Differential abundance analysis of Seasol metagenomic data
Mark Ziemann
2021-08-12
Source: TBA
Intro
Here we are determining whether there is any differentially abundant microbes in these soil samples with and without treatment of Seasol.
library("DESeq2")
library("mitch")
library("gplots")Load and clean data
x <- readLines("absoluteabundance.txt")
xx <- strsplit(x,"\t")
df <- as.data.frame(do.call(rbind, xx))
colnames(df) <- df[1,]
df <- df[2:nrow(df),]
x <- df[,grep("kraken",colnames(df))]
x <- apply(x,2,as.numeric)
x <- as.data.frame(x)
rownames(x) <- df$`#OTU ID`
colnames(x) <- gsub("_kraken_bracken","",colnames(x))
# Phytophthora cinnamomi low detection level
x[grep("innamomi",otu$`Consensus Lineage`),]## SR6 SR9 UR8 SR2 UR7 UR3 SR5 UR4
## 4785 0 0 0 0 0 1 0 1nrow(x)## [1] 39558x <- x[which(rowMeans(x)>10),]
nrow(x)## [1] 8225MDS
cols <- as.numeric(grepl("S",colnames(x)))
cols <- gsub("0","lightblue",cols)
cols <- gsub("1","pink",cols)
plot(cmdscale(dist(t(x))),
xlab="Coordinate 1", ylab="Coordinate 2", col=cols,
pch=19,cex=4, main="MDS plot")
text(cmdscale(dist(t(x))), labels=colnames(x) ) 
DESeq2
ss <- as.data.frame(colnames(x))
ss$s <- factor(as.numeric(grepl("S",colnames(x))))
y <- DESeqDataSetFromMatrix(countData = round(x), colData = ss, design = ~ s)## converting counts to integer modey <- DESeq(y)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingres <- results(y)
res <-as.data.frame(res[order(res$pvalue),])
head(res,10)## baseMean log2FoldChange lfcSE stat pvalue
## 10724 8651.05632 -30.000000 3.3833776 -8.866879 7.522452e-19
## 60487 898.29960 26.784487 3.3834125 7.916412 2.444627e-15
## 60924 379.00723 -26.273746 3.3834769 -7.765310 8.144582e-15
## 134676 305.64862 -25.071050 3.3835018 -7.409794 1.264961e-13
## 38006 20.90037 -5.862915 1.1597810 -5.055191 4.299597e-07
## 549 2343.07248 1.408029 0.3382526 4.162656 3.145671e-05
## 1841 16.21648 7.439888 1.8678801 3.983065 6.803218e-05
## 566 156527.09567 2.859798 0.7283017 3.926666 8.613133e-05
## 2596949 39442.90905 3.300644 0.8622950 3.827744 1.293234e-04
## 341523 262.40396 11.457075 3.1975638 3.583064 3.395877e-04
## padj
## 10724 5.987872e-15
## 60487 9.729614e-12
## 60924 2.161029e-11
## 134676 2.517272e-10
## 38006 6.844959e-04
## 549 4.173257e-02
## 1841 7.736231e-02
## 566 8.570067e-02
## 2596949 1.143793e-01
## 341523 2.436404e-01Charts
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)
DET=nrow(de)
SUBHEADER = paste(GENESUP, "up, ", GENESDN, "down", DET, "detected")
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)
}
make_volcano <- function(de,name) {
sig <- subset(de,padj<0.05)
N_SIG=nrow(sig)
N_UP=nrow(subset(sig,log2FoldChange>0))
N_DN=nrow(subset(sig,log2FoldChange<0))
DET=nrow(de)
HEADER=paste(N_SIG,"@5%FDR,", N_UP, "up", N_DN, "dn", DET, "detected")
plot(de$log2FoldChange,-log10(de$padj),cex=0.5,pch=19,col="darkgray",
main=name, xlab="log2 FC", ylab="-log10 pval")
mtext(HEADER)
grid()
points(sig$log2FoldChange,-log10(sig$padj),cex=0.5,pch=19,col="red")
}
maplot(res,"ctrl vs seasol")
make_volcano(res,"ctrl vs seasol")
Identify OTUs
otu <- df[,c(1,ncol(df))]
rownames(otu) <- otu[,1]
otu[,1] = NULL
resm <- merge(res,otu,by=0)
resm <-as.data.frame(resm[order(resm$pvalue),])
head(resm,10)## Row.names baseMean log2FoldChange lfcSE stat pvalue
## 185 10724 8651.05632 -30.000000 3.3833776 -8.866879 7.522452e-19
## 6915 60487 898.29960 26.784487 3.3834125 7.916412 2.444627e-15
## 6930 60924 379.00723 -26.273746 3.3834769 -7.765310 8.144582e-15
## 893 134676 305.64862 -25.071050 3.3835018 -7.409794 1.264961e-13
## 5691 38006 20.90037 -5.862915 1.1597810 -5.055191 4.299597e-07
## 6628 549 2343.07248 1.408029 0.3382526 4.162656 3.145671e-05
## 2342 1841 16.21648 7.439888 1.8678801 3.983065 6.803218e-05
## 6720 566 156527.09567 2.859798 0.7283017 3.926666 8.613133e-05
## 4445 2596949 39442.90905 3.300644 0.8622950 3.827744 1.293234e-04
## 5410 341523 262.40396 11.457075 3.1975638 3.583064 3.395877e-04
## padj
## 185 5.987872e-15
## 6915 9.729614e-12
## 6930 2.161029e-11
## 893 2.517272e-10
## 5691 6.844959e-04
## 6628 4.173257e-02
## 2342 7.736231e-02
## 6720 8.570067e-02
## 4445 1.143793e-01
## 5410 2.436404e-01
## Consensus Lineage
## 185 k__Viruses; p__; c__; o__Caudovirales; f__Siphoviridae; g__; s__Bacillus phage SPP1
## 6915 k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__Enterobacterales; f__Enterobacteriaceae; g__Citrobacter; s__sp. JYME-1
## 6930 k__Bacteria; p__Proteobacteria; c__Betaproteobacteria; o__Burkholderiales; f__Burkholderiaceae; g__Burkholderia; s__sp. JS150
## 893 k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Micromonosporales; f__Micromonosporaceae; g__Actinoplanes; s__sp. SE50/110
## 5691 k__Eukaryota; p__Ascomycota; c__Sordariomycetes; o__Hypocreales; f__Cordycipitaceae; g__Beauveria; s__caledonica
## 6628 k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__Enterobacterales; f__Erwiniaceae; g__Pantoea; s__agglomerans
## 2342 k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Propionibacteriales; f__Nocardioidaceae; g__Nocardioides; s__albus
## 6720 k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__Enterobacterales; f__Enterobacteriaceae; g__Pseudescherichia; s__vulneris
## 4445 k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria; o__Enterobacterales; f__Enterobacteriaceae; g__Enterobacter; s__sp. E76
## 5410 k__Bacteria; p__Proteobacteria; c__Betaproteobacteria; o__Burkholderiales; f__Burkholderiaceae; g__Burkholderia; s__sp. mpa8.6# library size normalised
x <- x+1
xx <- x/colSums(x)*1000000
xx <- xx[,order(colnames(xx)),]
top_otus <- head(resm,10)$Row.names
par(mfrow=c(5,2))
bars <- sapply(top_otus,function(o) {
vals <- as.numeric(xx[which(rownames(xx) == o),])
names(vals) <- colnames(xx)
taxon <- otu[which(rownames(otu) == o),]
barplot(vals,ylab="log2 CPM")
mtext(taxon,cex=0.4)
grid()
})
Phythophthora
Phytophthora was below the detection threshold.
phytophthora <- resm[grep("Phytophthora",resm$`Consensus Lineage`),]
phytophthora## Row.names baseMean log2FoldChange lfcSE stat pvalue
## 6289 4787 259.54139 0.91165363 0.4838824 1.8840396 0.05955962
## 6302 4792 192.87331 0.35327279 0.2614817 1.3510423 0.17668189
## 6284 4784 29.26561 -0.54168044 0.5381361 -1.0065864 0.31413360
## 7214 67593 253.95474 -0.06978585 0.1884438 -0.3703271 0.71113881
## 6300 4790 22.79400 -0.17181051 1.5796907 -0.1087621 0.91339117
## padj
## 6289 0.9906052
## 6302 0.9994601
## 6284 0.9994601
## 7214 0.9994601
## 6300 0.9994601
## Consensus Lineage
## 6289 k__Eukaryota; p__Perkinsozoa; c__Oomycetes; o__Peronosporales; f__Peronosporaceae; g__Phytophthora; s__infestans
## 6302 k__Eukaryota; p__Perkinsozoa; c__Oomycetes; o__Peronosporales; f__Peronosporaceae; g__Phytophthora; s__parasitica
## 6284 k__Eukaryota; p__Perkinsozoa; c__Oomycetes; o__Peronosporales; f__Peronosporaceae; g__Phytophthora; s__capsici
## 7214 k__Eukaryota; p__Perkinsozoa; c__Oomycetes; o__Peronosporales; f__Peronosporaceae; g__Phytophthora; s__sojae
## 6300 k__Eukaryota; p__Perkinsozoa; c__Oomycetes; o__Peronosporales; f__Peronosporaceae; g__Phytophthora; s__nicotianaexxm <- merge(xx,otu,by=0)
xxm[grep("Phytophthora",xxm$`Consensus Lineage`),]## Row.names SR2 SR5 SR6 SR9 UR3 UR4
## 6284 4784 0.8838866 0.6996980 0.3130937 0.33026104 0.35292896 1.046463
## 6289 4787 2.8874195 12.3970762 6.9755371 5.89745495 4.41053365 5.073736
## 6300 4790 1.1663761 0.9698925 0.1981566 0.02450296 0.09995686 0.382670
## 6302 4792 4.9979504 5.2486925 8.0715167 3.98166393 4.82738069 3.114079
## 7214 67593 7.7184339 3.5708106 6.6361065 4.71379924 7.49396656 6.996980
## UR7 UR8
## 6284 0.78730388 1.298657
## 6289 4.75575897 3.471194
## 6300 0.02076053 1.541136
## 6302 3.87146842 2.626508
## 7214 4.60074309 6.385047
## Consensus Lineage
## 6284 k__Eukaryota; p__Perkinsozoa; c__Oomycetes; o__Peronosporales; f__Peronosporaceae; g__Phytophthora; s__capsici
## 6289 k__Eukaryota; p__Perkinsozoa; c__Oomycetes; o__Peronosporales; f__Peronosporaceae; g__Phytophthora; s__infestans
## 6300 k__Eukaryota; p__Perkinsozoa; c__Oomycetes; o__Peronosporales; f__Peronosporaceae; g__Phytophthora; s__nicotianae
## 6302 k__Eukaryota; p__Perkinsozoa; c__Oomycetes; o__Peronosporales; f__Peronosporaceae; g__Phytophthora; s__parasitica
## 7214 k__Eukaryota; p__Perkinsozoa; c__Oomycetes; o__Peronosporales; f__Peronosporaceae; g__Phytophthora; s__sojaephytophthora <- xxm[grep("Phytophthora",xxm$`Consensus Lineage`),]
rownames(phytophthora) <- sapply(strsplit(phytophthora$`Consensus Lineage`,";"),"[[",7)
phytophthora[,1]=NULL
phytophthora[,ncol(phytophthora)]=NULL
phytophthora## SR2 SR5 SR6 SR9 UR3 UR4
## s__capsici 0.8838866 0.6996980 0.3130937 0.33026104 0.35292896 1.046463
## s__infestans 2.8874195 12.3970762 6.9755371 5.89745495 4.41053365 5.073736
## s__nicotianae 1.1663761 0.9698925 0.1981566 0.02450296 0.09995686 0.382670
## s__parasitica 4.9979504 5.2486925 8.0715167 3.98166393 4.82738069 3.114079
## s__sojae 7.7184339 3.5708106 6.6361065 4.71379924 7.49396656 6.996980
## UR7 UR8
## s__capsici 0.78730388 1.298657
## s__infestans 4.75575897 3.471194
## s__nicotianae 0.02076053 1.541136
## s__parasitica 3.87146842 2.626508
## s__sojae 4.60074309 6.385047heatmap.2(as.matrix(phytophthora),trace="none",scale="none",mar=c(7,10),main="relative abundance")
Taxonomic dataframe
Figure out the main groups of microbes for enrichment analysis
tax <- strsplit(otu$`Consensus Lineage`,";")
taxdf <- as.data.frame(do.call(rbind, tax))
colnames(taxdf) <- c("kingdom","phylum","class","order","family","genus","species")
rownames(taxdf) <- rownames(otu)
taxdf <- taxdf[which(rownames(taxdf) %in% rownames(res)),]
apply(taxdf,2,function(y){ length(unique(y))})## kingdom phylum class order family genus species
## 4 98 210 564 1150 2665 7071Enrichment analysis using tanonomic levels
Making OTU sets
phylums <- unique(taxdf$phylum)
phylum_sets <- lapply(phylums, function(group){
rownames(taxdf[which(taxdf$phylum==group),])
})
names(phylum_sets) <- phylums
phylum_sets <- phylum_sets[lapply(phylum_sets,length)>=5]
classes <- unique(taxdf$class)
class_sets <- lapply(classes, function(group){
rownames(taxdf[which(taxdf$class==group),])
})
names(class_sets) <- classes
class_sets <- class_sets[lapply(class_sets,length)>=5]
orders <- unique(taxdf$order)
order_sets <- lapply(orders, function(group){
rownames(taxdf[which(taxdf$order==group),])
})
names(order_sets) <- orders
order_sets <- order_sets[lapply(order_sets,length)>=5]
familys <- unique(taxdf$family)
family_sets <- lapply(familys, function(group){
rownames(taxdf[which(taxdf$family==group),])
})
names(family_sets) <- familys
family_sets <- family_sets[lapply(family_sets,length)>=5]
genuss <- unique(taxdf$genus)
genus_sets <- lapply(genuss, function(group){
rownames(taxdf[which(taxdf$genus==group),])
})
names(genus_sets) <- genuss
genus_sets <- genus_sets[lapply(genus_sets,length)>=5]Enrichment with mitch
m <- mitch_import(res,DEtype="DESeq2")## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 8225## Note: no. genes in output = 8225## Note: estimated proportion of input genes in output = 1mphylum <- mitch_calc(m,genesets=phylum_sets)## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.head(mphylum$enrichment_result,10)## set setSize pANOVA s.dist p.adjustANOVA
## 1 p__Proteobacteria 2752 2.403214e-05 0.05696317 0.0009612857
## 33 p__Euglenozoa 21 2.763744e-04 0.45862645 0.0055274887
## 39 p__ 257 1.441923e-03 0.11656304 0.0192256456
## 10 p__Planctomycetes 88 2.722993e-03 -0.18544918 0.0272299315
## 2 p__Firmicutes 994 6.013410e-03 -0.05364729 0.0481072786
## 7 p__Chloroflexi 22 1.135941e-02 -0.31200337 0.0757293875
## 25 p__Ascomycota 287 1.393044e-02 -0.08530308 0.0796025007
## 27 p__Mucoromycota 11 3.819556e-02 -0.36106869 0.1909777894
## 20 p__Chordata 379 5.254386e-02 -0.05886804 0.2335282712
## 40 p__Negarnaviricota 131 7.346716e-02 -0.09102209 0.2938686408mclass <- mitch_calc(m,genesets=class_sets)## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.head(mclass$enrichment_result,10)## set setSize pANOVA s.dist p.adjustANOVA
## 1 c__Gammaproteobacteria 1401 7.608219e-07 0.08369357 5.097506e-05
## 24 c__Planctomycetia 80 5.716711e-04 -0.22339779 1.915098e-02
## 56 c__Kinetoplastea 18 1.667571e-03 0.42822523 3.724242e-02
## 42 c__Sordariomycetes 65 2.463116e-03 -0.21766214 4.125720e-02
## 7 c__Bacilli 689 1.178145e-02 -0.05787257 1.396827e-01
## 31 c__Actinopteri 103 1.250890e-02 -0.14296780 1.396827e-01
## 50 c__Magnoliopsida 224 4.652611e-02 -0.07785857 4.143527e-01
## 14 c__ 592 4.996749e-02 0.04828629 4.143527e-01
## 4 c__Deltaproteobacteria 99 5.565931e-02 -0.11172269 4.143527e-01
## 3 c__Alphaproteobacteria 686 6.283653e-02 0.04283537 4.210047e-01morder <- mitch_calc(m,genesets=order_sets)## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.head(morder$enrichment_result,20)## set setSize pANOVA s.dist p.adjustANOVA
## 3 o__Aeromonadales 66 5.084183e-15 -0.55737668 6.304387e-13
## 1 o__Pseudomonadales 361 2.426505e-12 0.21747618 1.504433e-10
## 41 o__Propionibacteriales 57 9.422856e-08 0.40929945 3.832315e-06
## 2 o__Enterobacterales 413 1.236231e-07 0.15404376 3.832315e-06
## 36 o__Streptomycetales 293 2.138967e-05 0.14591003 4.516969e-04
## 15 o__Neisseriales 51 2.185630e-05 0.34412316 4.516969e-04
## 29 o__Bacillales 458 6.970218e-05 -0.11038078 1.234724e-03
## 121 o__Caudovirales 257 1.441923e-03 0.11656304 2.234981e-02
## 107 o__Trypanosomatida 18 1.667571e-03 0.42822523 2.297543e-02
## 65 o__Pirellulales 60 2.095818e-03 -0.23010410 2.598814e-02
## 90 o__Onygenales 12 2.650564e-03 -0.50123787 2.987908e-02
## 39 o__Micromonosporales 45 4.900259e-03 -0.24279272 5.063601e-02
## 22 o__Caulobacterales 30 7.159837e-03 0.28397804 6.829383e-02
## 87 o__Sordariales 11 8.898782e-03 -0.45565221 7.881778e-02
## 105 o__Haemosporida 22 1.105427e-02 -0.31317811 9.138196e-02
## 19 o__Sphingomonadales 124 1.251798e-02 0.13045582 9.701433e-02
## 20 o__Rhodospirillales 76 2.319350e-02 -0.15104856 1.691761e-01
## 40 o__Pseudonocardiales 51 2.682360e-02 -0.17955545 1.781961e-01
## 99 o__Malvales 10 2.730425e-02 -0.40321363 1.781961e-01
## 18 o__Rhizobiales 281 4.374201e-02 0.07067354 2.712005e-01mfamily <- mitch_calc(m,genesets=family_sets)## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.head(mfamily$enrichment_result,20)## set setSize pANOVA s.dist p.adjustANOVA
## 1 f__Pseudomonadaceae 276 2.967188e-40 0.4670700 4.747502e-38
## 2 f__Moraxellaceae 85 6.873432e-21 -0.5887007 5.498746e-19
## 26 f__Comamonadaceae 80 4.917801e-19 -0.5769153 2.622827e-17
## 60 f__Paenibacillaceae 105 1.172444e-17 -0.4841567 4.689776e-16
## 9 f__Aeromonadaceae 66 5.084183e-15 -0.5573767 1.626939e-13
## 91 f__Nocardioidaceae 34 7.112413e-08 0.5342377 1.896644e-06
## 3 f__Enterobacteriaceae 243 1.386532e-06 0.1813752 3.169215e-05
## 5 f__Erwiniaceae 41 2.701951e-06 0.4239265 5.403901e-05
## 79 f__Streptomycetaceae 293 2.138967e-05 0.1459100 3.599676e-04
## 63 f__Listeriaceae 21 2.249798e-05 0.5344780 3.599676e-04
## 28 f__Oxalobacteraceae 61 7.547560e-05 0.2936041 1.097827e-03
## 30 f__Chromobacteriaceae 21 1.309269e-04 0.4823431 1.745692e-03
## 8 f__Morganellaceae 31 1.449727e-04 -0.3946239 1.784280e-03
## 156 f__Herelleviridae 63 5.899641e-04 0.2508567 6.742446e-03
## 141 f__Trypanosomatidae 18 1.667571e-03 0.4282252 1.778743e-02
## 157 f__Myoviridae 75 3.122313e-03 0.1979108 3.065922e-02
## 117 f__Thermoguttaceae 53 3.257543e-03 -0.2340712 3.065922e-02
## 80 f__Microbacteriaceae 102 4.156352e-03 -0.1648502 3.454551e-02
## 29 f__Alcaligenaceae 45 4.198600e-03 -0.2470470 3.454551e-02
## 159 f__Mimiviridae 21 4.318189e-03 -0.3599870 3.454551e-02mgenus <- mitch_calc(m,genesets=genus_sets)## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.head(mgenus$enrichment_result,20)## set setSize pANOVA s.dist p.adjustANOVA
## 1 g__Pseudomonas 269 1.282640e-40 0.4751066 1.487863e-38
## 2 g__Acinetobacter 65 1.916634e-24 -0.7313725 1.111648e-22
## 6 g__Enterobacter 49 2.475011e-15 0.6536154 9.570041e-14
## 63 g__Paenibacillus 90 9.780442e-15 -0.4729768 2.836328e-13
## 13 g__Aeromonas 60 1.900233e-13 -0.5494468 4.408540e-12
## 26 g__Comamonas 18 2.127207e-10 -0.8643976 4.112600e-09
## 84 g__Nocardioides 20 1.832572e-08 0.7267520 3.036834e-07
## 61 g__Lysinibacillus 18 7.472746e-08 -0.7323152 1.083548e-06
## 111 g__Roufvirus 13 2.267815e-07 0.8289183 2.922961e-06
## 28 g__Acidovorax 11 3.099459e-07 -0.8908294 3.595373e-06
## 116 g__Bequatrovirus 10 2.371372e-06 0.8616190 2.500719e-05
## 45 g__Sphingobium 21 4.767080e-06 0.5768266 4.608178e-05
## 39 g__Rhizobium 30 3.719292e-05 0.4353712 3.318753e-04
## 5 g__Klebsiella 32 6.394363e-05 -0.4086797 5.298186e-04
## 72 g__Streptomyces 282 1.036403e-04 0.1357697 8.014846e-04
## 92 g__Chryseobacterium 36 1.172654e-03 -0.3129266 8.501743e-03
## 10 g__Pantoea 20 1.821550e-03 0.4029494 1.242940e-02
## 101 g__Thermogutta 53 3.257543e-03 -0.2340712 2.003394e-02
## 4 g__Citrobacter 43 3.281420e-03 -0.2595090 2.003394e-02
## 66 g__Exiguobacterium 10 3.973007e-03 0.5261351 2.304344e-02Reports
mitch_report(res=mphylum,outfile="mitch_phylum.html",overwrite=TRUE)## Note: overwriting existing report## Dataset saved as " /tmp/RtmpbuOks4/mitch_phylum.rds ".##
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## Output created: /tmp/RtmpbuOks4/mitch_report.html## [1] TRUEmitch_report(res=mclass,outfile="mitch_class.html",overwrite=TRUE)## Note: overwriting existing report## Dataset saved as " /tmp/RtmpbuOks4/mitch_class.rds ".##
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## Output created: /tmp/RtmpbuOks4/mitch_report.html## [1] TRUEmitch_report(res=morder,outfile="mitch_order.html",overwrite=TRUE)## Note: overwriting existing report## Dataset saved as " /tmp/RtmpbuOks4/mitch_order.rds ".##
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## Output created: /tmp/RtmpbuOks4/mitch_report.html## [1] TRUEmitch_report(res=mfamily,outfile="mitch_family.html",overwrite=TRUE)## Note: overwriting existing report## Dataset saved as " /tmp/RtmpbuOks4/mitch_family.rds ".##
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## Output created: /tmp/RtmpbuOks4/mitch_report.html## [1] TRUEmitch_report(res=mgenus,outfile="mitch_genus.html",overwrite=TRUE)## Note: overwriting existing report## Dataset saved as " /tmp/RtmpbuOks4/mitch_genus.rds ".##
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## ordinary text without R code## output file: /home/mdz/projects/seasol-metagenome/differential_analysis_mz/mitch.knit.md## /usr/bin/pandoc +RTS -K512m -RTS /home/mdz/projects/seasol-metagenome/differential_analysis_mz/mitch.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/RtmpbuOks4/mitch_report.html --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/latex-div.lua --self-contained --variable bs3=TRUE --standalone --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=bootstrap --include-in-header /tmp/RtmpbuOks4/rmarkdown-strc3bb784f35fa.html --mathjax --variable 'mathjax-url:https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'##
## Output created: /tmp/RtmpbuOks4/mitch_report.html
## [1] TRUESession information
sessionInfo()## R version 4.1.0 (2021-05-18)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] pkgload_1.2.1 GGally_2.1.2
## [3] ggplot2_3.3.5 reshape2_1.4.4
## [5] beeswarm_0.4.0 gplots_3.1.1
## [7] gtools_3.9.2 tibble_3.1.2
## [9] dplyr_1.0.7 echarts4r_0.4.1
## [11] mitch_1.4.0 DESeq2_1.32.0
## [13] SummarizedExperiment_1.22.0 Biobase_2.52.0
## [15] MatrixGenerics_1.4.0 matrixStats_0.59.0
## [17] GenomicRanges_1.44.0 GenomeInfoDb_1.28.1
## [19] IRanges_2.26.0 S4Vectors_0.30.0
## [21] BiocGenerics_0.38.0
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-7 bit64_4.0.5 RColorBrewer_1.1-2
## [4] httr_1.4.2 rprojroot_2.0.2 tools_4.1.0
## [7] bslib_0.2.5.1 utf8_1.2.1 R6_2.5.0
## [10] KernSmooth_2.23-20 DBI_1.1.1 colorspace_2.0-2
## [13] withr_2.4.2 tidyselect_1.1.1 gridExtra_2.3
## [16] bit_4.0.4 compiler_4.1.0 desc_1.3.0
## [19] DelayedArray_0.18.0 sass_0.4.0 caTools_1.18.2
## [22] scales_1.1.1 genefilter_1.74.0 stringr_1.4.0
## [25] digest_0.6.27 rmarkdown_2.9 XVector_0.32.0
## [28] pkgconfig_2.0.3 htmltools_0.5.1.1 highr_0.9
## [31] fastmap_1.1.0 htmlwidgets_1.5.3 rlang_0.4.11
## [34] rstudioapi_0.13 RSQLite_2.2.7 shiny_1.6.0
## [37] jquerylib_0.1.4 generics_0.1.0 jsonlite_1.7.2
## [40] BiocParallel_1.26.1 RCurl_1.98-1.3 magrittr_2.0.1
## [43] GenomeInfoDbData_1.2.6 Matrix_1.3-4 Rcpp_1.0.7
## [46] munsell_0.5.0 fansi_0.5.0 lifecycle_1.0.0
## [49] stringi_1.7.3 yaml_2.2.1 MASS_7.3-54
## [52] zlibbioc_1.38.0 plyr_1.8.6 grid_4.1.0
## [55] blob_1.2.1 promises_1.2.0.1 crayon_1.4.1
## [58] lattice_0.20-44 Biostrings_2.60.1 splines_4.1.0
## [61] annotate_1.70.0 KEGGREST_1.32.0 locfit_1.5-9.4
## [64] knitr_1.33 pillar_1.6.1 geneplotter_1.70.0
## [67] XML_3.99-0.6 glue_1.4.2 evaluate_0.14
## [70] png_0.1-7 vctrs_0.3.8 httpuv_1.6.1
## [73] testthat_3.0.4 gtable_0.3.0 purrr_0.3.4
## [76] reshape_0.8.8 assertthat_0.2.1 cachem_1.0.5
## [79] xfun_0.24 mime_0.11 xtable_1.8-4
## [82] later_1.2.0 survival_3.2-11 AnnotationDbi_1.54.1
## [85] memoise_2.0.0 ellipsis_0.3.2