Tohidul effect of Seasol on baseline gene expression - timecourse
Mark Ziemann
2020-08-25
Introduction
In this analysis I am performing differential expression and pathway analysis on seasol and control plants at all timepoints (0, 3, 6, 12 and 24 hrs).
Methods
The RNA-seq data is already deposited at SRA, so the raw data has been processed and incorporated into the DEE2 database, obtainable with the SRA project accession number SRP253869 (Ziemann et al, 2019). After obtaining processed data from DEE2 using the R/Bioconductor package, it will undergo differential expression analysis at each timepoint using DESeq2 (Love et al, 2014). Next, the differential expression profile underwent enrichment analysis of Mapman gene sets using the mitch software package (Kaspi & Ziemann 2020). Mitch allows to perform multi-dimensional enrichment analysis, so control-treatment enrichment analysis ca be performed at all timepoints at once.
suppressPackageStartupMessages({
library("getDEE2")
library("reshape2")
library("gplots")
library("DESeq2")
library("mitch")
library("edgeR")
library("RColorBrewer")
library("gplots")
})Set up the sample sheet
Using sample information from dee2.io.
md <- getDEE2Metadata("athaliana")
md <- md[which(md$SRP_accession=="SRP253869"),]
ctrl <- grep("H-",md$Library_name)
s80 <- grep("S80",md$Library_name)
md <- md[c(ctrl,s80),]
timepoint <- sapply(strsplit(md$Library_name, "-"),"[[",2)
timepoint <- sapply(strsplit(timepoint, "_"),"[[",1)
md$timepoint <- timepoint
md$trt <- as.numeric(grepl("S80",md$Library_name))
md## SRR_accession QC_summary SRX_accession SRS_accession SRP_accession
## 8000 SRR11404254 PASS SRX7983043 SRS6367595 SRP253869
## 8001 SRR11404255 PASS SRX7983042 SRS6367596 SRP253869
## 8002 SRR11404256 PASS SRX7983041 SRS6368266 SRP253869
## 8003 SRR11404257 PASS SRX7983040 SRS6368265 SRP253869
## 8014 SRR11404268 PASS SRX7983029 SRS6367587 SRP253869
## 8025 SRR11404279 PASS SRX7983018 SRS6367573 SRP253869
## 8035 SRR11404290 PASS SRX7983007 SRS6367565 SRP253869
## 8046 SRR11404301 PASS SRX7982996 SRS6367551 SRP253869
## 8052 SRR11404307 PASS SRX7982990 SRS6367545 SRP253869
## 8053 SRR11404308 PASS SRX7982989 SRS6367544 SRP253869
## 8054 SRR11404309 PASS SRX7982988 SRS6367543 SRP253869
## 8055 SRR11404310 PASS SRX7982987 SRS6367542 SRP253869
## 8056 SRR11404311 PASS SRX7982986 SRS6367541 SRP253869
## 8057 SRR11404312 PASS SRX7982985 SRS6367540 SRP253869
## 8058 SRR11404313 PASS SRX7982984 SRS6367539 SRP253869
## 8004 SRR11404258 PASS SRX7983039 SRS6368264 SRP253869
## 8005 SRR11404259 PASS SRX7983038 SRS6368262 SRP253869
## 8006 SRR11404260 WARN(5,7) SRX7983037 SRS6367591 SRP253869
## 8007 SRR11404261 PASS SRX7983036 SRS6368263 SRP253869
## 8008 SRR11404262 PASS SRX7983035 SRS6367590 SRP253869
## 8009 SRR11404263 PASS SRX7983034 SRS6367589 SRP253869
## 8010 SRR11404264 PASS SRX7983033 SRS6367593 SRP253869
## 8011 SRR11404265 PASS SRX7983032 SRS6367592 SRP253869
## 8012 SRR11404266 WARN(8) SRX7983031 SRS6367594 SRP253869
## 8013 SRR11404267 PASS SRX7983030 SRS6367588 SRP253869
## 8015 SRR11404269 PASS SRX7983028 SRS6367585 SRP253869
## 8016 SRR11404270 PASS SRX7983027 SRS6367582 SRP253869
## 8017 SRR11404271 PASS SRX7983026 SRS6367581 SRP253869
## 8018 SRR11404272 PASS SRX7983025 SRS6367580 SRP253869
## 8019 SRR11404273 PASS SRX7983024 SRS6367579 SRP253869
## Sample_name GEO_series Library_name timepoint trt
## 8000 H-12hpi_R1 NA H-12hpi_R1 12hpi 0
## 8001 H-6hpi_R3 NA H-6hpi_R3 6hpi 0
## 8002 H-6hpi_R2 NA H-6hpi_R2 6hpi 0
## 8003 H-6hpi_R1 NA H-6hpi_R1 6hpi 0
## 8014 H-3hpi_R3 NA H-3hpi_R3 3hpi 0
## 8025 H-3hpi_R2 NA H-3hpi_R2 3hpi 0
## 8035 H-3hpi_R1 NA H-3hpi_R1 3hpi 0
## 8046 H-0hpi_R3 NA H-0hpi_R3 0hpi 0
## 8052 H-24hpi_R3 NA H-24hpi_R3 24hpi 0
## 8053 H-24hpi_R2 NA H-24hpi_R2 24hpi 0
## 8054 H-24hpi_R1 NA H-24hpi_R1 24hpi 0
## 8055 H-12hpi_R3 NA H-12hpi_R3 12hpi 0
## 8056 H-12hpi_R2 NA H-12hpi_R2 12hpi 0
## 8057 H-0hpi_R2 NA H-0hpi_R2 0hpi 0
## 8058 H-0hpi_R1 NA H-0hpi_R1 0hpi 0
## 8004 S80-24hpi_R3 NA S80-24hpi_R3 24hpi 1
## 8005 S80-24hpi_R2 NA S80-24hpi_R2 24hpi 1
## 8006 S80-24hpi_R1 NA S80-24hpi_R1 24hpi 1
## 8007 S80-12hpi_R3 NA S80-12hpi_R3 12hpi 1
## 8008 S80-12hpi_R2 NA S80-12hpi_R2 12hpi 1
## 8009 S80-12hpi_R1 NA S80-12hpi_R1 12hpi 1
## 8010 S80-6hpi_R3 NA S80-6hpi_R3 6hpi 1
## 8011 S80-6hpi_R2 NA S80-6hpi_R2 6hpi 1
## 8012 S80-6hpi_R1 NA S80-6hpi_R1 6hpi 1
## 8013 S80-3hpi_R3 NA S80-3hpi_R3 3hpi 1
## 8015 S80-3hpi_R2 NA S80-3hpi_R2 3hpi 1
## 8016 S80-3hpi_R1 NA S80-3hpi_R1 3hpi 1
## 8017 S80-0hpi_R3 NA S80-0hpi_R3 0hpi 1
## 8018 S80-0hpi_R2 NA S80-0hpi_R2 0hpi 1
## 8019 S80-0hpi_R1 NA S80-0hpi_R1 0hpi 1Fetch dataset
Obtaining the RNA expression data from dee2.io.
# fetch the expresion data
x <- getDEE2(species="athaliana", SRRvec = md$SRR_accession , legacy = TRUE)## For more information about DEE2 QC metrics, visit
## https://github.com/markziemann/dee2/blob/master/qc/qc_metrics.md# collapse tx wise expression counts to genes
x <- Tx2Gene(x)Here, I’m running an MDS plot to see the overall variation in the expression data.
samplegroups <- factor(sapply(strsplit(md$Library_name,"_"),"[[",1))
colour_palette <- brewer.pal(n = length(levels(samplegroups)), name = "Paired")
colours <- colour_palette[as.integer(factor(samplegroups))]
plot(1,axes = FALSE,xlab="",ylab="",main="MDS by ART type")
legend("center",legend=levels(samplegroups),pch=16,cex=1.2,col=colour_palette)
mydist <- plotMDS(x$Tx2Gene, labels=colnames(x$Tx2Gene),col=colours,main="MDS plot")
# split into different objects for analysis
ss0 <- md[which(md$timepoint=="0hpi"),]
x0 <- x$Tx2Gene[,which(colnames(x$Tx2Gene) %in% ss0$SRR_accession)]
x0 <- x0[which(rowMeans(x0)>10),]
x0 <- round(x0)
ss3 <- md[which(md$timepoint=="3hpi"),]
x3 <- x$Tx2Gene[,which(colnames(x$Tx2Gene) %in% ss3$SRR_accession)]
x3 <- x3[which(rowMeans(x3)>10),]
x3 <- round(x3)
ss6 <- md[which(md$timepoint=="6hpi"),]
x6 <- x$Tx2Gene[,which(colnames(x$Tx2Gene) %in% ss6$SRR_accession)]
x6 <- x6[which(rowMeans(x6)>10),]
x6 <- round(x6)
ss12 <- md[which(md$timepoint=="12hpi"),]
x12 <- x$Tx2Gene[,which(colnames(x$Tx2Gene) %in% ss12$SRR_accession)]
x12 <- x12[which(rowMeans(x12)>10),]
x12 <- round(x12)
ss24 <- md[which(md$timepoint=="24hpi"),]
x24 <- x$Tx2Gene[,which(colnames(x$Tx2Gene) %in% ss24$SRR_accession)]
x24 <- x24[which(rowMeans(x24)>10),]
x24 <- round(x24)Differential expression
Now it is time to run differential expression analysis.
# t=0
dds <- DESeqDataSetFromMatrix(countData=x0, colData = ss0, design = ~ trt)## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functiondds <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingde <- DESeq2::results(dds)
de0 <- as.data.frame(de[order(de$pvalue),])
head(de0,20)## baseMean log2FoldChange lfcSE stat pvalue
## AT2G47450 3515.3630 -1.1462579 0.10558229 -10.856535 1.856607e-27
## AT2G22510 1188.1907 -1.5813409 0.16643308 -9.501362 2.071633e-21
## AT1G65970 420.4240 1.8116079 0.21189127 8.549705 1.234033e-17
## AT3G11510 6089.2093 0.8163242 0.09702351 8.413675 3.973605e-17
## AT2G32870 1201.6105 -0.8599037 0.10408554 -8.261510 1.438414e-16
## AT5G64100 3863.7325 0.9059243 0.11026448 8.215921 2.105416e-16
## AT3G22230 3382.0461 0.7492148 0.09701775 7.722452 1.141132e-14
## AT5G02870 7622.3840 0.6431751 0.08465480 7.597621 3.016242e-14
## AT4G09320 6532.5874 0.6684532 0.08855509 7.548445 4.404851e-14
## AT3G14440 643.4797 1.2614783 0.16872374 7.476590 7.627563e-14
## AT3G09735 1212.4507 0.7312255 0.09813692 7.451074 9.258328e-14
## AT2G44310 1536.7306 0.7109439 0.09618455 7.391457 1.452288e-13
## AT2G18328 687.4280 -1.0539247 0.14363968 -7.337281 2.179769e-13
## AT3G53430 5206.4546 0.6073020 0.08385435 7.242343 4.409993e-13
## AT4G38080 3013.3180 -1.4314495 0.19840915 -7.214634 5.407912e-13
## AT4G20150 3116.1585 0.6631730 0.09435802 7.028264 2.091192e-12
## AT5G48490 1035.1869 -1.2415864 0.17708729 -7.011155 2.363590e-12
## AT1G64370 5548.8727 -1.2789668 0.18261103 -7.003776 2.491535e-12
## AT5G54270 14902.9174 -1.2815541 0.18675104 -6.862367 6.772893e-12
## AT1G55330 2370.9362 0.6181038 0.09066543 6.817415 9.269333e-12
## padj
## AT2G47450 3.870841e-23
## AT2G22510 2.159574e-17
## AT1G65970 8.576116e-14
## AT3G11510 2.071142e-13
## AT2G32870 5.997899e-13
## AT5G64100 7.315968e-13
## AT3G22230 3.398780e-11
## AT5G02870 7.860705e-11
## AT4G09320 1.020408e-10
## AT3G14440 1.590271e-10
## AT3G09735 1.754790e-10
## AT2G44310 2.523229e-10
## AT2G18328 3.495846e-10
## AT3G53430 6.567425e-10
## AT4G38080 7.516637e-10
## AT4G20150 2.724954e-09
## AT5G48490 2.885890e-09
## AT1G64370 2.885890e-09
## AT5G54270 7.432003e-09
## AT1G55330 9.303737e-09write.table(de0,"h_vs_80_0hpi.tsv",sep="\t",quote=FALSE)
# t=3
dds <- DESeqDataSetFromMatrix(countData=x3, colData = ss3, design = ~ trt)## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functiondds <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingde <- DESeq2::results(dds)
de3 <- as.data.frame(de[order(de$pvalue),])
head(de3,20)## baseMean log2FoldChange lfcSE stat pvalue
## AT1G54410 11826.6026 -2.182179 0.1210430 -18.028134 1.171796e-72
## AT1G74250 1212.6322 -1.567921 0.1239941 -12.645129 1.190284e-36
## AT1G15340 2087.1364 -1.682312 0.1400228 -12.014559 2.979531e-33
## AT1G56660 601.4467 -3.815742 0.3592626 -10.621040 2.378958e-26
## AT2G44200 615.5771 -1.745086 0.1726302 -10.108811 5.049317e-24
## AT3G49601 1078.5561 -1.806036 0.1812096 -9.966555 2.135064e-23
## AT5G53800 545.4112 -1.857494 0.1870115 -9.932515 3.005766e-23
## AT1G47970 1070.5804 -1.414581 0.1434413 -9.861739 6.098371e-23
## AT1G15200 1338.6733 -1.256790 0.1277508 -9.837826 7.736478e-23
## AT5G63320 1249.8486 -1.265292 0.1298561 -9.743802 1.960777e-22
## AT1G20450 7681.6078 -1.537695 0.1590940 -9.665323 4.232820e-22
## AT2G05510 1444.3495 -3.554852 0.3712511 -9.575329 1.015335e-21
## AT3G51880 1254.4222 -1.192470 0.1247362 -9.559934 1.178322e-21
## AT5G55660 1957.5396 -1.871399 0.1966831 -9.514793 1.820743e-21
## AT2G36780 539.3108 -2.830050 0.3005426 -9.416468 4.665168e-21
## AT1G26255 112.7135 -4.099526 0.4369760 -9.381581 6.499062e-21
## AT2G43570 347.9389 -1.727088 0.1844085 -9.365555 7.565163e-21
## AT1G64330 635.1913 -1.495559 0.1601681 -9.337437 9.869430e-21
## AT2G38250 124.6096 -2.649332 0.2921541 -9.068269 1.209229e-19
## AT1G16210 1532.9289 -1.327241 0.1490090 -8.907120 5.237549e-19
## padj
## AT1G54410 2.452100e-68
## AT1G74250 1.245394e-32
## AT1G15340 2.078322e-29
## AT1G56660 1.244552e-22
## AT2G44200 2.113240e-20
## AT3G49601 7.446393e-20
## AT5G53800 8.985522e-20
## AT1G47970 1.595181e-19
## AT1G15200 1.798817e-19
## AT5G63320 4.103122e-19
## AT1G20450 8.052363e-19
## AT2G05510 1.770575e-18
## AT3G51880 1.896736e-18
## AT5G55660 2.721491e-18
## AT2G36780 6.508221e-18
## AT1G26255 8.499961e-18
## AT2G43570 9.312270e-18
## AT1G64330 1.147376e-17
## AT2G38250 1.331806e-16
## AT1G16210 5.480048e-16write.table(de3,"h_vs_80_3hpi.tsv",sep="\t",quote=FALSE)
# t=6
dds <- DESeqDataSetFromMatrix(countData=x6, colData = ss6, design = ~ trt)## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functiondds <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingde <- DESeq2::results(dds)
de6 <- as.data.frame(de[order(de$pvalue),])
head(de6,20)## baseMean log2FoldChange lfcSE stat pvalue
## AT1G72540 142.85512 -2.3101801 0.2530965 -9.127664 6.999298e-20
## AT1G19180 2214.85770 -1.4554270 0.1620743 -8.980001 2.707652e-19
## AT4G21840 479.36539 -1.9056667 0.2259958 -8.432310 3.389064e-17
## AT4G37710 93.21029 -3.1686164 0.3905549 -8.113115 4.933847e-16
## AT3G44860 304.16150 -1.9406993 0.2536584 -7.650837 1.996759e-14
## AT4G21830 3012.65933 -1.1209035 0.1481791 -7.564519 3.893029e-14
## AT3G53600 150.86840 -2.6294354 0.3481969 -7.551576 4.300241e-14
## AT1G80840 401.88292 -1.8017239 0.2455351 -7.337950 2.168909e-13
## AT5G07460 2961.02839 -0.8036362 0.1102874 -7.286743 3.175375e-13
## AT1G32970 231.64817 -2.0099810 0.2771139 -7.253266 4.068392e-13
## AT1G01680 291.19659 -1.3069886 0.1893211 -6.903554 5.071737e-12
## AT1G69930 464.94766 -2.2791447 0.3335972 -6.832025 8.372420e-12
## AT1G28190 467.42813 -1.2085472 0.1812617 -6.667416 2.603466e-11
## AT5G37840 90.97231 -2.5658963 0.3863179 -6.641930 3.096022e-11
## AT2G37970 684.14403 -0.9703772 0.1466028 -6.619090 3.614176e-11
## AT5G67340 815.10141 -1.2074458 0.1842801 -6.552230 5.668419e-11
## AT1G19020 609.53606 -1.4045477 0.2150684 -6.530702 6.546208e-11
## AT1G50010 9954.48893 0.6753116 0.1059865 6.371678 1.869711e-10
## AT1G07135 662.21627 -1.3918891 0.2202109 -6.320709 2.603653e-10
## AT4G22610 218.69455 -2.3237036 0.3719143 -6.247955 4.158613e-10
## padj
## AT1G72540 1.484761e-15
## AT1G19180 2.871872e-15
## AT4G21840 2.396407e-13
## AT4G37710 2.616542e-12
## AT3G44860 8.471450e-11
## AT4G21830 1.303157e-10
## AT3G53600 1.303157e-10
## AT1G80840 5.751132e-10
## AT5G07460 7.484359e-10
## AT1G32970 8.630280e-10
## AT1G01680 9.780613e-09
## AT1G69930 1.480035e-08
## AT1G28190 4.248256e-08
## AT5G37840 4.691136e-08
## AT2G37970 5.111168e-08
## AT5G67340 7.515261e-08
## AT1G19020 8.168513e-08
## AT1G50010 2.203455e-07
## AT1G07135 2.906910e-07
## AT4G22610 4.410833e-07write.table(de6,"h_vs_80_6hpi.tsv",sep="\t",quote=FALSE)
# t=12
dds <- DESeqDataSetFromMatrix(countData=x12, colData = ss12, design = ~ trt)## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functiondds <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingde <- DESeq2::results(dds)
de12 <- as.data.frame(de[order(de$pvalue),])
head(de12,20)## baseMean log2FoldChange lfcSE stat pvalue
## ATCG00520 373.4261 5.156364 0.5053982 10.202578 1.930781e-24
## ATCG00130 409.5522 4.153903 0.4224269 9.833423 8.082395e-23
## ATCG01070 131.1675 4.361646 0.4481186 9.733241 2.175492e-22
## ATCG00040 986.1424 4.089570 0.4309112 9.490517 2.298921e-21
## ATCG01090 272.4529 3.752220 0.4012476 9.351381 8.651036e-21
## ATCG00500 317.6603 4.962308 0.5433310 9.133122 6.655179e-20
## ATCG01050 252.1060 3.751759 0.4200319 8.932083 4.180632e-19
## ATCG00140 226.5290 4.024194 0.4601614 8.745179 2.226619e-18
## AT1G60640 293.4626 -1.703560 0.1993857 -8.544041 1.296080e-17
## ATCG01080 225.0500 3.881927 0.4667680 8.316608 9.051620e-17
## ATCG01060 133.9059 3.881125 0.4682476 8.288615 1.145747e-16
## ATCG01100 775.8392 3.075454 0.3746821 8.208169 2.245865e-16
## ATCG00540 607.1462 3.636501 0.4447703 8.176131 2.931024e-16
## ATCG01010 100.4831 4.328056 0.5346975 8.094402 5.754647e-16
## ATCG00650 358.4868 2.490750 0.3179340 7.834174 4.719343e-15
## ATCG00120 775.4356 3.884885 0.5080366 7.646861 2.059453e-14
## ATCG00280 1406.6448 1.826573 0.2395112 7.626253 2.416751e-14
## ATCG01110 685.9337 1.785154 0.2380655 7.498584 6.451115e-14
## ATCG00530 456.7897 3.476619 0.4693324 7.407584 1.286214e-13
## AT5G05210 599.9259 -1.265011 0.1759363 -7.190165 6.471285e-13
## padj
## ATCG00520 3.900758e-20
## ATCG00130 8.164432e-19
## ATCG01070 1.465049e-18
## ATCG00040 1.161127e-17
## ATCG01090 3.495537e-17
## ATCG00500 2.240910e-16
## ATCG01050 1.206590e-15
## ATCG00140 5.623048e-15
## AT1G60640 2.909411e-14
## ATCG01080 1.828699e-13
## ATCG01060 2.104320e-13
## ATCG01100 3.781101e-13
## ATCG00540 4.555037e-13
## ATCG01010 8.304367e-13
## ATCG00650 6.356326e-12
## ATCG00120 2.600445e-11
## ATCG00280 2.872095e-11
## ATCG01110 7.240660e-11
## ATCG00530 1.367651e-10
## AT5G05210 6.536968e-10write.table(de12,"h_vs_80_12hpi.tsv",sep="\t",quote=FALSE)
# t=24
dds <- DESeqDataSetFromMatrix(countData=x24, colData = ss24, design = ~ trt)## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functiondds <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingde <- DESeq2::results(dds)
de24 <- as.data.frame(de[order(de$pvalue),])
head(de24,20)## baseMean log2FoldChange lfcSE stat pvalue
## AT3G12900 108.78770 4.8830340 0.3781476 12.913037 3.800124e-38
## AT4G33070 536.08319 2.6331070 0.2654192 9.920561 3.388479e-23
## AT4G10250 69.77899 3.8454252 0.3918645 9.813151 9.883300e-23
## AT4G33560 86.40859 2.6102513 0.2850434 9.157381 5.317198e-20
## AT5G42600 144.54765 -3.4506751 0.4179198 -8.256787 1.496458e-16
## AT4G19690 763.61494 2.5395300 0.3176475 7.994805 1.297786e-15
## AT2G28400 465.56993 1.5526272 0.1951704 7.955238 1.787868e-15
## AT4G13580 240.02838 1.7043523 0.2324557 7.331946 2.268350e-13
## AT4G12550 2321.08448 -1.4047243 0.1952966 -7.192773 6.348856e-13
## AT3G50640 169.92943 1.6234357 0.2272605 7.143501 9.098301e-13
## AT2G29380 65.91844 2.7881817 0.3916457 7.119143 1.086001e-12
## AT5G19600 110.97431 -2.1696098 0.3078376 -7.047904 1.816325e-12
## AT4G12545 1560.47562 -1.2123427 0.1725200 -7.027260 2.106282e-12
## AT1G61340 238.87504 1.8640467 0.2692809 6.922314 4.443259e-12
## AT5G64310 1181.99883 0.9617894 0.1389806 6.920313 4.506483e-12
## AT5G46730 717.02497 -1.3426192 0.1950263 -6.884299 5.807272e-12
## AT2G37770 742.06957 2.7356917 0.4045853 6.761718 1.363651e-11
## AT1G73120 40.73863 3.4719629 0.5173804 6.710658 1.937494e-11
## AT1G35625 41.83925 -2.4204555 0.3663416 -6.607099 3.919233e-11
## AT3G24020 256.66263 1.2694019 0.1922026 6.604500 3.988612e-11
## padj
## AT3G12900 7.798994e-34
## AT4G33070 3.477087e-19
## AT4G10250 6.761166e-19
## AT4G33560 2.728121e-16
## AT5G42600 6.142360e-13
## AT4G19690 4.439077e-12
## AT2G28400 5.241774e-12
## AT4G13580 5.819168e-10
## AT4G12550 1.447751e-09
## AT3G50640 1.867244e-09
## AT2G29380 2.026181e-09
## AT5G19600 3.106370e-09
## AT4G12545 3.325172e-09
## AT1G61340 6.165769e-09
## AT5G64310 6.165769e-09
## AT5G46730 7.448915e-09
## AT2G37770 1.646248e-08
## AT1G73120 2.209066e-08
## AT1G35625 4.092914e-08
## AT3G24020 4.092914e-08write.table(de24,"h_vs_80_24hpi.tsv",sep="\t",quote=FALSE)Now run an MDS plot to visualise the gene expression differences at each timepoint.
maplot <- function(de,contrast_name) {
sig <-subset(de, padj < 0.05 )
up <-rownames(subset(de, padj < 0.05 & log2FoldChange > 0))
dn <-rownames(subset(de, padj < 0.05 & log2FoldChange < 0))
GENESUP <- length(up)
GENESDN <- length(dn)
SUBHEADER = paste(GENESUP, "up, ", GENESDN, "down")
ns <-subset(de, padj > 0.05 )
plot(log2(de$baseMean),de$log2FoldChange,
xlab="log2 basemean", ylab="log2 foldchange",
pch=19, cex=0.5, col="dark gray",
main=contrast_name, cex.main=0.7)
points(log2(sig$baseMean),sig$log2FoldChange,
pch=19, cex=0.5, col="red")
mtext(SUBHEADER,cex = 0.7)
}
maplot(de0, contrast_name = "0hpi")
maplot(de3, contrast_name = "3hpi")
maplot(de6, contrast_name = "6hpi")
maplot(de12, contrast_name = "12hpi")
maplot(de24, contrast_name = "24hpi")
Enrichment analysis
gsets <- gmt_import("Ath_AGI_LOCUS_TAIR10_Aug2012.txt.gmt")
xl <- list("de0"=de0,"de3"=de3,"de6"=de6,"de12"=de12,"de24"=de24)
y <- mitch_import(xl,DEtype = "DESeq2")## Note: Mean no. genes in input = 20870.6## Note: no. genes in output = 20269## Note: estimated proportion of input genes in output = 0.971# prioritisation by effect size
capture.output(
res <- mitch_calc(y,gsets,priority="effect")
, file = "/dev/null", append = FALSE,
type = c("output", "message"), split = FALSE)## Note: Enrichments with large effect sizes may not be
## statistically significant.n=50
head(res$enrichment_result,n)## set
## 74 RNA_REGULATION_OF_TRANSCRIPTION_TRIHELIX,_TRIPLE-HELIX_TRANSCRIPTION_FACTOR_FAMILY
## 69 RNA_REGULATION_OF_TRANSCRIPTION_G2-LIKE_TRANSCRIPTION_FACTOR_FAMILY,_GARP
## 34 NOT_ASSIGNED_NO_ONTOLOGY_GLYCINE_RICH_PROTEINS
## 96 TRANSPORT_METABOLITE_TRANSPORTERS_AT_THE_ENVELOPE_MEMBRANE
## 26 MITOCHONDRIAL_ELECTRON_TRANSPORT_/_ATP_SYNTHESIS_CYTOCHROME_C_OXIDASE
## 95 TRANSPORT_AMINO_ACIDS
## 99 TRANSPORT_SUGARS
## 63 RNA_PROCESSING_SPLICING
## 77 RNA_TRANSCRIPTION
## 42 PROTEIN_DEGRADATION_AAA_TYPE
## 53 PROTEIN_POSTRANSLATIONAL_MODIFICATION_KINASE_RECEPTOR_LIKE_CYTOPLASMATIC_KINASE_VI
## 32 NOT_ASSIGNED_NO_ONTOLOGY_DC1_DOMAIN_CONTAINING_PROTEIN
## 37 NOT_ASSIGNED_NO_ONTOLOGY_PROLINE_RICH_FAMILY
## 93 STRESS_BIOTIC_PR-PROTEINS_PLANT_DEFENSINS
## 44 PROTEIN_DEGRADATION_CYSTEINE_PROTEASE
## 83 SIGNALLING_RECEPTOR_KINASES_LEUCINE_RICH_REPEAT_I
## 55 PROTEIN_SYNTHESIS_ELONGATION
## 1 CELL_CYCLE
## 2 CELL_DIVISION
## 85 SIGNALLING_RECEPTOR_KINASES_LEUCINE_RICH_REPEAT_VIII_VIII-2
## 98 TRANSPORT_MISC
## 19 MISC_INVERTASE/PECTIN_METHYLESTERASE_INHIBITOR_FAMILY_PROTEIN
## 71 RNA_REGULATION_OF_TRANSCRIPTION_MYB_DOMAIN_TRANSCRIPTION_FACTOR_FAMILY
## 84 SIGNALLING_RECEPTOR_KINASES_LEUCINE_RICH_REPEAT_III
## 57 PROTEIN_SYNTHESIS_RIBOSOME_BIOGENESIS_PRE-RRNA_PROCESSING_AND_MODIFICATIONS_METHYLOTRANSFERASES
## 89 STRESS_ABIOTIC_HEAT
## 43 PROTEIN_DEGRADATION_ASPARTATE_PROTEASE
## 46 PROTEIN_DEGRADATION_UBIQUITIN
## 82 SIGNALLING_RECEPTOR_KINASES_CATHARANTHUS_ROSEUS-LIKE_RLK1
## 54 PROTEIN_POSTRANSLATIONAL_MODIFICATION_KINASE_RECEPTOR_LIKE_CYTOPLASMATIC_KINASE_VII
## 40 PROTEIN_AA_ACTIVATION_PSEUDOURIDYLATE_SYNTHASE
## 87 SIGNALLING_RECEPTOR_KINASES_S-LOCUS_GLYCOPROTEIN_LIKE
## 92 STRESS_BIOTIC_PR-PROTEINS
## 5 CELL_WALL_DEGRADATION_PECTATE_LYASES_AND_POLYGALACTURONASES
## 76 RNA_RNA_BINDING
## 36 NOT_ASSIGNED_NO_ONTOLOGY_PENTATRICOPEPTIDE_(PPR)_REPEAT-CONTAINING_PROTEIN
## 72 RNA_REGULATION_OF_TRANSCRIPTION_MYB-RELATED_TRANSCRIPTION_FACTOR_FAMILY
## 86 SIGNALLING_RECEPTOR_KINASES_MISC
## 64 RNA_REGULATION_OF_TRANSCRIPTION_B3_TRANSCRIPTION_FACTOR_FAMILY
## 38 NOT_ASSIGNED_NO_ONTOLOGY_TETRATRICOPEPTIDE_REPEAT_(TPR)
## 49 PROTEIN_DEGRADATION_UBIQUITIN_PROTEASOM
## 27 MITOCHONDRIAL_ELECTRON_TRANSPORT_/_ATP_SYNTHESIS_F1-ATPASE
## 8 DNA_SYNTHESIS/CHROMATIN_STRUCTURE
## 70 RNA_REGULATION_OF_TRANSCRIPTION_GENERAL_TRANSCRIPTION
## 20 MISC_MISC2
## 18 MISC_GDSL-MOTIF_LIPASE
## 60 REDOX_GLUTAREDOXINS
## 73 RNA_REGULATION_OF_TRANSCRIPTION_PUTATIVE_TRANSCRIPTION_REGULATOR
## 48 PROTEIN_DEGRADATION_UBIQUITIN_E3_SCF_FBOX
## 28 MITOCHONDRIAL_ELECTRON_TRANSPORT_/_ATP_SYNTHESIS_NADH-DH_LOCALISATION_NOT_CLEAR
## setSize pMANOVA s.de0 s.de3 s.de6 s.de12
## 74 18 1.191197e-06 -0.47352943 -0.7416917683 -0.38007451 -0.50358556
## 69 11 1.548926e-03 -0.70051786 -0.3981816387 -0.63430833 -0.31539504
## 34 35 1.913024e-13 -0.43893305 -0.7304056821 -0.36888123 -0.35522670
## 96 14 1.377235e-03 0.09918539 0.6054307578 0.47777268 0.47662306
## 26 11 1.007759e-02 0.42553783 0.3144616268 0.59825523 0.39541730
## 95 13 1.381460e-03 0.05231498 0.6091110706 0.28877294 0.56266709
## 99 22 1.651872e-04 0.18427870 0.5618610164 0.38184782 0.49549877
## 63 19 3.956646e-03 -0.26745159 -0.4248473034 -0.25881741 -0.37886420
## 77 15 3.081537e-02 -0.22977519 -0.3875382640 -0.41957803 -0.19963793
## 42 24 4.515506e-06 0.07902774 -0.4675763563 -0.46362065 -0.27597349
## 53 11 2.750362e-03 -0.32616520 0.3625772983 0.02739210 0.49703372
## 32 84 4.088214e-24 -0.16894913 0.2412210859 0.17430907 0.56641070
## 37 34 2.876719e-13 -0.60638091 0.0996962165 -0.22614573 0.06511141
## 93 10 3.577062e-02 0.44158152 0.3431758725 0.36555605 0.06193790
## 44 12 2.440611e-02 -0.16420661 -0.3442431423 -0.55537181 -0.12885258
## 83 14 3.679214e-04 -0.21636986 0.0826039426 0.09485489 0.60895017
## 55 15 1.164400e-03 0.54439288 0.0999967085 0.31595405 -0.06425068
## 1 15 7.064806e-02 -0.30066160 -0.4465488299 -0.28213686 -0.18236398
## 2 27 3.558055e-03 -0.35722206 -0.3615584758 -0.22988140 -0.21182214
## 85 10 3.217023e-02 -0.23207463 -0.3358309887 -0.25825559 0.16391727
## 98 32 5.057855e-03 0.22689875 0.3377847507 0.22234644 0.27866964
## 19 12 5.013396e-05 -0.18898817 0.4156505570 0.29305919 -0.24773329
## 71 10 1.889639e-01 -0.25408954 -0.4501505504 -0.21859914 -0.16813268
## 84 31 4.529513e-08 -0.32027263 0.2387141404 0.27098177 0.29731677
## 57 13 6.595289e-03 0.21895127 -0.0751458257 0.14695741 -0.14717007
## 89 76 5.961908e-07 -0.14976399 -0.3422642552 -0.20733214 -0.35867562
## 43 21 2.640814e-02 0.14873191 0.3963707174 0.32814999 0.08579801
## 46 22 5.722468e-02 -0.20008800 -0.3224181360 -0.28683486 -0.08787834
## 82 10 5.575015e-02 -0.29128782 -0.0023298287 -0.06988499 0.42576633
## 54 10 4.666946e-02 -0.24912385 -0.1821511427 -0.31915692 0.27954983
## 40 15 1.051726e-02 0.24794444 0.0589513183 0.22138837 0.05673283
## 87 16 3.092912e-03 -0.14094208 -0.0343776231 -0.22960796 0.32823532
## 92 105 9.708187e-23 -0.25220808 -0.1580459282 -0.27002201 0.33858456
## 5 53 5.627380e-05 0.15105913 0.3359061756 0.32790952 0.07710500
## 76 89 2.650472e-11 -0.21689179 -0.2118662376 -0.03522678 -0.22265899
## 36 206 4.170258e-47 0.12367886 -0.0108086711 0.16617896 0.36390564
## 72 16 5.863127e-02 -0.37469140 -0.1368130647 -0.28717351 -0.09701649
## 86 37 1.712248e-05 -0.16592126 0.1617507187 0.13640687 0.39045718
## 64 16 3.601604e-04 -0.15831605 -0.3624031008 0.15385992 0.02277440
## 38 11 1.913586e-01 0.08991285 -0.2069664958 -0.07971710 -0.23454707
## 49 11 1.828408e-01 0.40768630 0.0322745672 0.21764690 -0.13258959
## 27 12 2.721941e-01 0.39948331 0.1945747149 0.16500469 0.03116618
## 8 59 7.163944e-04 -0.22822063 -0.2218904889 -0.23578359 -0.05646139
## 70 14 1.511665e-01 0.08494552 -0.2541524139 -0.10741616 -0.26682653
## 20 18 1.777221e-02 0.20800070 0.0254034094 -0.05350627 -0.20431913
## 18 43 9.366571e-08 -0.16962050 0.3526361418 -0.07752858 0.19855401
## 60 16 9.843833e-03 0.09910877 -0.1123537254 0.21398064 0.11850096
## 73 75 2.380393e-05 -0.07028358 -0.3028992110 -0.14352910 -0.16656433
## 48 178 5.547639e-12 0.16721781 0.1733405141 0.16964050 0.31870383
## 28 19 7.898736e-02 0.22796621 -0.0008109162 0.22188434 -0.01480442
## s.de24 p.de0 p.de3 p.de6 p.de12
## 74 -0.285116236 5.042743e-04 5.050075e-08 5.241224e-03 2.161645e-04
## 69 -0.222017789 5.730745e-05 2.220954e-02 2.692623e-04 7.009807e-02
## 34 -0.455417332 6.967360e-06 7.282744e-14 1.586812e-04 2.756012e-04
## 96 0.233134676 5.205250e-01 8.753049e-05 1.965302e-03 2.015246e-03
## 26 0.296663944 1.452967e-02 7.093153e-02 5.902174e-04 2.315380e-02
## 95 0.248746810 7.439837e-01 1.428843e-04 7.142449e-02 4.430008e-04
## 99 0.192477449 1.345954e-01 5.042951e-06 1.931037e-03 5.726138e-05
## 63 -0.392296296 4.356569e-02 1.344856e-03 5.080964e-02 4.248007e-03
## 77 -0.357460255 1.233756e-01 9.355576e-03 4.897879e-03 1.806803e-01
## 42 -0.116082160 5.027699e-01 7.318331e-05 8.419676e-05 1.926621e-02
## 53 0.186646802 6.104996e-02 3.731821e-02 8.750059e-01 4.309414e-03
## 32 0.259868832 7.439724e-03 1.324941e-04 5.755053e-03 2.677906e-19
## 37 0.263288711 9.272207e-10 3.144213e-01 2.248643e-02 5.111782e-01
## 93 -0.212280962 1.560307e-02 6.022348e-02 4.531545e-02 7.345014e-01
## 44 -0.121011667 3.246737e-01 3.893922e-02 8.638088e-04 4.396144e-01
## 83 0.111535071 1.610048e-01 5.925678e-01 5.388965e-01 7.960064e-05
## 55 -0.185833251 2.613006e-04 5.025312e-01 3.411697e-02 6.665981e-01
## 1 -0.190204404 4.378642e-02 2.748243e-03 5.850457e-02 2.213942e-01
## 2 -0.277947941 1.313254e-03 1.145630e-03 3.868371e-02 5.676748e-02
## 85 -0.412498149 2.038146e-01 6.592525e-02 1.573258e-01 3.694287e-01
## 98 0.286393734 2.632474e-02 9.422357e-04 2.949445e-02 6.365787e-03
## 19 -0.051545968 2.569870e-01 1.265967e-02 7.878271e-02 1.373033e-01
## 71 -0.004787995 1.641315e-01 1.370130e-02 2.313187e-01 3.572456e-01
## 84 0.130313782 2.025914e-03 2.142993e-02 9.019531e-03 4.168261e-03
## 57 -0.488265585 1.716695e-01 6.389929e-01 3.589292e-01 3.582342e-01
## 89 -0.081367436 2.399897e-02 2.472736e-07 1.778319e-03 6.396894e-08
## 43 0.117232037 2.380564e-01 1.662831e-03 9.232648e-03 4.961185e-01
## 46 -0.263837067 1.042493e-01 8.845321e-03 1.985882e-02 4.755365e-01
## 82 -0.167155338 1.107106e-01 9.898216e-01 7.019730e-01 1.972936e-02
## 54 0.126659756 1.725304e-01 3.185765e-01 8.052964e-02 1.258362e-01
## 40 -0.411935091 9.639244e-02 6.926307e-01 1.376717e-01 7.036398e-01
## 87 -0.319421814 3.290441e-01 8.118294e-01 1.118136e-01 2.301485e-02
## 92 -0.071666619 7.989577e-06 5.144246e-03 1.743915e-06 2.022580e-09
## 5 0.176719729 5.713734e-02 2.329160e-05 3.627846e-05 3.315799e-01
## 76 -0.370901215 4.053449e-04 5.510905e-04 5.657566e-01 2.827108e-04
## 36 -0.321188257 2.219970e-03 7.892273e-01 3.944612e-05 2.080979e-19
## 72 0.143336790 9.459948e-03 3.434113e-01 4.672490e-02 5.016811e-01
## 86 0.190239172 8.073267e-02 8.865244e-02 1.510675e-01 3.946863e-05
## 64 -0.246358564 2.729206e-01 1.207820e-02 2.866492e-01 8.746824e-01
## 38 -0.358726968 6.056172e-01 2.346141e-01 6.471043e-01 1.779958e-01
## 49 0.003078470 1.921374e-02 8.529611e-01 2.113383e-01 4.464056e-01
## 27 0.080416646 1.656550e-02 2.431901e-01 3.223276e-01 8.517153e-01
## 8 -0.265606052 2.429291e-03 3.199452e-03 1.733163e-03 4.532337e-01
## 70 -0.273167119 5.821220e-01 9.966488e-02 4.865184e-01 8.388190e-02
## 20 0.371756676 1.265727e-01 8.519885e-01 6.943261e-01 1.334293e-01
## 18 0.144538805 5.429906e-02 6.295032e-05 3.790896e-01 2.427499e-02
## 60 0.349738310 4.925011e-01 4.365337e-01 1.383762e-01 4.118566e-01
## 73 -0.241888350 2.926844e-01 5.734593e-06 3.163919e-02 1.263568e-02
## 48 0.048550444 1.192789e-04 6.648168e-05 9.486289e-05 2.188337e-13
## 28 0.293016244 8.538574e-02 9.951177e-01 9.406000e-02 9.110521e-01
## p.de24 s.dist SD p.adjustMANOVA
## 74 3.624025e-02 1.1196799 0.17102215 9.071425e-06
## 69 2.023107e-01 1.0956217 0.20580627 5.111457e-03
## 34 3.107682e-06 1.0935351 0.15197581 3.787788e-12
## 96 1.309636e-01 0.9413672 0.20623317 4.716017e-03
## 26 8.843751e-02 0.9392775 0.12019715 2.433369e-02
## 95 1.204529e-01 0.9141183 0.23181684 4.716017e-03
## 99 1.181006e-01 0.8820549 0.17210166 7.787394e-04
## 63 3.071191e-03 0.7851411 0.07615613 1.119165e-02
## 77 1.652763e-02 0.7393655 0.09810809 5.981807e-02
## 42 3.249398e-01 0.7276356 0.23443253 3.193108e-05
## 53 2.837819e-01 0.7214402 0.31965967 8.508933e-03
## 32 3.832708e-05 0.7109627 0.26232709 1.349110e-22
## 37 7.885769e-03 0.7087591 0.34264657 4.746587e-12
## 93 2.450846e-01 0.7037717 0.27171055 6.681682e-02
## 44 4.679522e-01 0.6965284 0.18708135 5.140862e-02
## 83 4.699624e-01 0.6677553 0.29706417 1.456969e-03
## 55 2.127303e-01 0.6669723 0.29323979 4.269465e-03
## 1 2.021661e-01 0.6624491 0.10696412 1.076024e-01
## 2 1.242272e-02 0.6582562 0.06979243 1.036022e-02
## 85 2.389640e-02 0.6560188 0.22324119 6.124718e-02
## 98 5.047962e-03 0.6121255 0.04760880 1.353318e-02
## 19 7.571956e-01 0.5986605 0.29524372 2.757368e-04
## 71 9.790844e-01 0.5858960 0.16057410 2.492698e-01
## 84 2.092298e-01 0.5816950 0.25603612 4.484218e-07
## 57 2.300627e-03 0.5790038 0.27905442 1.718246e-02
## 89 2.200998e-01 0.5637637 0.12060422 4.918574e-06
## 43 3.523862e-01 0.5549937 0.13814948 5.446678e-02
## 46 3.217353e-02 0.5509950 0.09217525 9.602107e-02
## 82 3.600475e-01 0.5467685 0.27237628 9.515975e-02
## 54 4.879743e-01 0.5397163 0.25864835 8.400502e-02
## 40 5.737610e-03 0.5356059 0.26499159 2.479069e-02
## 87 2.695681e-02 0.5324801 0.25107456 9.278736e-03
## 92 2.045822e-01 0.5303536 0.24854675 2.402776e-21
## 5 2.605209e-02 0.5294823 0.11393833 2.932161e-04
## 76 1.451946e-09 0.5294478 0.11899026 3.279960e-10
## 36 1.870570e-15 0.5278427 0.25392479 4.128556e-45
## 72 3.208926e-01 0.5210928 0.19895308 9.674159e-02
## 86 4.523750e-02 0.5108304 0.19955773 1.130084e-04
## 64 8.798864e-02 0.4912067 0.20709864 1.456969e-03
## 38 3.938410e-02 0.4908886 0.17039475 2.492698e-01
## 49 9.858953e-01 0.4818811 0.21001917 2.446114e-01
## 27 6.295725e-01 0.4817789 0.14186809 3.326817e-01
## 8 4.173911e-04 0.4802587 0.08284152 2.727809e-03
## 70 7.678163e-02 0.4787109 0.15475103 2.107814e-01
## 20 6.319768e-03 0.4761532 0.22505194 3.909886e-02
## 18 1.010351e-01 0.4684541 0.21166272 8.429914e-07
## 60 1.542994e-02 0.4523196 0.16962273 2.433369e-02
## 73 2.923693e-04 0.4511563 0.08993896 1.472868e-04
## 48 2.640216e-01 0.4367103 0.09582682 7.845946e-11
## 28 2.702310e-02 0.4327581 0.14274161 1.167127e-01z <- res$enrichment_result[1:n,4:8]
rownames(z) <- head(res$enrichment_result$set,n)
colnames(z) <- gsub("s.","",colnames(z))
heatmap.2(as.matrix(z),margins = c(5,28),cexRow = 0.5, trace="none", main="multidimensional enrichment analysis")
capture.output(
mitch_plots(res,"timecourse_mitch_eff_plots.pdf")
, file = "/dev/null", append = FALSE,
type = c("output", "message"), split = FALSE)
unlink("timecourse_mitch_eff_report.html")
capture.output(
mitch_report(res,"timecourse_mitch_eff_report.html")
, file = "/dev/null", append = FALSE,
type = c("output", "message"), split = FALSE)## Dataset saved as " /tmp/Rtmpx51yyy/timecourse_mitch_eff_report.RData ".##
##
## processing file: mitch.Rmd## output file: /mnt/mziemann/projects/tohidul_rnaseq/mitch.knit.md##
## Output created: /tmp/Rtmpx51yyy/mitch_report.html
References
Ziemann M, Kaspi A, El-Osta A. Digital expression explorer 2: a repository of uniformly processed RNA sequencing data. Gigascience. 2019;8(4):giz022. doi:10.1093/gigascience/giz022
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. doi:10.1186/s13059-014-0550-8
Kaspi A, Ziemann M. mitch: multi-contrast pathway enrichment for multi-omics and single-cell profiling data. BMC Genomics. 2020;21(1):447. Published 2020 Jun 29. doi:10.1186/s12864-020-06856-9
Session information
For reproducibility.
sessionInfo()## R version 4.0.2 (2020-06-22)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.4 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
##
## locale:
## [1] LC_CTYPE=en_AU.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_AU.UTF-8 LC_COLLATE=en_AU.UTF-8
## [5] LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8
## [7] LC_PAPER=en_AU.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] pkgload_1.1.0 GGally_2.0.0
## [3] ggplot2_3.3.2 beeswarm_0.2.3
## [5] gtools_3.8.2 tibble_3.0.3
## [7] dplyr_1.0.1 echarts4r_0.3.2
## [9] RColorBrewer_1.1-2 edgeR_3.30.3
## [11] limma_3.44.3 mitch_1.0.8
## [13] DESeq2_1.28.1 SummarizedExperiment_1.18.2
## [15] DelayedArray_0.14.1 matrixStats_0.56.0
## [17] Biobase_2.48.0 GenomicRanges_1.40.0
## [19] GenomeInfoDb_1.24.2 IRanges_2.22.2
## [21] S4Vectors_0.26.1 BiocGenerics_0.34.0
## [23] gplots_3.0.4 reshape2_1.4.4
## [25] getDEE2_0.99.30
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-6 bit64_4.0.2 rprojroot_1.3-2
## [4] backports_1.1.8 tools_4.0.2 R6_2.4.1
## [7] KernSmooth_2.23-17 DBI_1.1.0 colorspace_1.4-1
## [10] withr_2.2.0 htm2txt_2.1.1 tidyselect_1.1.0
## [13] gridExtra_2.3 bit_4.0.4 compiler_4.0.2
## [16] desc_1.2.0 labeling_0.3 caTools_1.18.0
## [19] scales_1.1.1 genefilter_1.70.0 stringr_1.4.0
## [22] digest_0.6.25 rmarkdown_2.3 XVector_0.28.0
## [25] pkgconfig_2.0.3 htmltools_0.5.0 highr_0.8
## [28] fastmap_1.0.1 htmlwidgets_1.5.1 rlang_0.4.7
## [31] RSQLite_2.2.0 shiny_1.5.0 generics_0.0.2
## [34] farver_2.0.3 jsonlite_1.7.0 BiocParallel_1.22.0
## [37] RCurl_1.98-1.2 magrittr_1.5 GenomeInfoDbData_1.2.3
## [40] Matrix_1.2-18 Rcpp_1.0.5 munsell_0.5.0
## [43] lifecycle_0.2.0 stringi_1.4.6 yaml_2.2.1
## [46] MASS_7.3-51.6 zlibbioc_1.34.0 plyr_1.8.6
## [49] grid_4.0.2 blob_1.2.1 gdata_2.18.0
## [52] promises_1.1.1 crayon_1.3.4 lattice_0.20-41
## [55] splines_4.0.2 annotate_1.66.0 locfit_1.5-9.4
## [58] knitr_1.29 pillar_1.4.6 geneplotter_1.66.0
## [61] XML_3.99-0.5 glue_1.4.1 evaluate_0.14
## [64] vctrs_0.3.2 httpuv_1.5.4 testthat_2.3.2
## [67] gtable_0.3.0 purrr_0.3.4 assertthat_0.2.1
## [70] reshape_0.8.8 xfun_0.16 mime_0.9
## [73] xtable_1.8-4 later_1.1.0.1 survival_3.2-3
## [76] pbmcapply_1.5.0 AnnotationDbi_1.50.3 memoise_1.1.0
## [79] ellipsis_0.3.1