.251866 5.6517414
## FBgn0037750 FBgn0037750 Whamy -3.659782 4.1745421
## FBgn0040260 FBgn0040260 Ugt36Bc -3.500956 2.9430625
## FBgn0031589 FBgn0031589 CG3714 -1.457274 7.1548585
## FBgn0042106 FBgn0042106 CG18754 -3.597500 2.8874345
## FBgn0028516 FBgn0028516 ZnT35C -7.529289 -1.8247788
## FBgn0035904 FBgn0035904 CG6776 -2.057723 7.2038795
## t P.Value adj.P.Val B
## FBgn0000212 -14.134882 3.169696e-10 1.521612e-06 13.763677
## FBgn0053192 -17.039792 2.103763e-11 2.019822e-07 11.889052
## FBgn0003231 -11.828428 3.949327e-09 1.263916e-05 11.356863
## FBgn0051361 10.542641 1.931019e-08 3.707943e-05 9.738498
## FBgn0037750 -9.720582 5.773860e-08 8.843375e-05 8.721793
## FBgn0040260 -9.640492 6.447623e-08 8.843375e-05 8.599475
## FBgn0031589 -9.034895 1.518916e-07 1.553119e-04 7.647276
## FBgn0042106 -8.947836 1.723769e-07 1.553119e-04 7.545238
## FBgn0028516 -10.830223 1.336842e-08 3.208755e-05 7.495148
## FBgn0035904 -8.827465 2.056207e-07 1.553119e-04 7.346980volcanoplot(fit.WT_dsBrm.vs.WT_dsBrm_100, highlight = 10, names = fit.WT_dsBrm.vs.WT_dsBrm_100$genes[,2], main = "WT_dsBrm vs WT_dsBrm_100")
write.fit(fit.WT_dsBrm.vs.WT_dsBrm_100 , results = results.fit.WT_dsBrm.vs.WT_dsBrm_100 , file="WT_dsBrm_vs_WT_dsBrm_100.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast WT_dsBrm vs K804R_dsGFP
WT_dsBrm.vs.K804R_dsGFP <- makeContrasts(WT_dsBrm - K804R_dsGFP, levels = des)
fit.WT_dsBrm.vs.K804R_dsGFP <- contrasts.fit(fit, WT_dsBrm.vs.K804R_dsGFP)
fit.WT_dsBrm.vs.K804R_dsGFP <- eBayes(fit.WT_dsBrm.vs.K804R_dsGFP)
fit.WT_dsBrm.vs.K804R_dsGFP$genes <- gene.names.limma # add common gene names to the object
results.fit.WT_dsBrm.vs.K804R_dsGFP <- decideTests(fit.WT_dsBrm.vs.K804R_dsGFP)
table(results.fit.WT_dsBrm.vs.K804R_dsGFP)## results.fit.WT_dsBrm.vs.K804R_dsGFP
## -1 0 1
## 355 8930 316topTable(fit.WT_dsBrm.vs.K804R_dsGFP, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr t
## FBgn0051361 FBgn0051361 dpr17 2.853493 5.651741 21.66417
## FBgn0261673 FBgn0261673 nemy -2.913145 6.498878 -14.57741
## FBgn0036368 FBgn0036368 CG10738 5.935277 4.304201 15.68624
## FBgn0086251 FBgn0086251 del -2.945597 6.648684 -14.38918
## FBgn0263041 FBgn0263041 CG43336 -3.612936 3.702348 -13.65563
## FBgn0052207 FBgn0052207 CR32207 -3.333527 3.297262 -12.90927
## FBgn0033724 FBgn0033724 CG8501 3.798040 7.129524 12.71624
## FBgn0052626 FBgn0052626 CG32626 -1.666198 8.488482 -12.20645
## FBgn0035608 FBgn0035608 blanks -3.395542 5.798946 -11.98711
## FBgn0259896 FBgn0259896 nimC1 5.365205 4.568158 11.79182
## P.Value adj.P.Val B
## FBgn0051361 6.017368e-13 5.777275e-09 19.93848
## FBgn0261673 2.034687e-10 5.888635e-07 14.29248
## FBgn0036368 7.042279e-11 3.380646e-07 14.18441
## FBgn0086251 2.453343e-10 5.888635e-07 14.10578
## FBgn0263041 5.193828e-10 9.973188e-07 13.24519
## FBgn0052207 1.155216e-09 1.848538e-06 12.39659
## FBgn0033724 1.429579e-09 1.960770e-06 12.34317
## FBgn0052626 2.543210e-09 3.052170e-06 11.73083
## FBgn0035608 3.278588e-09 3.497525e-06 11.53338
## FBgn0259896 4.123773e-09 3.599304e-06 11.27839volcanoplot(fit.WT_dsBrm.vs.K804R_dsGFP, highlight = 10, names = fit.WT_dsBrm.vs.K804R_dsGFP$genes[,2], main = "WT_dsBrm vs K804R_dsGFP")
write.fit(fit.WT_dsBrm.vs.K804R_dsGFP , results = results.fit.WT_dsBrm.vs.K804R_dsGFP , file="WT_dsBrm_vs_K804R_dsGFP.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast WT_dsBrm vs K804R_dsBrm
WT_dsBrm.vs.K804R_dsBrm <- makeContrasts(WT_dsBrm - K804R_dsBrm, levels = des)
fit.WT_dsBrm.vs.K804R_dsBrm <- contrasts.fit(fit, WT_dsBrm.vs.K804R_dsBrm)
fit.WT_dsBrm.vs.K804R_dsBrm <- eBayes(fit.WT_dsBrm.vs.K804R_dsBrm)
fit.WT_dsBrm.vs.K804R_dsBrm$genes <- gene.names.limma # add common gene names to the object
results.fit.WT_dsBrm.vs.K804R_dsBrm <- decideTests(fit.WT_dsBrm.vs.K804R_dsBrm)
table(results.fit.WT_dsBrm.vs.K804R_dsBrm)## results.fit.WT_dsBrm.vs.K804R_dsBrm
## -1 0 1
## 251 9088 262topTable(fit.WT_dsBrm.vs.K804R_dsBrm, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr t
## FBgn0051361 FBgn0051361 dpr17 2.579536 5.651741 20.53532
## FBgn0036368 FBgn0036368 CG10738 5.847521 4.304201 16.34383
## FBgn0263041 FBgn0263041 CG43336 -3.823312 3.702348 -14.54223
## FBgn0086251 FBgn0086251 del -2.690613 6.648684 -13.15054
## FBgn0050090 FBgn0050090 CG30090 -3.073519 5.714388 -13.02625
## FBgn0052207 FBgn0052207 CR32207 -3.292905 3.297262 -12.82139
## FBgn0259896 FBgn0259896 nimC1 5.559806 4.568158 12.25589
## FBgn0030596 FBgn0030596 CG12398 3.370657 4.245533 11.54311
## FBgn0053462 FBgn0053462 CG33462 -3.015539 7.231529 -11.53169
## FBgn0033724 FBgn0033724 CG8501 3.201126 7.129524 11.47615
## P.Value adj.P.Val B
## FBgn0051361 1.335855e-12 1.282554e-08 19.21317
## FBgn0036368 3.871876e-11 1.858694e-07 14.79068
## FBgn0263041 2.106779e-10 6.742394e-07 14.07702
## FBgn0086251 8.884058e-10 1.952035e-06 12.81771
## FBgn0050090 1.016579e-09 1.952035e-06 12.70079
## FBgn0052207 1.272482e-09 2.036183e-06 12.30319
## FBgn0259896 2.402956e-09 3.295826e-06 11.78875
## FBgn0030596 5.547706e-09 5.480670e-06 11.00419
## FBgn0053462 5.624452e-09 5.480670e-06 10.94008
## FBgn0033724 6.014187e-09 5.480670e-06 10.86658volcanoplot(fit.WT_dsBrm.vs.K804R_dsBrm, highlight = 10, names = fit.WT_dsBrm.vs.K804R_dsBrm$genes[,2], main = "WT_dsBrm vs K804R_dsBrm")
write.fit(fit.WT_dsBrm.vs.K804R_dsBrm , results = results.fit.WT_dsBrm.vs.K804R_dsBrm , file="WT_dsBrm_vs_K804R_dsBrm.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast WT_dsBrm vs K804R_dsBrm_100
WT_dsBrm.vs.K804R_dsBrm_100 <- makeContrasts(WT_dsBrm - K804R_dsBrm_100, levels = des)
fit.WT_dsBrm.vs.K804R_dsBrm_100 <- contrasts.fit(fit, WT_dsBrm.vs.K804R_dsBrm_100)
fit.WT_dsBrm.vs.K804R_dsBrm_100 <- eBayes(fit.WT_dsBrm.vs.K804R_dsBrm_100)
fit.WT_dsBrm.vs.K804R_dsBrm_100$genes <- gene.names.limma # add common gene names to the object
results.fit.WT_dsBrm.vs.K804R_dsBrm_100 <- decideTests(fit.WT_dsBrm.vs.K804R_dsBrm_100)
table(results.fit.WT_dsBrm.vs.K804R_dsBrm_100)## results.fit.WT_dsBrm.vs.K804R_dsBrm_100
## -1 0 1
## 543 8716 342topTable(fit.WT_dsBrm.vs.K804R_dsBrm_100, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr t
## FBgn0051361 FBgn0051361 dpr17 2.748712 5.651741 21.37574
## FBgn0050090 FBgn0050090 CG30090 -3.792174 5.714388 -15.97317
## FBgn0036368 FBgn0036368 CG10738 4.978160 4.304201 16.13016
## FBgn0263041 FBgn0263041 CG43336 -3.807879 3.702348 -14.45613
## FBgn0261673 FBgn0261673 nemy -2.836966 6.498878 -14.18438
## FBgn0052626 FBgn0052626 CG32626 -1.954287 8.488482 -14.11711
## FBgn0053462 FBgn0053462 CG33462 -3.761029 7.231529 -14.10031
## FBgn0052207 FBgn0052207 CR32207 -3.495901 3.297262 -13.64705
## FBgn0020269 FBgn0020269 mspo -2.382389 5.504223 -12.88887
## FBgn0086251 FBgn0086251 del -2.613291 6.648684 -12.79655
## P.Value adj.P.Val B
## FBgn0051361 7.349620e-13 7.056370e-09 19.72703
## FBgn0050090 5.409816e-11 1.425703e-07 15.55843
## FBgn0036368 4.691299e-11 1.425703e-07 14.95275
## FBgn0263041 2.294818e-10 3.940070e-07 13.95227
## FBgn0261673 3.014589e-10 3.940070e-07 13.90010
## FBgn0052626 3.227424e-10 3.940070e-07 13.83246
## FBgn0053462 3.283050e-10 3.940070e-07 13.81901
## FBgn0052207 5.240660e-10 5.590620e-07 13.06021
## FBgn0020269 1.181371e-09 1.134234e-06 12.54868
## FBgn0086251 1.307873e-09 1.141535e-06 12.43782volcanoplot(fit.WT_dsBrm.vs.K804R_dsBrm_100, highlight = 10, names = fit.WT_dsBrm.vs.K804R_dsBrm_100$genes[,2], main = "WT_dsBrm vs K804R_dsBrm_100")
write.fit(fit.WT_dsBrm.vs.K804R_dsBrm_100 , results = results.fit.WT_dsBrm.vs.K804R_dsBrm_100 , file="WT_dsBrm_vs_K804R_dsBrm_100.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast WT_dsBrm vs YN_dsGFP
WT_dsBrm.vs.YN_dsGFP <- makeContrasts(WT_dsBrm - YN_dsGFP, levels = des)
fit.WT_dsBrm.vs.YN_dsGFP <- contrasts.fit(fit, WT_dsBrm.vs.YN_dsGFP)
fit.WT_dsBrm.vs.YN_dsGFP <- eBayes(fit.WT_dsBrm.vs.YN_dsGFP)
fit.WT_dsBrm.vs.YN_dsGFP$genes <- gene.names.limma # add common gene names to the object
results.fit.WT_dsBrm.vs.YN_dsGFP <- decideTests(fit.WT_dsBrm.vs.YN_dsGFP)
table(results.fit.WT_dsBrm.vs.YN_dsGFP)## results.fit.WT_dsBrm.vs.YN_dsGFP
## -1 0 1
## 215 9095 291topTable(fit.WT_dsBrm.vs.YN_dsGFP, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr t
## FBgn0051361 FBgn0051361 dpr17 2.494155 5.651741 20.05438
## FBgn0261673 FBgn0261673 nemy -2.792498 6.498878 -13.97583
## FBgn0060296 FBgn0060296 pain -1.755715 4.977743 -12.34115
## FBgn0031327 FBgn0031327 CG5397 -2.921703 3.658995 -12.10866
## FBgn0261089 FBgn0261089 Sytalpha 3.494094 4.150789 11.44540
## FBgn0032666 FBgn0032666 CG5758 3.704869 7.555261 11.39781
## FBgn0036454 FBgn0036454 CG17839 2.692738 5.523289 10.81811
## FBgn0011591 FBgn0011591 fng 2.532789 4.616138 10.30277
## FBgn0038611 FBgn0038611 CG14309 2.742399 5.447557 10.16806
## FBgn0000489 FBgn0000489 Pka-C3 -2.607773 3.582247 -10.09543
## P.Value adj.P.Val B
## FBgn0051361 1.900030e-12 1.824219e-08 18.855063
## FBgn0261673 3.728129e-10 1.789688e-06 13.691097
## FBgn0060296 2.179962e-09 6.833028e-06 11.944415
## FBgn0031327 2.846799e-09 6.833028e-06 11.676698
## FBgn0261089 6.242161e-09 1.058218e-05 10.901676
## FBgn0032666 6.613174e-09 1.058218e-05 10.798057
## FBgn0036454 1.357505e-08 1.448156e-05 10.114811
## FBgn0011591 2.639935e-08 2.019065e-05 9.489499
## FBgn0038611 3.154461e-08 2.019065e-05 9.251437
## FBgn0000489 3.474856e-08 2.085131e-05 9.220570volcanoplot(fit.WT_dsBrm.vs.YN_dsGFP, highlight = 10, names = fit.WT_dsBrm.vs.YN_dsGFP$genes[,2], main = "WT_dsBrm vs YN_dsGFP")
write.fit(fit.WT_dsBrm.vs.YN_dsGFP , results = results.fit.WT_dsBrm.vs.YN_dsGFP , file="WT_dsBrm_vs_YN_dsGFP.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast WT_dsBrm vs YN_dsBrm
WT_dsBrm.vs.YN_dsBrm <- makeContrasts(WT_dsBrm - YN_dsBrm, levels = des)
fit.WT_dsBrm.vs.YN_dsBrm <- contrasts.fit(fit, WT_dsBrm.vs.YN_dsBrm)
fit.WT_dsBrm.vs.YN_dsBrm <- eBayes(fit.WT_dsBrm.vs.YN_dsBrm)
fit.WT_dsBrm.vs.YN_dsBrm$genes <- gene.names.limma # add common gene names to the object
results.fit.WT_dsBrm.vs.YN_dsBrm <- decideTests(fit.WT_dsBrm.vs.YN_dsBrm)
table(results.fit.WT_dsBrm.vs.YN_dsBrm)## results.fit.WT_dsBrm.vs.YN_dsBrm
## -1 0 1
## 192 9209 200topTable(fit.WT_dsBrm.vs.YN_dsBrm, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr t
## FBgn0060296 FBgn0060296 pain -2.068302 4.977743 -14.461948
## FBgn0261673 FBgn0261673 nemy -2.826341 6.498878 -14.100054
## FBgn0031327 FBgn0031327 CG5397 -3.202104 3.658995 -13.252295
## FBgn0051361 FBgn0051361 dpr17 1.535688 5.651741 12.697750
## FBgn0261089 FBgn0261089 Sytalpha 3.447054 4.150789 10.444352
## FBgn0036454 FBgn0036454 CG17839 2.611623 5.523289 10.046358
## FBgn0016122 FBgn0016122 Acer 1.961736 5.306564 9.683257
## FBgn0030596 FBgn0030596 CG12398 2.540131 4.245533 9.367552
## FBgn0259240 FBgn0259240 Ten-a 2.289057 5.722983 9.416616
## FBgn0030262 FBgn0030262 Vago 4.276001 4.009945 9.380147
## P.Value adj.P.Val B
## FBgn0060296 2.281574e-10 1.576438e-06 14.165449
## FBgn0261673 3.283904e-10 1.576438e-06 13.811442
## FBgn0031327 7.962355e-10 2.548219e-06 12.943046
## FBgn0051361 1.459271e-09 3.502616e-06 12.314328
## FBgn0261089 2.193535e-08 3.510021e-05 9.663013
## FBgn0036454 3.710704e-08 5.089496e-05 9.123772
## FBgn0016122 6.078110e-08 6.256538e-05 8.600168
## FBgn0030596 9.440002e-08 6.256538e-05 8.236456
## FBgn0259240 8.809527e-08 6.256538e-05 8.233143
## FBgn0030262 9.273751e-08 6.256538e-05 8.174069volcanoplot(fit.WT_dsBrm.vs.YN_dsBrm, highlight = 10, names = fit.WT_dsBrm.vs.YN_dsBrm$genes[,2], main = "WT_dsBrm vs YN_dsBrm")
write.fit(fit.WT_dsBrm.vs.YN_dsBrm , results = results.fit.WT_dsBrm.vs.YN_dsBrm , file="WT_dsBrm_vs_YN_dsBrm.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast WT_dsBrm vs YN_dsBrm_100
WT_dsBrm.vs.YN_dsBrm_100 <- makeContrasts(WT_dsBrm - YN_dsBrm_100, levels = des)
fit.WT_dsBrm.vs.YN_dsBrm_100 <- contrasts.fit(fit, WT_dsBrm.vs.YN_dsBrm_100)
fit.WT_dsBrm.vs.YN_dsBrm_100 <- eBayes(fit.WT_dsBrm.vs.YN_dsBrm_100)
fit.WT_dsBrm.vs.YN_dsBrm_100$genes <- gene.names.limma # add common gene names to the object
results.fit.WT_dsBrm.vs.YN_dsBrm_100 <- decideTests(fit.WT_dsBrm.vs.YN_dsBrm_100)
table(results.fit.WT_dsBrm.vs.YN_dsBrm_100)## results.fit.WT_dsBrm.vs.YN_dsBrm_100
## -1 0 1
## 1353 7286 962topTable(fit.WT_dsBrm.vs.YN_dsBrm_100, adjust = "BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr
## FBgn0051361 FBgn0051361 dpr17 2.677151 5.6517414
## FBgn0000212 FBgn0000212 brm -4.836261 10.5329124
## FBgn0261673 FBgn0261673 nemy -2.993923 6.4988781
## FBgn0060296 FBgn0060296 pain -1.842845 4.9777433
## FBgn0003231 FBgn0003231 ref(2)P -2.246575 8.5215263
## FBgn0036984 FBgn0036984 CG13248 -2.892918 3.4083829
## FBgn0035996 FBgn0035996 CG3448 -2.042576 4.2730452
## FBgn0053192 FBgn0053192 MtnD -8.180466 -0.6745924
## FBgn0010041 FBgn0010041 GstD5 -3.939903 7.4410796
## FBgn0032666 FBgn0032666 CG5758 3.958691 7.5552611
## t P.Value adj.P.Val B
## FBgn0051361 20.31046 1.573528e-12 1.510744e-08 18.81528
## FBgn0000212 -16.12764 4.702004e-11 1.574691e-07 15.52765
## FBgn0261673 -14.96439 1.394067e-10 3.346108e-07 14.58187
## FBgn0060296 -12.80663 1.293388e-09 2.483564e-06 12.44431
## FBgn0003231 -12.45863 1.907956e-09 2.739096e-06 12.07928
## FBgn0036984 -12.41829 1.997050e-09 2.739096e-06 11.93399
## FBgn0035996 -12.21484 2.518805e-09 3.022880e-06 11.77609
## FBgn0053192 -16.07748 4.920397e-11 1.574691e-07 11.43687
## FBgn0010041 -11.48027 5.984312e-09 5.636377e-06 10.94189
## FBgn0032666 11.45034 6.204941e-09 5.636377e-06 10.91390volcanoplot(fit.WT_dsBrm.vs.YN_dsBrm_100, highlight = 10, names = fit.WT_dsBrm.vs.YN_dsBrm_100$genes[,2], main = "WT_dsBrm vs YN_dsBrm_100")
write.fit(fit.WT_dsBrm.vs.YN_dsBrm_100 , results = results.fit.WT_dsBrm.vs.YN_dsBrm_100, file = "WT_dsBrm_vs_YN_dsBrm_100.txt", digits=30, dec = ",", adjust = "BH") # save the results to file# contrast WT_dsBrm_100 vs K804R_dsGFP
WT_dsBrm_100.vs.K804R_dsGFP <- makeContrasts(WT_dsBrm_100 - K804R_dsGFP, levels = des)
fit.WT_dsBrm_100.vs.K804R_dsGFP <- contrasts.fit(fit, WT_dsBrm_100.vs.K804R_dsGFP)
fit.WT_dsBrm_100.vs.K804R_dsGFP <- eBayes(fit.WT_dsBrm_100.vs.K804R_dsGFP)
fit.WT_dsBrm_100.vs.K804R_dsGFP$genes <- gene.names.limma # add common gene names to the object
results.fit.WT_dsBrm_100.vs.K804R_dsGFP <- decideTests(fit.WT_dsBrm_100.vs.K804R_dsGFP)
table(results.fit.WT_dsBrm_100.vs.K804R_dsGFP)## results.fit.WT_dsBrm_100.vs.K804R_dsGFP
## -1 0 1
## 850 7808 943topTable(fit.WT_dsBrm_100.vs.K804R_dsGFP, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr
## FBgn0000212 FBgn0000212 brm 4.589343 10.5329124
## FBgn0086251 FBgn0086251 del -3.064802 6.6486839
## FBgn0052207 FBgn0052207 CR32207 -3.785981 3.2972615
## FBgn0034139 FBgn0034139 CG4927 -4.044425 3.4729332
## FBgn0003231 FBgn0003231 ref(2)P 2.348437 8.5215263
## FBgn0259896 FBgn0259896 nimC1 5.876306 4.5681578
## FBgn0028940 FBgn0028940 Cyp28a5 5.195220 2.5473942
## FBgn0051361 FBgn0051361 dpr17 1.601626 5.6517414
## FBgn0053192 FBgn0053192 MtnD 9.806797 -0.6745924
## FBgn0036368 FBgn0036368 CG10738 4.745430 4.3042006
## t P.Value adj.P.Val B
## FBgn0000212 15.43362 8.914615e-11 4.279461e-07 15.07718
## FBgn0086251 -14.86868 1.529498e-10 4.894904e-07 14.57756
## FBgn0052207 -13.65988 5.170805e-10 1.136375e-06 13.00528
## FBgn0034139 -13.53131 5.918005e-10 1.136375e-06 12.91131
## FBgn0003231 13.02106 1.022346e-09 1.402221e-06 12.67435
## FBgn0259896 12.87043 1.205571e-09 1.446836e-06 12.42981
## FBgn0028940 13.10285 9.354339e-10 1.402221e-06 12.38194
## FBgn0051361 12.17288 2.643382e-09 2.537911e-06 11.72151
## FBgn0053192 16.29101 4.059195e-11 3.897233e-07 11.68593
## FBgn0036368 12.36346 2.125311e-09 2.267235e-06 11.37956volcanoplot(fit.WT_dsBrm_100.vs.K804R_dsGFP, highlight = 10, names = fit.WT_dsBrm_100.vs.K804R_dsGFP$genes[,2], main = "WT_dsBrm_100 vs K804R_dsGFP")
write.fit(fit.WT_dsBrm_100.vs.K804R_dsGFP , results = results.fit.WT_dsBrm_100.vs.K804R_dsGFP , file="WT_dsBrm_100_vs_K804R_dsGFP.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast WT_dsBrm_100 vs K804R_dsBrm
WT_dsBrm_100.vs.K804R_dsBrm <- makeContrasts(WT_dsBrm_100 - K804R_dsBrm, levels = des)
fit.WT_dsBrm_100.vs.K804R_dsBrm <- contrasts.fit(fit, WT_dsBrm_100.vs.K804R_dsBrm)
fit.WT_dsBrm_100.vs.K804R_dsBrm <- eBayes(fit.WT_dsBrm_100.vs.K804R_dsBrm)
fit.WT_dsBrm_100.vs.K804R_dsBrm$genes <- gene.names.limma # add common gene names to the object
results.fit.WT_dsBrm_100.vs.K804R_dsBrm <- decideTests(fit.WT_dsBrm_100.vs.K804R_dsBrm)
table(results.fit.WT_dsBrm_100.vs.K804R_dsBrm)## results.fit.WT_dsBrm_100.vs.K804R_dsBrm
## -1 0 1
## 831 7736 1034topTable(fit.WT_dsBrm_100.vs.K804R_dsBrm, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr t
## FBgn0000212 FBgn0000212 brm 6.811100 10.532912 23.79409
## FBgn0086251 FBgn0086251 del -2.809818 6.648684 -13.63876
## FBgn0034139 FBgn0034139 CG4927 -4.149993 3.472933 -13.97065
## FBgn0028940 FBgn0028940 Cyp28a5 5.330929 2.547394 13.76268
## FBgn0052207 FBgn0052207 CR32207 -3.745360 3.297262 -13.57711
## FBgn0259896 FBgn0259896 nimC1 6.070907 4.568158 13.33594
## FBgn0003231 FBgn0003231 ref(2)P 2.340980 8.521526 12.91542
## FBgn0263041 FBgn0263041 CG43336 -2.699911 3.702348 -12.69586
## FBgn0036368 FBgn0036368 CG10738 4.657674 4.304201 12.81190
## FBgn0035763 FBgn0035763 CG8602 1.955211 9.088558 12.05736
## P.Value adj.P.Val B
## FBgn0000212 1.478575e-13 1.419580e-09 21.00918
## FBgn0086251 5.286341e-10 9.024128e-07 13.34334
## FBgn0034139 3.747986e-10 9.024128e-07 13.30254
## FBgn0028940 4.645411e-10 9.024128e-07 13.00467
## FBgn0052207 5.639492e-10 9.024128e-07 12.91567
## FBgn0259896 7.280617e-10 9.985886e-07 12.89126
## FBgn0003231 1.147451e-09 1.371746e-06 12.56272
## FBgn0263041 1.462337e-09 1.403990e-06 12.33800
## FBgn0036368 1.285878e-09 1.371746e-06 11.88577
## FBgn0035763 3.021220e-09 2.636976e-06 11.58720volcanoplot(fit.WT_dsBrm_100.vs.K804R_dsBrm, highlight = 10, names = fit.WT_dsBrm_100.vs.K804R_dsBrm$genes[,2], main = "WT_dsBrm_100 vs K804R_dsBrm")
write.fit(fit.WT_dsBrm_100.vs.K804R_dsBrm , results = results.fit.WT_dsBrm_100.vs.K804R_dsBrm , file="WT_dsBrm_100_vs_K804R_dsBrm.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast WT_dsBrm_100 vs K804R_dsBrm_100
WT_dsBrm_100.vs.K804R_dsBrm_100 <- makeContrasts(WT_dsBrm_100 - K804R_dsBrm_100, levels = des)
fit.WT_dsBrm_100.vs.K804R_dsBrm_100 <- contrasts.fit(fit, WT_dsBrm_100.vs.K804R_dsBrm_100)
fit.WT_dsBrm_100.vs.K804R_dsBrm_100 <- eBayes(fit.WT_dsBrm_100.vs.K804R_dsBrm_100)
fit.WT_dsBrm_100.vs.K804R_dsBrm_100$genes <- gene.names.limma # add common gene names to the object
results.fit.WT_dsBrm_100.vs.K804R_dsBrm_100 <- decideTests(fit.WT_dsBrm_100.vs.K804R_dsBrm_100)
table(results.fit.WT_dsBrm_100.vs.K804R_dsBrm_100)## results.fit.WT_dsBrm_100.vs.K804R_dsBrm_100
## -1 0 1
## 356 8876 369topTable(fit.WT_dsBrm_100.vs.K804R_dsBrm_100, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr t
## FBgn0052207 FBgn0052207 CR32207 -3.948356 3.297262 -14.34510
## FBgn0086251 FBgn0086251 del -2.732496 6.648684 -13.28793
## FBgn0259896 FBgn0259896 nimC1 5.408860 4.568158 13.29260
## FBgn0028940 FBgn0028940 Cyp28a5 4.013318 2.547394 13.27782
## FBgn0034139 FBgn0034139 CG4927 -3.861172 3.472933 -12.89463
## FBgn0263041 FBgn0263041 CG43336 -2.684477 3.702348 -12.58686
## FBgn0052626 FBgn0052626 CG32626 -1.676630 8.488482 -12.05021
## FBgn0036368 FBgn0036368 CG10738 3.788312 4.304201 12.01552
## FBgn0051361 FBgn0051361 dpr17 1.496845 5.651741 11.65356
## FBgn0085400 FBgn0085400 CG34371 -3.537621 1.396259 -11.54572
## P.Value adj.P.Val B
## FBgn0052207 2.564003e-10 1.859574e-06 13.54850
## FBgn0086251 7.663958e-10 1.859574e-06 12.97604
## FBgn0259896 7.625803e-10 1.859574e-06 12.90942
## FBgn0028940 7.747417e-10 1.859574e-06 12.83539
## FBgn0034139 1.173926e-09 2.254172e-06 12.23923
## FBgn0263041 1.651582e-09 2.642807e-06 12.21390
## FBgn0052626 3.046387e-09 3.806588e-06 11.57786
## FBgn0036368 3.171827e-09 3.806588e-06 11.29649
## FBgn0051361 4.859961e-09 4.312373e-06 11.11221
## FBgn0085400 5.530323e-09 4.424719e-06 10.88117volcanoplot(fit.WT_dsBrm_100.vs.K804R_dsBrm_100, highlight = 10, names = fit.WT_dsBrm_100.vs.K804R_dsBrm_100$genes[,2], main = "WT_dsBrm_100 vs K804R_dsBrm_100")
write.fit(fit.WT_dsBrm_100.vs.K804R_dsBrm_100 , results = results.fit.WT_dsBrm_100.vs.K804R_dsBrm_100 , file="WT_dsBrm_100_vs_K804R_dsBrm_100.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast WT_dsBrm_100 vs YN_dsGFP
WT_dsBrm_100.vs.YN_dsGFP <- makeContrasts(WT_dsBrm_100 - YN_dsGFP, levels = des)
fit.WT_dsBrm_100.vs.YN_dsGFP <- contrasts.fit(fit, WT_dsBrm_100.vs.YN_dsGFP)
fit.WT_dsBrm_100.vs.YN_dsGFP <- eBayes(fit.WT_dsBrm_100.vs.YN_dsGFP)
fit.WT_dsBrm_100.vs.YN_dsGFP$genes <- gene.names.limma # add common gene names to the object
results.fit.WT_dsBrm_100.vs.YN_dsGFP <- decideTests(fit.WT_dsBrm_100.vs.YN_dsGFP)
table(results.fit.WT_dsBrm_100.vs.YN_dsGFP)## results.fit.WT_dsBrm_100.vs.YN_dsGFP
## -1 0 1
## 544 8104 953topTable(fit.WT_dsBrm_100.vs.YN_dsGFP, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr
## FBgn0036368 FBgn0036368 CG10738 -2.324857 4.3042006
## FBgn0053192 FBgn0053192 MtnD 8.894276 -0.6745924
## FBgn0028940 FBgn0028940 Cyp28a5 3.522897 2.5473942
## FBgn0031327 FBgn0031327 CG5397 -2.985556 3.6589947
## FBgn0003231 FBgn0003231 ref(2)P 2.193016 8.5215263
## FBgn0060296 FBgn0060296 pain -1.679472 4.9777433
## FBgn0261451 FBgn0261451 trol -2.549455 8.4369685
## FBgn0033204 FBgn0033204 CG2065 2.690088 5.3511720
## FBgn0000477 FBgn0000477 DNaseII 5.322971 -0.3213821
## FBgn0037750 FBgn0037750 Whamy 4.138524 4.1745421
## t P.Value adj.P.Val B
## FBgn0036368 -14.35146 2.547735e-10 1.223040e-06 14.069592
## FBgn0053192 17.49939 1.423513e-11 1.366715e-07 13.362302
## FBgn0028940 12.97566 1.074239e-09 3.437923e-06 12.616715
## FBgn0031327 -12.34580 2.168451e-09 4.867492e-06 11.942838
## FBgn0003231 12.04789 3.054631e-09 4.867492e-06 11.572151
## FBgn0060296 -12.01784 3.163257e-09 4.867492e-06 11.562621
## FBgn0261451 -11.50884 5.781491e-09 5.550809e-06 10.916132
## FBgn0033204 11.24377 7.982689e-09 6.967436e-06 10.608788
## FBgn0000477 11.80588 4.055821e-09 4.867492e-06 10.411467
## FBgn0037750 10.56937 1.865534e-08 1.377768e-05 9.825601volcanoplot(fit.WT_dsBrm_100.vs.YN_dsGFP, highlight = 10, names = fit.WT_dsBrm_100.vs.YN_dsGFP$genes[,2], main = "WT_dsBrm_100 vs YN_dsGFP")
write.fit(fit.WT_dsBrm_100.vs.YN_dsGFP , results = results.fit.WT_dsBrm_100.vs.YN_dsGFP , file="WT_dsBrm_100_vs_YN_dsGFP.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast WT_dsBrm_100 vs YN_dsBrm
WT_dsBrm_100.vs.YN_dsBrm <- makeContrasts(WT_dsBrm_100 - YN_dsBrm, levels = des)
fit.WT_dsBrm_100.vs.YN_dsBrm <- contrasts.fit(fit, WT_dsBrm_100.vs.YN_dsBrm)
fit.WT_dsBrm_100.vs.YN_dsBrm <- eBayes(fit.WT_dsBrm_100.vs.YN_dsBrm)
fit.WT_dsBrm_100.vs.YN_dsBrm$genes <- gene.names.limma # add common gene names to the object
results.fit.WT_dsBrm_100.vs.YN_dsBrm <- decideTests(fit.WT_dsBrm_100.vs.YN_dsBrm)
table(results.fit.WT_dsBrm_100.vs.YN_dsBrm)## results.fit.WT_dsBrm_100.vs.YN_dsBrm
## -1 0 1
## 532 8277 792topTable(fit.WT_dsBrm_100.vs.YN_dsBrm, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr
## FBgn0036368 FBgn0036368 CG10738 -2.540714 4.3042006
## FBgn0000212 FBgn0000212 brm 4.323252 10.5329124
## FBgn0060296 FBgn0060296 pain -1.992059 4.9777433
## FBgn0031327 FBgn0031327 CG5397 -3.265957 3.6589947
## FBgn0003231 FBgn0003231 ref(2)P 2.276980 8.5215263
## FBgn0053192 FBgn0053192 MtnD 10.440250 -0.6745924
## FBgn0028940 FBgn0028940 Cyp28a5 3.452745 2.5473942
## FBgn0261451 FBgn0261451 trol -2.609721 8.4369685
## FBgn0033204 FBgn0033204 CG2065 2.683140 5.3511720
## FBgn0010389 FBgn0010389 htl -2.439321 6.1563975
## t P.Value adj.P.Val B
## FBgn0036368 -15.66537 7.179890e-11 3.446706e-07 15.327435
## FBgn0000212 14.50625 2.183284e-10 6.987236e-07 14.213804
## FBgn0060296 -14.17700 3.037193e-10 7.290023e-07 13.897642
## FBgn0031327 -13.48661 6.203950e-10 1.191282e-06 13.188992
## FBgn0003231 12.62374 1.584800e-09 2.535943e-06 12.225766
## FBgn0053192 16.53741 3.260076e-11 3.129999e-07 11.588659
## FBgn0028940 12.01686 3.166885e-09 4.201999e-06 11.562115
## FBgn0261451 -11.93088 3.501301e-09 4.201999e-06 11.411645
## FBgn0033204 10.67203 1.635001e-08 1.308137e-05 9.892830
## FBgn0010389 -10.49274 2.059880e-08 1.521301e-05 9.585343volcanoplot(fit.WT_dsBrm_100.vs.YN_dsBrm, highlight = 10, names = fit.WT_dsBrm_100.vs.YN_dsBrm$genes[,2], main = "WT_dsBrm_100 vs YN_dsBrm")
write.fit(fit.WT_dsBrm_100.vs.YN_dsBrm , results = results.fit.WT_dsBrm_100.vs.YN_dsBrm , file="WT_dsBrm_100_vs_YN_dsBrm.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast WT_dsBrm_100 vs YN_dsBrm_100
WT_dsBrm_100.vs.YN_dsBrm_100 <- makeContrasts(WT_dsBrm_100 - YN_dsBrm_100, levels = des)
fit.WT_dsBrm_100.vs.YN_dsBrm_100 <- contrasts.fit(fit, WT_dsBrm_100.vs.YN_dsBrm_100)
fit.WT_dsBrm_100.vs.YN_dsBrm_100 <- eBayes(fit.WT_dsBrm_100.vs.YN_dsBrm_100)
fit.WT_dsBrm_100.vs.YN_dsBrm_100$genes <- gene.names.limma # add common gene names to the object
results.fit.WT_dsBrm_100.vs.YN_dsBrm_100 <- decideTests(fit.WT_dsBrm_100.vs.YN_dsBrm_100)
table(results.fit.WT_dsBrm_100.vs.YN_dsBrm_100)## results.fit.WT_dsBrm_100.vs.YN_dsBrm_100
## -1 0 1
## 163 9266 172topTable(fit.WT_dsBrm_100.vs.YN_dsBrm_100, adjust = "BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr
## FBgn0060296 FBgn0060296 pain -1.766603 4.9777433
## FBgn0051361 FBgn0051361 dpr17 1.425285 5.6517414
## FBgn0261673 FBgn0261673 nemy -1.844868 6.4988781
## FBgn0000477 FBgn0000477 DNaseII 4.606912 -0.3213821
## FBgn0036368 FBgn0036368 CG10738 -1.604611 4.3042006
## FBgn0261089 FBgn0261089 Sytalpha 3.546999 4.1507887
## FBgn0052207 FBgn0052207 CR32207 -2.689789 3.2972615
## FBgn0053630 FBgn0053630 CG33630 6.392781 -1.5059336
## FBgn0000489 FBgn0000489 Pka-C3 -2.091969 3.5822465
## FBgn0259211 FBgn0259211 grh -2.497452 5.9546061
## t P.Value adj.P.Val B
## FBgn0060296 -12.492765 1.835827e-09 1.762578e-05 12.116751
## FBgn0051361 10.824667 1.346276e-08 3.437243e-05 10.097729
## FBgn0261673 -10.601434 1.790044e-08 3.437243e-05 9.788587
## FBgn0000477 10.753311 1.473893e-08 3.437243e-05 9.357924
## FBgn0036368 -9.811627 5.097162e-08 8.156308e-05 8.746673
## FBgn0261089 9.468553 8.190278e-08 9.699196e-05 8.363200
## FBgn0052207 -9.211293 1.178523e-07 9.699196e-05 7.954403
## FBgn0053630 11.110353 9.411764e-09 3.437243e-05 7.940772
## FBgn0000489 -9.060467 1.463744e-07 9.699196e-05 7.780909
## FBgn0259211 -9.106197 1.370280e-07 9.699196e-05 7.739132volcanoplot(fit.WT_dsBrm_100.vs.YN_dsBrm_100, highlight = 10, names = fit.WT_dsBrm_100.vs.YN_dsBrm_100$genes[,2], main = "WT_dsBrm_100 vs YN_dsBrm_100")
write.fit(fit.WT_dsBrm_100.vs.YN_dsBrm_100 , results = results.fit.WT_dsBrm_100.vs.YN_dsBrm_100, file = "WT_dsBrm_100_vs_YN_dsBrm_100.txt", digits=30, dec = ",", adjust = "BH") # save the results to file# contrast K804R_dsGFP vs K804R_dsBrm
K804R_dsGFP.vs.K804R_dsBrm <- makeContrasts(K804R_dsGFP - K804R_dsBrm, levels = des)
fit.K804R_dsGFP.vs.K804R_dsBrm <- contrasts.fit(fit, K804R_dsGFP.vs.K804R_dsBrm)
fit.K804R_dsGFP.vs.K804R_dsBrm <- eBayes(fit.K804R_dsGFP.vs.K804R_dsBrm)
fit.K804R_dsGFP.vs.K804R_dsBrm$genes <- gene.names.limma # add common gene names to the object
results.fit.K804R_dsGFP.vs.K804R_dsBrm <- decideTests(fit.K804R_dsGFP.vs.K804R_dsBrm)
table(results.fit.K804R_dsGFP.vs.K804R_dsBrm)## results.fit.K804R_dsGFP.vs.K804R_dsBrm
## -1 0 1
## 3 9594 4topTable(fit.K804R_dsGFP.vs.K804R_dsBrm, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr
## FBgn0000212 FBgn0000212 brm 2.2217569 10.532912
## FBgn0030332 FBgn0030332 CG9360 -1.7671135 4.831107
## FBgn0030596 FBgn0030596 CG12398 2.3674179 4.245533
## FBgn0041150 FBgn0041150 hoe1 1.2514010 5.244726
## FBgn0083959 FBgn0083959 trpm -0.8139573 7.850324
## FBgn0040091 FBgn0040091 Ugt58Fa 0.8855263 8.240297
## FBgn0082585 FBgn0082585 sprt -0.8343476 5.914105
## FBgn0028985 FBgn0028985 Spn4 -0.8425344 7.016699
## FBgn0034013 FBgn0034013 unc-5 0.9925991 6.850264
## FBgn0038346 FBgn0038346 CG14872 1.2401392 4.696065
## t P.Value adj.P.Val B
## FBgn0000212 9.174082 1.242964e-07 0.001193370 7.711521
## FBgn0030332 -7.641396 1.282341e-06 0.004103919 5.459826
## FBgn0030596 7.762032 1.056030e-06 0.004103919 4.989534
## FBgn0041150 6.474012 9.274737e-06 0.022261689 3.762172
## FBgn0083959 -6.344204 1.169012e-05 0.022447362 3.570129
## FBgn0040091 6.044520 2.012437e-05 0.032202339 3.055370
## FBgn0082585 -5.787775 3.236446e-05 0.044390166 2.604725
## FBgn0028985 -5.577895 4.804181e-05 0.052020430 2.224052
## FBgn0034013 5.553202 5.034659e-05 0.052020430 2.180373
## FBgn0038346 5.481608 5.769951e-05 0.052020430 2.055245volcanoplot(fit.K804R_dsGFP.vs.K804R_dsBrm, highlight = 10, names = fit.K804R_dsGFP.vs.K804R_dsBrm$genes[,2], main = "K804R_dsGFP vs K804R_dsBrm")
write.fit(fit.K804R_dsGFP.vs.K804R_dsBrm , results = results.fit.K804R_dsGFP.vs.K804R_dsBrm , file="K804R_dsGFP_vs_K804R_dsBrm.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast K804R_dsGFP vs K804R_dsBrm_100
K804R_dsGFP.vs.K804R_dsBrm_100 <- makeContrasts(K804R_dsGFP - K804R_dsBrm_100, levels = des)
fit.K804R_dsGFP.vs.K804R_dsBrm_100 <- contrasts.fit(fit, K804R_dsGFP.vs.K804R_dsBrm_100)
fit.K804R_dsGFP.vs.K804R_dsBrm_100 <- eBayes(fit.K804R_dsGFP.vs.K804R_dsBrm_100)
fit.K804R_dsGFP.vs.K804R_dsBrm_100$genes <- gene.names.limma # add common gene names to the object
results.fit.K804R_dsGFP.vs.K804R_dsBrm_100 <- decideTests(fit.K804R_dsGFP.vs.K804R_dsBrm_100)
table(results.fit.K804R_dsGFP.vs.K804R_dsBrm_100)## results.fit.K804R_dsGFP.vs.K804R_dsBrm_100
## -1 0 1
## 75 9490 36topTable(fit.K804R_dsGFP.vs.K804R_dsBrm_100, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr
## FBgn0042106 FBgn0042106 CG18754 -3.142179 2.8874345
## FBgn0053192 FBgn0053192 MtnD -9.222253 -0.6745924
## FBgn0002868 FBgn0002868 MtnA -4.943571 9.1243145
## FBgn0035763 FBgn0035763 CG8602 -1.166095 9.0885578
## FBgn0030596 FBgn0030596 CG12398 2.161065 4.2455327
## FBgn0034013 FBgn0034013 unc-5 1.313766 6.8502639
## FBgn0037750 FBgn0037750 Whamy -2.908729 4.1745421
## FBgn0262146 FBgn0262146 MtnE -8.616540 -1.2739640
## FBgn0032889 FBgn0032889 CG9331 -1.265408 5.2045777
## FBgn0259923 FBgn0259923 Sep4 -1.909672 3.5168907
## t P.Value adj.P.Val B
## FBgn0042106 -9.247642 1.118967e-07 5.371603e-04 7.861837
## FBgn0053192 -15.307597 1.004005e-10 9.639449e-07 7.851865
## FBgn0002868 -8.115303 6.044458e-07 1.450821e-03 6.416943
## FBgn0035763 -7.286951 2.293655e-06 3.255490e-03 5.058914
## FBgn0030596 7.236679 2.494178e-06 3.255490e-03 5.038217
## FBgn0034013 7.186521 2.712626e-06 3.255490e-03 4.865858
## FBgn0037750 -6.876150 4.594923e-06 4.901762e-03 4.467553
## FBgn0262146 -8.411395 3.832197e-07 1.226431e-03 4.178054
## FBgn0032889 -6.289831 1.288907e-05 1.215997e-02 3.360362
## FBgn0259923 -6.154458 1.646491e-05 1.215997e-02 3.238642volcanoplot(fit.K804R_dsGFP.vs.K804R_dsBrm_100, highlight = 10, names = fit.K804R_dsGFP.vs.K804R_dsBrm_100$genes[,2], main = "K804R_dsGFP vs K804R_dsBrm_100")
write.fit(fit.K804R_dsGFP.vs.K804R_dsBrm_100 , results = results.fit.K804R_dsGFP.vs.K804R_dsBrm_100 , file="K804R_dsGFP_vs_K804R_dsBrm_100.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast K804R_dsGFP vs YN_dsGFP
K804R_dsGFP.vs.YN_dsGFP <- makeContrasts(K804R_dsGFP - YN_dsGFP, levels = des)
fit.K804R_dsGFP.vs.YN_dsGFP <- contrasts.fit(fit, K804R_dsGFP.vs.YN_dsGFP)
fit.K804R_dsGFP.vs.YN_dsGFP <- eBayes(fit.K804R_dsGFP.vs.YN_dsGFP)
fit.K804R_dsGFP.vs.YN_dsGFP$genes <- gene.names.limma # add common gene names to the object
results.fit.K804R_dsGFP.vs.YN_dsGFP <- decideTests(fit.K804R_dsGFP.vs.YN_dsGFP)
table(results.fit.K804R_dsGFP.vs.YN_dsGFP)## results.fit.K804R_dsGFP.vs.YN_dsGFP
## -1 0 1
## 279 8948 374topTable(fit.K804R_dsGFP.vs.YN_dsGFP, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr t
## FBgn0036368 FBgn0036368 CG10738 -7.070287 4.304201 -18.73713
## FBgn0035888 FBgn0035888 CG7120 -3.495555 5.672332 -14.98335
## FBgn0031327 FBgn0031327 CG5397 -4.938399 3.658995 -14.36434
## FBgn0263041 FBgn0263041 CG43336 3.634389 3.702348 14.29528
## FBgn0259834 FBgn0259834 out 3.302933 4.022702 13.87061
## FBgn0034225 FBgn0034225 veil 3.635333 4.854702 12.64312
## FBgn0034797 FBgn0034797 nahoda 4.010398 4.424711 12.63917
## FBgn0024250 FBgn0024250 brk 2.205101 5.626609 12.44812
## FBgn0051999 FBgn0051999 CG31999 3.944864 6.847512 12.17436
## FBgn0010389 FBgn0010389 htl -2.970471 6.156398 -12.13262
## P.Value adj.P.Val B
## FBgn0036368 5.199402e-12 4.991945e-08 15.87539
## FBgn0035888 1.368775e-10 6.469905e-07 14.66170
## FBgn0031327 2.515058e-10 6.469905e-07 13.87294
## FBgn0263041 2.695513e-10 6.469905e-07 13.84943
## FBgn0259834 4.154267e-10 7.977024e-07 13.46511
## FBgn0034225 1.550863e-09 2.136517e-06 12.27256
## FBgn0034797 1.557715e-09 2.136517e-06 12.18802
## FBgn0024250 1.930742e-09 2.317131e-06 12.05871
## FBgn0051999 2.638862e-09 2.658549e-06 11.73258
## FBgn0010389 2.769033e-09 2.658549e-06 11.67566volcanoplot(fit.K804R_dsGFP.vs.YN_dsGFP, highlight = 10, names = fit.K804R_dsGFP.vs.YN_dsGFP$genes[,2], main = "K804R_dsGFP vs YN_dsGFP")
write.fit(fit.K804R_dsGFP.vs.YN_dsGFP , results = results.fit.K804R_dsGFP.vs.YN_dsGFP , file="K804R_dsGFP_vs_YN_dsGFP.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast K804R_dsGFP vs YN_dsBrm
K804R_dsGFP.vs.YN_dsBrm <- makeContrasts(K804R_dsGFP - YN_dsBrm, levels = des)
fit.K804R_dsGFP.vs.YN_dsBrm <- contrasts.fit(fit, K804R_dsGFP.vs.YN_dsBrm)
fit.K804R_dsGFP.vs.YN_dsBrm <- eBayes(fit.K804R_dsGFP.vs.YN_dsBrm)
fit.K804R_dsGFP.vs.YN_dsBrm$genes <- gene.names.limma # add common gene names to the object
results.fit.K804R_dsGFP.vs.YN_dsBrm <- decideTests(fit.K804R_dsGFP.vs.YN_dsBrm)
table(results.fit.K804R_dsGFP.vs.YN_dsBrm)## results.fit.K804R_dsGFP.vs.YN_dsBrm
## -1 0 1
## 329 8911 361topTable(fit.K804R_dsGFP.vs.YN_dsBrm, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr t
## FBgn0036368 FBgn0036368 CG10738 -7.286144 4.304201 -19.30496
## FBgn0031327 FBgn0031327 CG5397 -5.218800 3.658995 -15.16954
## FBgn0035888 FBgn0035888 CG7120 -3.426379 5.672332 -14.69849
## FBgn0033724 FBgn0033724 CG8501 -4.276723 7.129524 -14.20210
## FBgn0052626 FBgn0052626 CG32626 1.804427 8.488482 13.21846
## FBgn0259834 FBgn0259834 out 3.698088 4.022702 13.29931
## FBgn0010389 FBgn0010389 htl -3.160570 6.156398 -12.97988
## FBgn0263041 FBgn0263041 CG43336 3.389061 3.702348 13.06128
## FBgn0029002 FBgn0029002 miple2 -3.521393 7.804977 -12.54557
## FBgn0034860 FBgn0034860 CG9812 -3.132680 3.767806 -11.92842
## P.Value adj.P.Val B
## FBgn0036368 3.342810e-12 3.209432e-08 16.03101
## FBgn0031327 1.144815e-10 5.495684e-07 14.54815
## FBgn0035888 1.805970e-10 5.779706e-07 14.38477
## FBgn0033724 2.961004e-10 7.107150e-07 13.92197
## FBgn0052626 8.257074e-10 1.283293e-06 12.89174
## FBgn0259834 7.571237e-10 1.283293e-06 12.72581
## FBgn0010389 1.069299e-09 1.283293e-06 12.63928
## FBgn0263041 9.785812e-10 1.283293e-06 12.61331
## FBgn0029002 1.729906e-09 1.845425e-06 12.15350
## FBgn0034860 3.511424e-09 3.371319e-06 11.45019volcanoplot(fit.K804R_dsGFP.vs.YN_dsBrm, highlight = 10, names = fit.K804R_dsGFP.vs.YN_dsBrm$genes[,2], main = "K804R_dsGFP vs YN_dsBrm")
write.fit(fit.K804R_dsGFP.vs.YN_dsBrm , results = results.fit.K804R_dsGFP.vs.YN_dsBrm , file="K804R_dsGFP_vs_YN_dsBrm.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast K804R_dsGFP vs YN_dsBrm_100
K804R_dsGFP.vs.YN_dsBrm_100 <- makeContrasts(K804R_dsGFP - YN_dsBrm_100, levels = des)
fit.K804R_dsGFP.vs.YN_dsBrm_100 <- contrasts.fit(fit, K804R_dsGFP.vs.YN_dsBrm_100)
fit.K804R_dsGFP.vs.YN_dsBrm_100 <- eBayes(fit.K804R_dsGFP.vs.YN_dsBrm_100)
fit.K804R_dsGFP.vs.YN_dsBrm_100$genes <- gene.names.limma # add common gene names to the object
results.fit.K804R_dsGFP.vs.YN_dsBrm_100 <- decideTests(fit.K804R_dsGFP.vs.YN_dsBrm_100)
table(results.fit.K804R_dsGFP.vs.YN_dsBrm_100)## results.fit.K804R_dsGFP.vs.YN_dsBrm_100
## -1 0 1
## 1300 7219 1082topTable(fit.K804R_dsGFP.vs.YN_dsBrm_100, adjust = "BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr t
## FBgn0000212 FBgn0000212 brm -5.186920 10.532912 -17.44322
## FBgn0036368 FBgn0036368 CG10738 -6.350041 4.304201 -16.79870
## FBgn0035888 FBgn0035888 CG7120 -3.195407 5.672332 -13.71393
## FBgn0003231 FBgn0003231 ref(2)P -2.451239 8.521526 -13.66079
## FBgn0036984 FBgn0036984 CG13248 -4.089852 3.408383 -13.92777
## FBgn0035763 FBgn0035763 CG8602 -2.207447 9.088558 -13.44500
## FBgn0263041 FBgn0263041 CG43336 3.740748 3.702348 13.20948
## FBgn0031762 FBgn0031762 CG9098 -2.588790 4.839396 -12.61738
## FBgn0051999 FBgn0051999 CG31999 4.635122 6.847512 12.56631
## FBgn0028684 FBgn0028684 Tbp-1 -1.603412 7.484029 -12.38914
## P.Value adj.P.Val B
## FBgn0000212 1.492361e-11 1.244321e-07 16.67297
## FBgn0036368 2.592065e-11 1.244321e-07 14.38472
## FBgn0035888 4.887151e-10 8.266282e-07 13.40148
## FBgn0003231 5.165888e-10 8.266282e-07 13.37166
## FBgn0036984 3.916726e-10 8.266282e-07 13.21973
## FBgn0035763 6.483271e-10 8.892269e-07 13.14678
## FBgn0263041 8.337214e-10 1.000570e-06 12.59482
## FBgn0031762 1.596109e-09 1.622651e-06 12.24036
## FBgn0051999 1.690085e-09 1.622651e-06 12.19440
## FBgn0028684 2.064185e-09 1.801658e-06 11.97830volcanoplot(fit.K804R_dsGFP.vs.YN_dsBrm_100, highlight = 10, names = fit.K804R_dsGFP.vs.YN_dsBrm_100$genes[,2], main = "K804R_dsGFP vs YN_dsBrm_100")
write.fit(fit.K804R_dsGFP.vs.YN_dsBrm_100 , results = results.fit.K804R_dsGFP.vs.YN_dsBrm_100, file = "K804R_dsGFP_vs_YN_dsBrm_100.txt", digits=30, dec = ",", adjust = "BH") # save the results to file# contrast K804R_dsBrm vs K804R_dsBrm_100
K804R_dsBrm.vs.K804R_dsBrm_100 <- makeContrasts(K804R_dsBrm - K804R_dsBrm_100, levels = des)
fit.K804R_dsBrm.vs.K804R_dsBrm_100 <- contrasts.fit(fit, K804R_dsBrm.vs.K804R_dsBrm_100)
fit.K804R_dsBrm.vs.K804R_dsBrm_100 <- eBayes(fit.K804R_dsBrm.vs.K804R_dsBrm_100)
fit.K804R_dsBrm.vs.K804R_dsBrm_100$genes <- gene.names.limma # add common gene names to the object
results.fit.K804R_dsBrm.vs.K804R_dsBrm_100 <- decideTests(fit.K804R_dsBrm.vs.K804R_dsBrm_100)
table(results.fit.K804R_dsBrm.vs.K804R_dsBrm_100)## results.fit.K804R_dsBrm.vs.K804R_dsBrm_100
## -1 0
## 27 9574topTable(fit.K804R_dsBrm.vs.K804R_dsBrm_100, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr
## FBgn0000212 FBgn0000212 brm -3.525820 10.5329124
## FBgn0053192 FBgn0053192 MtnD -10.238050 -0.6745924
## FBgn0035763 FBgn0035763 CG8602 -1.457598 9.0885578
## FBgn0002868 FBgn0002868 MtnA -5.177142 9.1243145
## FBgn0042106 FBgn0042106 CG18754 -2.124170 2.8874345
## FBgn0262146 FBgn0262146 MtnE -7.256577 -1.2739640
## FBgn0031762 FBgn0031762 CG9098 -1.620793 4.8393957
## FBgn0259923 FBgn0259923 Sep4 -2.244002 3.5168907
## FBgn0033809 FBgn0033809 CG4630 -1.840155 4.3669282
## FBgn0062412 FBgn0062412 Ctr1B -2.686749 0.7790356
## t P.Value adj.P.Val B
## FBgn0000212 -13.830053 4.332033e-10 2.079593e-06 13.505922
## FBgn0053192 -16.205218 4.384242e-11 4.209311e-07 9.067107
## FBgn0035763 -9.126603 1.330624e-07 4.258439e-04 7.854478
## FBgn0002868 -8.497366 3.363955e-07 8.074333e-04 6.956625
## FBgn0042106 -7.794326 1.002859e-06 1.604741e-03 5.924522
## FBgn0262146 -8.307495 4.491231e-07 8.624062e-04 5.397206
## FBgn0031762 -7.230957 2.518138e-06 3.022080e-03 5.017271
## FBgn0259923 -7.118327 3.042269e-06 3.245425e-03 4.857936
## FBgn0033809 -6.759618 5.619271e-06 5.344351e-03 4.193798
## FBgn0062412 -6.710221 6.123098e-06 5.344351e-03 4.026462volcanoplot(fit.K804R_dsBrm.vs.K804R_dsBrm_100, highlight = 10, names = fit.K804R_dsBrm.vs.K804R_dsBrm_100$genes[,2], main = "K804R_dsBrm vs K804R_dsBrm_100")
write.fit(fit.K804R_dsBrm.vs.K804R_dsBrm_100 , results = results.fit.K804R_dsBrm.vs.K804R_dsBrm_100 , file="K804R_dsBrm_vs_K804R_dsBrm_100.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast K804R_dsBrm vs YN_dsGFP
K804R_dsBrm.vs.YN_dsGFP <- makeContrasts(K804R_dsBrm - YN_dsGFP, levels = des)
fit.K804R_dsBrm.vs.YN_dsGFP <- contrasts.fit(fit, K804R_dsBrm.vs.YN_dsGFP)
fit.K804R_dsBrm.vs.YN_dsGFP <- eBayes(fit.K804R_dsBrm.vs.YN_dsGFP)
fit.K804R_dsBrm.vs.YN_dsGFP$genes <- gene.names.limma # add common gene names to the object
results.fit.K804R_dsBrm.vs.YN_dsGFP <- decideTests(fit.K804R_dsBrm.vs.YN_dsGFP)
table(results.fit.K804R_dsBrm.vs.YN_dsGFP)## results.fit.K804R_dsBrm.vs.YN_dsGFP
## -1 0 1
## 441 8653 507topTable(fit.K804R_dsBrm.vs.YN_dsGFP, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr t
## FBgn0036368 FBgn0036368 CG10738 -6.982531 4.304201 -19.57601
## FBgn0035888 FBgn0035888 CG7120 -3.428298 5.672332 -15.36133
## FBgn0000212 FBgn0000212 brm -3.984310 10.532912 -15.27899
## FBgn0263041 FBgn0263041 CG43336 3.844766 3.702348 15.22648
## FBgn0031327 FBgn0031327 CG5397 -4.937974 3.658995 -15.03581
## FBgn0010389 FBgn0010389 htl -3.573555 6.156398 -14.06380
## FBgn0034225 FBgn0034225 veil 3.728051 4.854702 12.98935
## FBgn0034860 FBgn0034860 CG9812 -3.580323 3.767806 -12.55452
## FBgn0034139 FBgn0034139 CG4927 2.675084 3.472933 12.39876
## FBgn0031762 FBgn0031762 CG9098 -2.575514 4.839396 -12.04955
## P.Value adj.P.Val B
## FBgn0036368 2.718733e-12 2.610256e-08 16.53315
## FBgn0035888 9.542804e-11 2.498790e-07 15.02293
## FBgn0000212 1.031599e-10 2.498790e-07 14.96185
## FBgn0263041 1.084350e-10 2.498790e-07 14.68046
## FBgn0031327 1.301317e-10 2.498790e-07 14.51564
## FBgn0010389 3.407422e-10 5.452443e-07 13.78355
## FBgn0034225 1.058306e-09 1.451542e-06 12.64146
## FBgn0034860 1.712592e-09 2.055325e-06 12.10118
## FBgn0034139 2.041785e-09 2.178131e-06 11.99483
## FBgn0031762 3.048723e-09 2.313814e-06 11.61509volcanoplot(fit.K804R_dsBrm.vs.YN_dsGFP, highlight = 10, names = fit.K804R_dsBrm.vs.YN_dsGFP$genes[,2], main = "K804R_dsBrm vs YN_dsGFP")
write.fit(fit.K804R_dsBrm.vs.YN_dsGFP , results = results.fit.K804R_dsBrm.vs.YN_dsGFP , file="K804R_dsBrm_vs_YN_dsGFP.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast K804R_dsBrm vs YN_dsBrm
K804R_dsBrm.vs.YN_dsBrm <- makeContrasts(K804R_dsBrm - YN_dsBrm, levels = des)
fit.K804R_dsBrm.vs.YN_dsBrm <- contrasts.fit(fit, K804R_dsBrm.vs.YN_dsBrm)
fit.K804R_dsBrm.vs.YN_dsBrm <- eBayes(fit.K804R_dsBrm.vs.YN_dsBrm)
fit.K804R_dsBrm.vs.YN_dsBrm$genes <- gene.names.limma # add common gene names to the object
results.fit.K804R_dsBrm.vs.YN_dsBrm <- decideTests(fit.K804R_dsBrm.vs.YN_dsBrm)
table(results.fit.K804R_dsBrm.vs.YN_dsBrm)## results.fit.K804R_dsBrm.vs.YN_dsBrm
## -1 0 1
## 372 8872 357topTable(fit.K804R_dsBrm.vs.YN_dsBrm, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr t
## FBgn0036368 FBgn0036368 CG10738 -7.198388 4.304201 -20.17624
## FBgn0031327 FBgn0031327 CG5397 -5.218376 3.658995 -15.87767
## FBgn0035888 FBgn0035888 CG7120 -3.359122 5.672332 -15.06442
## FBgn0010389 FBgn0010389 htl -3.763653 6.156398 -14.88734
## FBgn0263041 FBgn0263041 CG43336 3.599437 3.702348 13.96337
## FBgn0034860 FBgn0034860 CG9812 -3.811850 3.767806 -13.30058
## FBgn0033724 FBgn0033724 CG8501 -3.679808 7.129524 -13.06906
## FBgn0052626 FBgn0052626 CG32626 1.706033 8.488482 12.44857
## FBgn0034139 FBgn0034139 CG4927 3.045149 3.472933 12.22155
## FBgn0029002 FBgn0029002 miple2 -3.315487 7.804977 -11.97627
## P.Value adj.P.Val B
## FBgn0036368 1.736485e-12 1.667199e-08 16.65410
## FBgn0031327 5.903313e-11 2.833885e-07 15.18028
## FBgn0035888 1.266022e-10 3.605264e-07 14.73811
## FBgn0010389 1.502037e-10 3.605264e-07 14.59058
## FBgn0263041 3.776065e-10 7.250800e-07 13.48174
## FBgn0034860 7.560926e-10 1.209874e-06 12.83119
## FBgn0033724 9.703479e-10 1.330902e-06 12.73843
## FBgn0052626 1.929759e-09 2.315952e-06 12.04240
## FBgn0034139 2.499480e-09 2.666390e-06 11.71631
## FBgn0029002 3.320347e-09 2.891609e-06 11.49665volcanoplot(fit.K804R_dsBrm.vs.YN_dsBrm, highlight = 10, names = fit.K804R_dsBrm.vs.YN_dsBrm$genes[,2], main = "K804R_dsBrm vs YN_dsBrm")
write.fit(fit.K804R_dsBrm.vs.YN_dsBrm , results = results.fit.K804R_dsBrm.vs.YN_dsBrm , file="K804R_dsBrm_vs_YN_dsBrm.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast K804R_dsBrm vs YN_dsBrm_100
K804R_dsBrm.vs.YN_dsBrm_100 <- makeContrasts(K804R_dsBrm - YN_dsBrm_100, levels = des)
fit.K804R_dsBrm.vs.YN_dsBrm_100 <- contrasts.fit(fit, K804R_dsBrm.vs.YN_dsBrm_100)
fit.K804R_dsBrm.vs.YN_dsBrm_100 <- eBayes(fit.K804R_dsBrm.vs.YN_dsBrm_100)
fit.K804R_dsBrm.vs.YN_dsBrm_100$genes <- gene.names.limma # add common gene names to the object
results.fit.K804R_dsBrm.vs.YN_dsBrm_100 <- decideTests(fit.K804R_dsBrm.vs.YN_dsBrm_100)
table(results.fit.K804R_dsBrm.vs.YN_dsBrm_100)## results.fit.K804R_dsBrm.vs.YN_dsBrm_100
## -1 0 1
## 1416 7090 1095topTable(fit.K804R_dsBrm.vs.YN_dsBrm_100, adjust = "BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr t
## FBgn0000212 FBgn0000212 brm -7.408677 10.532912 -25.88168
## FBgn0036368 FBgn0036368 CG10738 -6.262285 4.304201 -17.52210
## FBgn0035763 FBgn0035763 CG8602 -2.498950 9.088558 -15.24911
## FBgn0031762 FBgn0031762 CG9098 -3.075484 4.839396 -14.39832
## FBgn0036984 FBgn0036984 CG13248 -4.193873 3.408383 -14.67526
## FBgn0035888 FBgn0035888 CG7120 -3.128151 5.672332 -14.03560
## FBgn0263041 FBgn0263041 CG43336 3.951124 3.702348 14.02934
## FBgn0003231 FBgn0003231 ref(2)P -2.443782 8.521526 -13.55108
## FBgn0028684 FBgn0028684 Tbp-1 -1.744040 7.484029 -13.47491
## FBgn0031589 FBgn0031589 CG3714 -2.076039 7.154859 -12.82514
## P.Value adj.P.Val B
## FBgn0000212 4.175868e-14 4.009251e-10 22.00361
## FBgn0036368 1.396636e-11 6.704553e-08 15.33112
## FBgn0035763 1.061274e-10 2.547322e-07 14.93548
## FBgn0031762 2.431028e-10 3.890050e-07 14.05997
## FBgn0036984 1.847652e-10 3.547861e-07 14.02647
## FBgn0035888 3.506942e-10 4.235735e-07 13.75016
## FBgn0263041 3.529411e-10 4.235735e-07 13.45691
## FBgn0003231 5.796050e-10 6.030548e-07 13.25345
## FBgn0028684 6.281166e-10 6.030548e-07 13.16002
## FBgn0031589 1.267224e-09 1.078541e-06 12.45280volcanoplot(fit.K804R_dsBrm.vs.YN_dsBrm_100, highlight = 10, names = fit.K804R_dsBrm.vs.YN_dsBrm_100$genes[,2], main = "K804R_dsBrm vs YN_dsBrm_100")
write.fit(fit.K804R_dsBrm.vs.YN_dsBrm_100 , results = results.fit.K804R_dsBrm.vs.YN_dsBrm_100, file = "K804R_dsBrm_vs_YN_dsBrm_100.txt", digits=30, dec = ",", adjust = "BH") # save the results to file# contrast YN_dsGFP vs YN_dsBrm
YN_dsGFP.vs.YN_dsBrm <- makeContrasts(YN_dsGFP - YN_dsBrm, levels = des)
fit.YN_dsGFP.vs.YN_dsBrm <- contrasts.fit(fit, YN_dsGFP.vs.YN_dsBrm)
fit.YN_dsGFP.vs.YN_dsBrm <- eBayes(fit.YN_dsGFP.vs.YN_dsBrm)
fit.YN_dsGFP.vs.YN_dsBrm$genes <- gene.names.limma # add common gene names to the object
results.fit.YN_dsGFP.vs.YN_dsBrm <- decideTests(fit.YN_dsGFP.vs.YN_dsBrm)
table(results.fit.YN_dsGFP.vs.YN_dsBrm)## results.fit.YN_dsGFP.vs.YN_dsBrm
## -1 0
## 2 9599topTable(fit.YN_dsGFP.vs.YN_dsBrm, adjust="BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr
## FBgn0041604 FBgn0041604 dlp -1.5429573 5.1650211
## FBgn0051361 FBgn0051361 dpr17 -0.9584672 5.6517414
## FBgn0030596 FBgn0030596 CG12398 1.6162448 4.2455327
## FBgn0000212 FBgn0000212 brm 1.4964621 10.5329124
## FBgn0038365 FBgn0038365 CG9593 -1.5216080 1.2599325
## FBgn0039178 FBgn0039178 CG6356 -1.8207375 0.9037314
## FBgn0031530 FBgn0031530 pgant2 -1.1057732 5.0898606
## FBgn0082585 FBgn0082585 sprt -0.6918791 5.9141050
## FBgn0032666 FBgn0032666 CG5758 -1.4128035 7.5552611
## FBgn0083959 FBgn0083959 trpm -0.5519640 7.8503244
## t P.Value adj.P.Val B
## FBgn0041604 -8.425911 3.748559e-07 0.003598991 6.29681059
## FBgn0051361 -7.587428 1.399564e-06 0.006718605 5.32909144
## FBgn0030596 5.780052 3.283489e-05 0.078811937 2.12240299
## FBgn0000212 5.468639 5.914621e-05 0.113572543 1.96302638
## FBgn0038365 -5.367726 7.176936e-05 0.114842937 1.55601118
## FBgn0039178 -5.841351 2.928789e-05 0.078811937 1.11648751
## FBgn0031530 -4.765393 2.337655e-04 0.320626120 0.74808201
## FBgn0082585 -4.618062 3.140252e-04 0.376869491 0.48402820
## FBgn0032666 -4.479987 4.149189e-04 0.442372991 0.22588592
## FBgn0083959 -4.379204 5.090647e-04 0.442372991 0.02788826volcanoplot(fit.YN_dsGFP.vs.YN_dsBrm, highlight = 10, names = fit.YN_dsGFP.vs.YN_dsBrm$genes[,2], main = "YN_dsGFP vs YN_dsBrm")
write.fit(fit.YN_dsGFP.vs.YN_dsBrm , results = results.fit.YN_dsGFP.vs.YN_dsBrm , file="YN_dsGFP_vs_YN_dsBrm.txt", digits=30, dec=",", adjust = "BH") # save the results to file
# contrast YN_dsGFP vs YN_dsBrm_100
YN_dsGFP.vs.YN_dsBrm_100 <- makeContrasts(YN_dsGFP - YN_dsBrm_100, levels = des)
fit.YN_dsGFP.vs.YN_dsBrm_100 <- contrasts.fit(fit, YN_dsGFP.vs.YN_dsBrm_100)
fit.YN_dsGFP.vs.YN_dsBrm_100 <- eBayes(fit.YN_dsGFP.vs.YN_dsBrm_100)
fit.YN_dsGFP.vs.YN_dsBrm_100$genes <- gene.names.limma # add common gene names to the object
results.fit.YN_dsGFP.vs.YN_dsBrm_100 <- decideTests(fit.YN_dsGFP.vs.YN_dsBrm_100)
table(results.fit.YN_dsGFP.vs.YN_dsBrm_100)## results.fit.YN_dsGFP.vs.YN_dsBrm_100
## -1 0 1
## 869 8349 383topTable(fit.YN_dsGFP.vs.YN_dsBrm_100, adjust = "BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr
## FBgn0003231 FBgn0003231 ref(2)P -2.295818 8.5215263
## FBgn0053192 FBgn0053192 MtnD -8.419673 -0.6745924
## FBgn0000212 FBgn0000212 brm -3.424367 10.5329124
## FBgn0010041 FBgn0010041 GstD5 -3.655013 7.4410796
## FBgn0033205 FBgn0033205 CG2064 -2.068212 8.0155012
## FBgn0037750 FBgn0037750 Whamy -4.181621 4.1745421
## FBgn0053461 FBgn0053461 CG33461 -3.175643 3.4528936
## FBgn0035868 FBgn0035868 CG7194 -3.144751 5.3692984
## FBgn0031589 FBgn0031589 CG3714 -1.674241 7.1548585
## FBgn0035996 FBgn0035996 CG3448 -1.562237 4.2730452
## t P.Value adj.P.Val B
## FBgn0003231 -12.67618 1.494729e-09 7.175447e-06 12.317260
## FBgn0053192 -16.53692 3.261478e-11 3.131345e-07 12.114056
## FBgn0000212 -10.95135 1.147632e-08 2.246401e-05 10.309640
## FBgn0010041 -10.82004 1.354182e-08 2.246401e-05 10.097394
## FBgn0033205 -10.73672 1.505369e-08 2.246401e-05 9.986095
## FBgn0037750 -10.66780 1.643878e-08 2.246401e-05 9.954028
## FBgn0053461 -10.47576 2.105781e-08 2.246401e-05 9.692721
## FBgn0035868 -10.48265 2.087027e-08 2.246401e-05 9.679116
## FBgn0031589 -10.37490 2.401619e-08 2.305795e-05 9.495111
## FBgn0035996 -10.29682 2.660686e-08 2.322295e-05 9.458697volcanoplot(fit.YN_dsGFP.vs.YN_dsBrm_100, highlight = 10, names = fit.YN_dsGFP.vs.YN_dsBrm_100$genes[,2], main = "YN_dsGFP vs YN_dsBrm_100")
write.fit(fit.YN_dsGFP.vs.YN_dsBrm_100 , results = results.fit.YN_dsGFP.vs.YN_dsBrm_100, file = "YN_dsGFP_vs_YN_dsBrm_100.txt", digits=30, dec = ",", adjust = "BH") # save the results to file# contrast YN_dsBrm vs YN_dsBrm_100
YN_dsBrm.vs.YN_dsBrm_100 <- makeContrasts(YN_dsBrm - YN_dsBrm_100, levels = des)
fit.YN_dsBrm.vs.YN_dsBrm_100 <- contrasts.fit(fit, YN_dsBrm.vs.YN_dsBrm_100)
fit.YN_dsBrm.vs.YN_dsBrm_100 <- eBayes(fit.YN_dsBrm.vs.YN_dsBrm_100)
fit.YN_dsBrm.vs.YN_dsBrm_100$genes <- gene.names.limma # add common gene names to the object
results.fit.YN_dsBrm.vs.YN_dsBrm_100 <- decideTests(fit.YN_dsBrm.vs.YN_dsBrm_100)
table(results.fit.YN_dsBrm.vs.YN_dsBrm_100)## results.fit.YN_dsBrm.vs.YN_dsBrm_100
## -1 0 1
## 737 8505 359topTable(fit.YN_dsBrm.vs.YN_dsBrm_100, adjust = "BH") # show the 10 genes with lowest p-values ## ensembl_gene_id external_gene_id logFC AveExpr
## FBgn0000212 FBgn0000212 brm -4.920829 10.5329124
## FBgn0003231 FBgn0003231 ref(2)P -2.379783 8.5215263
## FBgn0053192 FBgn0053192 MtnD -9.965647 -0.6745924
## FBgn0035904 FBgn0035904 CG6776 -2.427504 7.2038795
## FBgn0010041 FBgn0010041 GstD5 -3.479240 7.4410796
## FBgn0040319 FBgn0040319 Gclc -3.314343 6.5996220
## FBgn0037750 FBgn0037750 Whamy -3.898737 4.1745421
## FBgn0033205 FBgn0033205 CG2064 -1.891464 8.0155012
## FBgn0053461 FBgn0053461 CG33461 -3.077257 3.4528936
## FBgn0015283 FBgn0015283 Pros54 -1.290976 6.7563536
## t P.Value adj.P.Val B
## FBgn0000212 -16.511361 3.336055e-11 3.134835e-07 15.865447
## FBgn0003231 -13.261368 7.885238e-10 2.523539e-06 12.952373
## FBgn0053192 -15.767902 6.530227e-11 3.134835e-07 9.765918
## FBgn0035904 -10.318584 2.585623e-08 4.651012e-05 9.453942
## FBgn0010041 -10.210498 2.981798e-08 4.651012e-05 9.322864
## FBgn0040319 -10.113734 3.391009e-08 4.651012e-05 9.194372
## FBgn0037750 -9.831985 4.957635e-08 5.315957e-05 8.870991
## FBgn0033205 -9.828211 4.983190e-08 5.315957e-05 8.795192
## FBgn0053461 -9.726872 5.724186e-08 5.454404e-05 8.704186
## FBgn0015283 -9.655394 6.316238e-08 5.454404e-05 8.538663volcanoplot(fit.YN_dsBrm.vs.YN_dsBrm_100, highlight = 10, names = fit.YN_dsBrm.vs.YN_dsBrm_100$genes[,2], main = "YN_dsBrm vs YN_dsBrm_100")
write.fit(fit.YN_dsBrm.vs.YN_dsBrm_100 , results = results.fit.YN_dsBrm.vs.YN_dsBrm_100, file = "YN_dsBrm_vs_YN_dsBrm_100.txt", digits=30, dec = ",", adjust = "BH") # save the results to file