This supplementary file documents the commands run to compare the expression levels of CAGE clusters in libraries made with different template-switching oligonucleotides. It is also intended as a self-executable tutorial exemplifying how to make differential gene expression analysis with nanoCAGE in general.
The following commands are run in a command-line terminal on Unix systems.
The nanoCAGE library is named NCms10010. To better re-use commands, this name is put
in an environment variable.
export LIBRARY=NCms10010
The NCms10010 library was made with total RNA from rat muscle. It is
comparing template-switching oligonucleotides that end in RNA ®, DNA (d) or
LNA (l) bases. The comparisons were multiplexed in triplicates.
The data is a single-end MiSeq run (ID: 121012_M00528_0022_AMS2003997-00050)
of 4,682,200 reads. The output files (s_G1_L001_R1_001.fastq.gz and
s_G1_L001_R1_002.fastq.gz were decompressed, concatenated, and re-compressed
with xz. The resulting file is available for download from the supplementary
material, and from RIKEN. Deposition to DDBJ is in progress.
wget http://genome.gsc.riken.jp/plessy-20130430/$LIBRARY.fastq.xz
echo '47b797f36cd20d6548e80e09dc05daa1 NCms10010.fastq.xz' | md5sum -c
## NCms10010.fastq.xz: OK
The barcodes and sample IDs are associated in a whitespace-delimited file
called NCms10010.id containing the following. The sample IDs encode the type
of template-switching oligonucleotide used, which each letter representing the
chemical nature of the third, second and first nucleotide, from the 3′ end.
cat <<__ID__ > $LIBRARY.id
rrr_1 CACTGA
rrr_2 GCTCTC
rrr_3 TCGCGT
ddd_1 ATCGTG
ddd_2 CACGAT
ddd_3 GTATAC
ddl_1 ACAGAT
ddl_2 CTGACG
ddl_3 GAGTGA
dll_1 AGTAGC
dll_2 GCTGCA
dll_3 TCGAGC
lll_1 ATCATA
lll_2 CGATGA
lll_3 TATAGC
__ID__
Samples were demultiplexed with FASTX-toolkit.
The number of extracted reads was collected in a file named NCms10010.extracted.log.
xzcat $LIBRARY.fastq.xz |
fastx_barcode_splitter.pl --bcfile $LIBRARY.id --prefix $LIBRARY. --suffix .fq --bol --exact |
sed 1d | cut -f1,2 |
perl -ne 'print "extracted\t$_"' |
grep -v -e unmatched -e total |
tee $LIBRARY.extracted.log
## extracted ddd_1 333255
## extracted ddd_2 340917
## extracted ddd_3 148290
## extracted ddl_1 479148
## extracted ddl_2 48278
## extracted ddl_3 37851
## extracted dll_1 111856
## extracted dll_2 42939
## extracted dll_3 55763
## extracted lll_1 100226
## extracted lll_2 89915
## extracted lll_3 61455
## extracted rrr_1 776221
## extracted rrr_2 351372
## extracted rrr_3 186395
The reads start with 6 bases of barcode, 8 bases of random fingerprint, 4 bases of spacer, and 3 base of linker, that are all removed in the following command. The reads were also trimmed in 3′ to ensure that the results were comparable with a reference HiSeq run (not covered in this document).
for FASTQ in *.fq
do
fastx_trimmer -f 22 -l 52 -Q33 < $FASTQ | sponge $FASTQ
done
Download TagDust and install it in the user's path.
cat > tagdust.fa <<__TagDust__
>TS (before barcode)
TAGTCGAACTGAAGGTCTCCAGCA
>RT (without random bases)
TAGTCGAACTGAAGGTCTCCGAACCGCTCTTCCGATCT
>empty (TS linker + RT reverse-complemented)
TATAGGGAGATCGGAAGAGCGGTTCGGAGACCTTCAGTTCGACTA
__TagDust__
for ID in $( awk '{print $1}' $LIBRARY.id )
do
echo -ne "tagdust\t$ID\t"
tagdust tagdust.fa N*.$ID.fq -o $LIBRARY.$ID.dusted.fastq 2>&1 |
grep -e rejected | cut -f1
done | tee $LIBRARY.tagdust.log
## tagdust rrr_1 13723
## tagdust rrr_2 2625
## tagdust rrr_3 1596
## tagdust ddd_1 3691
## tagdust ddd_2 3927
## tagdust ddd_3 2432
## tagdust ddl_1 8646
## tagdust ddl_2 721
## tagdust ddl_3 531
## tagdust dll_1 1786
## tagdust dll_2 680
## tagdust dll_3 671
## tagdust lll_1 1312
## tagdust lll_2 1293
## tagdust lll_3 296
The following assumes the genome downloaded and indexed for BWA in
the current directory, using rn4_male as a base name.
GENOME=rn4_male
for FQ in *dusted.fastq
do
bwa aln -t8 $GENOME -f $(basename $FQ .dusted.fastq).sai $FQ
bwa samse $GENOME $(basename $FQ .dusted.fastq).sai $FQ |
samtools view -uS - |
samtools sort - $(basename $FQ .dusted.fastq)
done
rRNAdust is available in the supplementary material at http://genome.gsc.riken.jp/plessy-20130430/rRNAdust_1.02.tar.gz.
Download the reference rRNA sequences at http://www.ncbi.nlm.nih.gov/nuccore/V01270.1 and http://www.ncbi.nlm.nih.gov/gene/170603, and save them in a file called rat_rDNA.fa.
for ID in $( awk '{print $1}' $LIBRARY.id )
do
echo -ne "rdna\t$ID\t"
(rRNAdust -t8 rat_rDNA.fa $LIBRARY.$ID.bam | samtools view -bS - 2> /dev/null | sponge $LIBRARY.$ID.bam) 2>&1 | sed 's/Excluded: //'
done | tee $LIBRARY.rdna.log
## rdna rrr_1 239373
## rdna rrr_2 112182
## rdna rrr_3 56075
## rdna ddd_1 56429
## rdna ddd_2 78449
## rdna ddd_3 29489
## rdna ddl_1 178108
## rdna ddl_2 10240
## rdna ddl_3 6743
## rdna dll_1 23612
## rdna dll_2 10327
## rdna dll_3 15254
## rdna lll_1 21777
## rdna lll_2 15454
## rdna lll_3 12189
for ID in $( awk '{print $1}' $LIBRARY.id )
do
echo -ne "mapped\t$ID\t"
(samtools flagstat $LIBRARY.$ID.bam | grep mapped | grep %) | cut -f1 -d' '
done | tee $LIBRARY.mapped.log
## mapped rrr_1 469027
## mapped rrr_2 211307
## mapped rrr_3 113141
## mapped ddd_1 259605
## mapped ddd_2 242952
## mapped ddd_3 109710
## mapped ddl_1 265929
## mapped ddl_2 34369
## mapped ddl_3 28418
## mapped dll_1 79426
## mapped dll_2 27701
## mapped dll_3 35436
## mapped lll_1 68930
## mapped lll_2 65139
## mapped lll_3 43334
Strand invasion was described in Tang et al, Nucl. Acids Res. (2013) 41 (3):e44, and is more frequent in LNA- or DNA-based template-switching oligonucleotides.
The following commands need an updated version of the find_strand_invasion.pl
that was in Tang et al's supplementary material, where a new -f option is
added, with the same semantics as in samtools. It is available at
http://genome.gsc.riken.jp/plessy-20130430/find_strand_invasion-20130307.pl.
ERRORS=2
for BAM in $LIBRARY.???_?.bam
do
find_strand_invasion.pl -f $BAM -g rn4_male.fa -e $ERRORS -s TATA
done
for ID in $( awk '{print $1}' $LIBRARY.id )
do
echo -ne "strand-invasion-$ERRORS\t$ID\t"
(samtools flagstat ${LIBRARY}.${ID}_nw_${ERRORS}_??????_removed_sorted.bam | grep mapped | grep %) | cut -f1 -d' '
done | tee $LIBRARY.strand-invasion-$ERRORS.log
## strand-invasion-2 rrr_1 51979
## strand-invasion-2 rrr_2 20470
## strand-invasion-2 rrr_3 11137
## strand-invasion-2 ddd_1 201479
## strand-invasion-2 ddd_2 172866
## strand-invasion-2 ddd_3 82123
## strand-invasion-2 ddl_1 130483
## strand-invasion-2 ddl_2 15228
## strand-invasion-2 ddl_3 16489
## strand-invasion-2 dll_1 39596
## strand-invasion-2 dll_2 9538
## strand-invasion-2 dll_3 13259
## strand-invasion-2 lll_1 27204
## strand-invasion-2 lll_2 24179
## strand-invasion-2 lll_3 14127
Level 1 clusters are single-nucleotide resolution data representing the 5′ ends of the CAGE tags. Level 2 clusters are groups of level 1 clusters that are separated by 20 or less nucleotides.
The level1.py and level2.py scripts implement tag clustering like in the
FANTOM3 and
FANTOM4 projects, and are available at
http://genome.gsc.riken.jp/plessy-20130430/PromoterPipeline_20130430.tar.gz.
They output their results in Order Switchable Column (OSC) format, where each line is a cluster, and each library gives one column counting the tags in the clusters, and another column where the counts are normalised in parts per million.
See also Carninci et al., Nature Genetics 38 626-635 (2006) and Suzuki et al., Nature Genetics 41 553-562 (2009) for original examples of CAGE tag clustering.
level1.py -o $LIBRARY.l1.osc.gz -F 516 \
NCms10010.???_?.bam \
NCms10010.???_?_nw_?_??????_filtered_sorted.bam
level2.py -o $LIBRARY.l2.osc -t 0 $LIBRARY.l1.osc.gz
gzip $LIBRARY.l2.osc
The resulting file NCms10010.l1.osc.gz can be loaded in the Zenbu system to browse the alignments on the rat genome.
Data from ENSEMBL 69 were retrieved via
BioMart, with the following XML query.
Note that external_gene_id is called Associated Gene Name in the web
interface.
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE Query>
<Query virtualSchemaName = "default" formatter = "TSV" header = "0" uniqueRows = "0" count = "" datasetConfigVersion = "0.6" >
<Dataset name = "rnorvegicus_gene_ensembl" interface = "default" >
<Attribute name = "ensembl_gene_id" />
<Attribute name = "ensembl_transcript_id" />
<Attribute name = "chromosome_name" />
<Attribute name = "strand" />
<Attribute name = "external_gene_id" />
<Attribute name = "transcript_start" />
<Attribute name = "transcript_end" />
<Attribute name = "gene_biotype" />
<Attribute name = "exon_chrom_start" />
<Attribute name = "exon_chrom_end" />
</Dataset>
</Query>
Convert coordinates to gene names
cat mart_export.txt |
sed -e 1d -e 's|\t\t|\tno_symbol\t|' |
awk '{OFS="\t"} {print $3, $6, $7, $5, 0, $4}' |
grep -v ^[JA] |
uniq |
sed -e 's/-1$/-/' \
-e 's/1$/+/' \
-e 's/^/chr/' |
sort -k1,1 -k2,2n \
> rn4_male.symbols.bed
### Gene symbols
zcat $LIBRARY.l1.osc.gz | grep -v \# | sed 1d | awk '{OFS="\t"}{print $2, $3, $4, "l2", "1000", $5}' > $LIBRARY.l1.bed
zcat $LIBRARY.l2.osc.gz | grep -v \# | sed 1d | awk '{OFS="\t"}{print $2, $3, $4, "l2", "1000", $5}' > $LIBRARY.l2.bed
zcat $LIBRARY.l2.osc.gz | grep -v \# | sed 1d | awk '{OFS="\t"}{print $2, $3, $4, "l2", "1000", $5}' > $LIBRARY.l2.bed
bedtools intersect -a $LIBRARY.l2.bed -b rn4_male.symbols.bed -s -loj |
awk '{OFS="\t"}{print $1":"$2"-"$3$6,$10}' |
bedtools groupby -g 1 -c 2 -o distinct > $LIBRARY.l2.genes
Download the repeatmasker track from the UCSC genome browser and save it in a file called rn4_male.repeatmasker.bed.
bedtools intersect -a $LIBRARY.l2.bed -b rn4_male.repeatmasker.bed -s -loj |
awk '{OFS="\t"}{print $1":"$2"-"$3$6,$10}' |
bedtools groupby -g 1 -c 2 -o distinct > $LIBRARY.l2.rmsk
The following commands are run in the R package for statistical computing.
The oscR library is available at https://github.com/charles-plessy/oscR.
The following commands load the level 1 and 2 clusters into data frames where the column names correspond to the sample IDs defined above.
LIBRARY <- system("echo $LIBRARY", intern = TRUE)
library(oscR)
l1 <- read.osc(paste(LIBRARY, "l1", "osc", "gz", sep = "."), drop.coord = T,
drop.norm = T)
l2 <- read.osc(paste(LIBRARY, "l2", "osc", "gz", sep = "."), drop.coord = T,
drop.norm = T)
colnames(l1) <- sub("raw.NCms1.....", "", colnames(l1))
colnames(l2) <- sub("raw.NCms1.....", "", colnames(l2))
colnames(l1) <- sub("_......_filtered_sorted", "", colnames(l1))
colnames(l2) <- sub("_......_filtered_sorted", "", colnames(l2))
The following commands defined convenient shortcuts to manipulates groups of
libraries. The presence of nw_2 in the names indicate that strand-invasion
artifacts have been removed.
ddd <- c("ddd_1", "ddd_2", "ddd_3")
ddl <- c("ddl_1", "ddl_2", "ddl_3")
dll <- c("dll_1", "dll_2", "dll_3")
lll <- c("lll_1", "lll_2", "lll_3")
rrr <- c("rrr_1", "rrr_2", "rrr_3")
all <- c(rrr, lll, dll, ddl, ddd)
ddd_nw_2 <- c("ddd_1_nw_2", "ddd_2_nw_2", "ddd_3_nw_2")
ddl_nw_2 <- c("ddl_1_nw_2", "ddl_2_nw_2", "ddl_3_nw_2")
dll_nw_2 <- c("dll_1_nw_2", "dll_2_nw_2", "dll_3_nw_2")
lll_nw_2 <- c("lll_1_nw_2", "lll_2_nw_2", "lll_3_nw_2")
rrr_nw_2 <- c("rrr_1_nw_2", "rrr_2_nw_2", "rrr_3_nw_2")
all_nw_2 <- c(rrr_nw_2, lll_nw_2, dll_nw_2, ddl_nw_2, ddd_nw_2)
TPM <- function(clusters) {
clusters.tpm <- data.frame(prop.table(as.matrix(clusters), 2) * 1e+06)
colnames(clusters.tpm) <- colnames(clusters)
return(clusters.tpm)
}
L2 <- TPM(l2)
L2.means <- data.frame(ddd = apply(L2[, ddd], 1, mean), ddl = apply(L2[, ddl],
1, mean), dll = apply(L2[, dll], 1, mean), lll = apply(L2[, lll], 1, mean),
rrr = apply(L2[, rrr], 1, mean))
L2.means_nw_2 <- data.frame(ddd = apply(L2[, ddd_nw_2], 1, mean), ddl = apply(L2[,
ddl_nw_2], 1, mean), dll = apply(L2[, dll_nw_2], 1, mean), lll = apply(L2[,
lll_nw_2], 1, mean), rrr = apply(L2[, rrr_nw_2], 1, mean))
L2.sd <- data.frame(ddd = apply(L2[, ddd], 1, sd), ddl = apply(L2[, ddl], 1,
sd), dll = apply(L2[, dll], 1, sd), lll = apply(L2[, lll], 1, sd), rrr = apply(L2[,
rrr], 1, sd))
genesymbols <- read.table(paste(LIBRARY, "l2", "genes", sep = "."), col.names = c("cluster",
"symbol"))
rownames(genesymbols) <- genesymbols$cluster
genesymbols$rmsk <- read.table(paste(LIBRARY, "l2", "rmsk", sep = "."), col.names = c("cluster",
"rmsk"))[, "rmsk"]
Statistical comparisons using edgeR.
library(edgeR)
The following plots represent:
the multidimensional scaling of the samples, where spatial separation between the two sets of triplicates indicates that the factor that is compared (type of template-switching oligonucleotide) introduces more differences that the simple technical fluctuations,
the expression levels of the CAGE clusters as a M-A plot, where dots in red are clusters significantly enriched in one type of libraries. Vertical distance from the horizontal midline represent the amplitude of the differences, and distance on the horizontal axis represents the average strength of expression.
The following comparisons show the difference (or lack of it) between non-filtered and filtered data, and then explore the filtered data in more details.
x <- DGEList(counts = l2[, c(lll, rrr)], group = c(rep("lll", 3), rep("rrr",
3)), remove.zeros = TRUE)
## Removing 114680 rows with all zero counts.
x <- calcNormFactors(x)
x <- estimateCommonDisp(x)
x <- estimateTagwiseDisp(x)
x.com <- exactTest(x)
lr <- x
lr.com <- x.com
plotMDS(lr)
plotSmear(lr.com, de.tags = rownames(lr.com)[decideTestsDGE(lr.com) != 0], cex = 0.8,
main = "LNA / RNA", ylab = "LNA (bottom) / RNA (top)")
x <- DGEList(counts = l2[, c(lll_nw_2, rrr_nw_2)], group = c(rep("lll_nw_2",
3), rep("rrr_nw_2", 3)), remove.zeros = TRUE)
## Removing 123000 rows with all zero counts.
x <- calcNormFactors(x)
x <- estimateCommonDisp(x)
x <- estimateTagwiseDisp(x)
x.com <- exactTest(x)
lr_nw_2 <- x
lr_nw_2.com <- x.com
plotMDS(lr_nw_2)
plotSmear(lr_nw_2.com, de.tags = rownames(lr_nw_2.com)[decideTestsDGE(lr_nw_2.com) !=
0], cex = 0.8, main = "LNA / RNA (filtered)", ylab = "LNA (bottom) / RNA (top)")
lr_nw_2.up <- sum(decideTestsDGE(lr_nw_2.com) > 0)
lr_nw_2.down <- sum(decideTestsDGE(lr_nw_2.com) < 0)
866 clusters were enriched and 44 were depleted in RNA libraries compared to LNA. The top 100 stronger fold changes in each direction are shown below.
# Summary of the top 100 clusters enriched in RNA libraries.
summary(merge(subset(topTags(lr_nw_2.com, Inf)$table, logFC > 0)[1:100, ], genesymbols[,
-1], by = 0, sort = FALSE))
## Row.names logFC logCPM PValue FDR symbol rmsk
## Length:100 Min. : 2.03 Min. : 7.27 Min. :0.00e+00 Min. :0.00e+00 . :40 . :86
## Class :AsIs 1st Qu.: 4.93 1st Qu.: 8.07 1st Qu.:0.00e+00 1st Qu.:0.00e+00 Myh2 : 3 7SLRNA :11
## Mode :character Median : 6.55 Median : 8.86 Median :0.00e+00 Median :0.00e+00 Eno3 : 2 GC_rich: 1
## Mean : 6.45 Mean : 9.69 Mean :1.03e-15 Mean :1.47e-12 Myl1 : 2 L1MCa : 1
## 3rd Qu.: 7.96 3rd Qu.:11.12 3rd Qu.:1.24e-16 3rd Qu.:2.22e-13 Acat1 : 1 L1_Rat1: 1
## Max. :11.82 Max. :16.94 Max. :1.11e-14 Max. :1.50e-11 Aco2 : 1 4.5SRNA: 0
## (Other):51 (Other): 0
# Top 15 clusters enriched in RNA libraries.
merge(subset(topTags(lr_nw_2.com, Inf)$table, logFC > 0)[1:15, ], genesymbols[,
-1], by = 0, sort = FALSE)
## Row.names logFC logCPM PValue FDR symbol rmsk
## 1 chr5:5309920-5309978- 9.729 11.305 4.054e-109 5.622e-104 . 7SLRNA
## 2 chr8:62243583-62243658+ 8.946 11.343 2.517e-87 1.745e-82 . 7SLRNA
## 3 chr9:65727648-65727774- 6.269 12.740 7.765e-76 3.556e-71 Myl1 .
## 4 chr11:68375411-68375528- 7.333 11.185 1.026e-75 3.556e-71 . 7SLRNA
## 5 chr12:22851453-22851493- 7.585 11.187 1.724e-64 4.781e-60 . 7SLRNA
## 6 chr3:70682541-70682599+ 9.607 11.185 6.080e-61 1.405e-56 . 7SLRNA
## 7 chr6:91117279-91117393+ 8.852 11.247 1.312e-60 2.599e-56 . 7SLRNA
## 8 chrX:54985894-54985915+ 11.819 11.312 3.036e-55 5.262e-51 . 7SLRNA
## 9 chr2:145713673-145713772+ 2.927 14.250 1.063e-53 1.637e-49 . .
## 10 chrM:12334-13779+ 2.595 12.261 1.519e-53 2.106e-49 . .
## 11 chr1:202745119-202745217+ 6.467 14.539 1.020e-51 1.286e-47 . .
## 12 chr7:68333299-68333423- 8.331 11.253 3.925e-51 4.535e-47 . 7SLRNA
## 13 chr2:77482169-77482291+ 8.263 11.194 2.203e-48 2.350e-44 Znf622 7SLRNA
## 14 chr10:59473911-59473930+ 7.875 11.178 2.466e-47 2.443e-43 . 7SLRNA
## 15 chr1:85599001-85599082+ 6.291 8.801 1.865e-39 1.724e-35 Hspb6 .
# Summary of the top 100 clusters enriched in LNA libraries.
summary(merge(subset(topTags(lr_nw_2.com, Inf)$table, logFC < 0)[1:100, ], genesymbols[,
-1], by = 0, sort = FALSE))
## Row.names logFC logCPM PValue FDR symbol rmsk
## Length:100 Min. :-7.96 Min. : 5.37 Min. :0.000000 Min. :0.0000 . :66 . :50
## Class :AsIs 1st Qu.:-5.46 1st Qu.: 5.72 1st Qu.:0.000056 1st Qu.:0.0108 Armc2 : 3 (CAGAGA)n :31
## Mode :character Median :-4.46 Median : 6.12 Median :0.000420 Median :0.0609 Nrd1 : 2 GA-rich : 5
## Mean :-4.41 Mean : 6.70 Mean :0.000874 Mean :0.1001 Sesn1 : 2 (AGTAG)n,(TAGGG)n: 1
## 3rd Qu.:-3.50 3rd Qu.: 7.15 3rd Qu.:0.001418 3rd Qu.:0.1664 Acbd3 : 1 B1_Mur3 : 1
## Max. :-1.20 Max. :12.55 Max. :0.003802 Max. :0.3682 Chordc1: 1 B1_Rn : 1
## (Other):25 (Other) :11
Positive fold change indicate enrichment in RNA libraries. The 7SLRNA hits are concentrated at the top of the list. LNA libraries are enriched for hits on CAGAGA repeats, even beyond the significance level (FDR) of the statistical comparison.
library(reshape)
## Loading required package: plyr
## Attaching package: 'reshape'
## The following object is masked from 'package:plyr':
##
## rename, round_any
library(ggplot2)
srprna <- rownames(subset(genesymbols, rmsk == "7SLRNA"))
srprna.expression <- melt(L2[srprna, c(rrr_nw_2, ddd_nw_2, lll_nw_2)], measure.vars = c(rrr_nw_2, ddd_nw_2, lll_nw_2))
for (group in c("ddd", "lll", "rrr")) srprna.expression[grep(group, srprna.expression$variable), "group"] <- group
srprna.expression$group <- reorder(factor(srprna.expression$group), srprna.expression$value, function(x) sum(x) * -1)
qplot(data = srprna.expression, value, reorder(variable, value, sum), xlab = "Parts per million", ylab = "Library", main = "Expression levels of 7SL RNA genes",
col = group)
The measured expression of 7SL RNA is strongest in RNA libraries, strong in DNA libraries, and weak in LNA libraries.
x <- DGEList(counts = l2[, c(rrr, ddd)], group = factor(c(rep("rrr", 3), rep("ddd", 3)), levels = c("rrr", "ddd")), remove.zeros = TRUE)
## Removing 89641 rows with all zero counts.
x <- calcNormFactors(x)
x <- estimateCommonDisp(x)
x <- estimateTagwiseDisp(x)
x.com <- exactTest(x)
rd <- x
rd.com <- x.com
plotMDS(x)
plotSmear(rd.com, de.tags = rownames(rd.com)[decideTestsDGE(rd.com) != 0], cex = 0.8, main = "RNA / DNA", ylab = "RNA (bottom) / LNA (top)")
x <- DGEList(counts = l2[, c(rrr_nw_2, ddd_nw_2)], group = factor(c(rep("rrr_nw_2", 3), rep("ddd_nw_2", 3)), levels = c("rrr_nw_2", "ddd_nw_2")), remove.zeros = TRUE)
## Removing 106919 rows with all zero counts.
x <- calcNormFactors(x)
x <- estimateCommonDisp(x)
x <- estimateTagwiseDisp(x)
x.com <- exactTest(x)
plotMDS(x)
rd_nw_2 <- x
rd_nw_2.com <- x.com
plotSmear(rd_nw_2.com, de.tags = rownames(rd_nw_2.com)[decideTestsDGE(rd_nw_2.com) != 0], cex = 0.8, main = "RNA / DNA (filtered)", ylab = "RNA (bottom) / DNA (top)")
626 clusters were enriched and 87 were depleted in RNA libraries compared to DNA.
The majority of the clusters in the top 100 enriched in the RNA libraries did not overlap with repeat elements, and were overlapping with loci having gene symbols. In contrast, the majority of the top 100 clusters enriched in the DNA libraries did not overlap with known genes. A mild enrichment for GGGTG simple repeats is noted.
# Summary of the top 100 clusters enriched in DNA libraries.
summary(merge(subset(topTags(rd_nw_2.com, Inf)$table, logFC > 0)[1:100, ], genesymbols[, -1], by = 0, sort = FALSE))
## Row.names logFC logCPM PValue FDR symbol rmsk
## Length:100 Min. : 1.41 Min. : 5.21 Min. :0.00e+00 Min. :0.00000 . :85 . :81
## Class :AsIs 1st Qu.: 4.70 1st Qu.: 5.60 1st Qu.:0.00e+00 1st Qu.:0.00001 F1LY53_RAT : 5 (GGGTG)n : 4
## Mode :character Median : 5.76 Median : 6.23 Median :3.70e-06 Median :0.00145 F1M1V3_RAT : 2 GA-rich : 3
## Mean : 5.84 Mean : 6.84 Mean :8.09e-05 Mean :0.01749 D3ZWL6_RAT : 1 G-rich : 3
## 3rd Qu.: 6.81 3rd Qu.: 7.63 3rd Qu.:1.07e-04 3rd Qu.:0.02649 Kpnb1 : 1 (CAGA)n,GA-rich: 2
## Max. :10.54 Max. :13.73 Max. :5.20e-04 Max. :0.09911 LOC100364794: 1 (CAGA)n : 1
## (Other) : 5 (Other) : 6
# Summary of the top 100 clusters enriched in RNA libraries.
summary(merge(subset(topTags(rd_nw_2.com, Inf)$table, logFC < 0)[1:100, ], genesymbols[, -1], by = 0, sort = FALSE))
## Row.names logFC logCPM PValue FDR symbol rmsk
## Length:100 Min. :-9.17 Min. : 6.92 Min. :0.00e+00 Min. :0.00e+00 . :36 . :91
## Class :AsIs 1st Qu.:-6.09 1st Qu.: 7.62 1st Qu.:0.00e+00 1st Qu.:0.00e+00 7SK : 1 7SLRNA : 6
## Mode :character Median :-5.03 Median : 8.06 Median :2.00e-15 Median :4.00e-12 Acat1 : 1 7SK : 1
## Mean :-5.15 Mean : 8.79 Mean :3.38e-13 Mean :5.07e-10 Aco2 : 1 GC_rich: 1
## 3rd Qu.:-3.93 3rd Qu.: 9.17 3rd Qu.:7.00e-14 3rd Qu.:1.30e-10 Actb : 1 L1MCa : 1
## Max. :-1.83 Max. :16.75 Max. :3.99e-12 Max. :5.61e-09 Atp5j : 1 4.5SRNA: 0
## (Other):59 (Other): 0
x <- DGEList(counts = l2[, c(ddd, lll)], group = c(rep("ddd", 3), rep("lll", 3)), remove.zeros = TRUE)
## Removing 168500 rows with all zero counts.
x <- calcNormFactors(x)
x <- estimateCommonDisp(x)
x <- estimateTagwiseDisp(x)
x.com <- exactTest(x)
dl <- x
dl.com <- x.com
plotMDS(x)
plotSmear(dl.com, de.tags = rownames(dl.com)[decideTestsDGE(dl.com) != 0], cex = 0.8, main = "DNA / LNA", ylab = "DNA (bottom) / LNA (top)")
x <- DGEList(counts = l2[, c(ddd_nw_2, lll_nw_2)], group = c(rep("ddd_nw_2", 3), rep("lll_nw_2", 3)), remove.zeros = TRUE)
## Removing 190706 rows with all zero counts.
x <- calcNormFactors(x)
x <- estimateCommonDisp(x)
x <- estimateTagwiseDisp(x)
x.com <- exactTest(x)
plotMDS(x)
dl_nw_2 <- x
dl_nw_2.com <- x.com
plotSmear(dl_nw_2.com, de.tags = rownames(dl_nw_2.com)[decideTestsDGE(dl_nw_2.com) != 0], cex = 0.8, main = "DNA / LNA (filtered)", ylab = "DNA (bottom) / LNA (top)")
5 clusters were enriched and 39 were depleted in LNA libraries compared to DNA.
After filtering out strand-invasion artifacts, only few significant differences remain between the DNA and LNA libraries. 7SLRNA was also depleted in LNA libraries, and CAGAGA repeats were enriched.
summary(decideTestsDGE(dl_nw_2.com))
## [,1]
## -1 39
## 0 70908
## 1 5
summary(merge(subset(topTags(dl_nw_2.com, Inf)$table, logFC > 0)[1:100, ], genesymbols[, -1], by = 0, sort = FALSE))
## Row.names logFC logCPM PValue FDR symbol rmsk
## Length:100 Min. :0.896 Min. : 6.36 Min. :0.00000 Min. :0.000 . :49 . :62
## Class :AsIs 1st Qu.:2.921 1st Qu.: 6.76 1st Qu.:0.00239 1st Qu.:1.000 D4A4B0_RAT: 3 (CAGAGA)n :21
## Mode :character Median :3.923 Median : 7.36 Median :0.00875 Median :1.000 Mbnl2 : 2 GA-rich : 5
## Mean :3.922 Mean : 7.85 Mean :0.00976 Mean :0.871 NEXN_RAT : 2 B1_Mur3 : 2
## 3rd Qu.:4.866 3rd Qu.: 8.26 3rd Qu.:0.01569 3rd Qu.:1.000 Nrd1 : 2 (AGTAG)n,(TAGGG)n: 1
## Max. :9.389 Max. :13.91 Max. :0.02512 Max. :1.000 Nsbp1 : 2 B4A : 1
## (Other) :40 (Other) : 8
summary(merge(subset(topTags(dl_nw_2.com, Inf)$table, logFC < 0)[1:100, ], genesymbols[, -1], by = 0, sort = FALSE))
## Row.names logFC logCPM PValue FDR symbol rmsk
## Length:100 Min. :-8.447 Min. : 6.71 Min. :0.00e+00 Min. :0.0000 . :57 . :79
## Class :AsIs 1st Qu.:-5.815 1st Qu.: 7.60 1st Qu.:3.40e-06 1st Qu.:0.0085 Myh2 :11 7SLRNA :11
## Mode :character Median :-4.765 Median : 8.68 Median :1.54e-04 Median :0.1882 F1LY53_RAT: 3 (GGGTG)n : 3
## Mean :-4.647 Mean : 8.68 Mean :5.67e-04 Mean :0.3550 Col3a1 : 2 G-rich : 2
## 3rd Qu.:-3.204 3rd Qu.: 9.19 3rd Qu.:8.75e-04 3rd Qu.:0.6733 Myl1 : 2 (CAGA)n : 1
## Max. :-0.776 Max. :15.64 Max. :2.46e-03 Max. :1.0000 Tnnt3 : 2 (CAGA)n,GA-rich: 1
## (Other) :23 (Other) : 3
save.image("analysis.RData")
# One table per list of significantly over-represented clusters.
write.csv(file = "R-L.csv", merge(subset(topTags(lr_nw_2.com, Inf)$table, logFC > 0 & FDR < 0.1), genesymbols[, -1], by = "row.names", all.x = "T", sort = FALSE),
row.names = FALSE)
write.csv(file = "L-R.csv", merge(subset(topTags(lr_nw_2.com, Inf)$table, logFC < 0 & FDR < 0.1), genesymbols[, -1], by = "row.names", all.x = "T", sort = FALSE),
row.names = FALSE)
write.csv(file = "D-R.csv", merge(subset(topTags(rd_nw_2.com, Inf)$table, logFC > 0 & FDR < 0.1), genesymbols[, -1], by = "row.names", all.x = "T", sort = FALSE),
row.names = FALSE)
write.csv(file = "R-D.csv", merge(subset(topTags(rd_nw_2.com, Inf)$table, logFC < 0 & FDR < 0.1), genesymbols[, -1], by = "row.names", all.x = "T", sort = FALSE),
row.names = FALSE)
write.csv(file = "L-D.csv", merge(subset(topTags(dl_nw_2.com, Inf)$table, logFC > 0 & FDR < 0.1), genesymbols[, -1], by = "row.names", all.x = "T", sort = FALSE),
row.names = FALSE)
write.csv(file = "D-L.csv", merge(subset(topTags(dl_nw_2.com, Inf)$table, logFC < 0 & FDR < 0.1), genesymbols[, -1], by = "row.names", all.x = "T", sort = FALSE),
row.names = FALSE)
# One summary table combining all the results.
l2[rownames(topTags(lr_nw_2.com, Inf)), "LR.logFC"] <- topTags(lr_nw_2.com, Inf)$table$logFC
l2[rownames(topTags(lr_nw_2.com, Inf)), "LR.FDR"] <- topTags(lr_nw_2.com, Inf)$table$FDR
l2[rownames(topTags(rd_nw_2.com, Inf)), "RD.logFC"] <- topTags(rd_nw_2.com, Inf)$table$logFC
l2[rownames(topTags(rd_nw_2.com, Inf)), "RD.FDR"] <- topTags(rd_nw_2.com, Inf)$table$FDR
l2[rownames(topTags(dl_nw_2.com, Inf)), "DL.logFC"] <- topTags(dl_nw_2.com, Inf)$table$logFC
l2[rownames(topTags(dl_nw_2.com, Inf)), "DL.FDR"] <- topTags(dl_nw_2.com, Inf)$table$FDR
l2$symbol <- genesymbols$symbol
l2$rmsk <- genesymbols$rmsk
write.csv(file = paste(LIBRARY, "DGE", "csv", sep = "."), l2)
This analysis was done on a iMac with a i7 hyperthreaded quad-core CPU (2.93 GHz) and 12 GiB of memory, running Debian system, with the following packages installed.
sponge command)This tutorial was made with the knitr library for
R, that produces HTML pages from templates containing executable code.
sessionInfo()
## R version 3.0.1 (2013-05-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
##
## locale:
## [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C LC_TIME=en_GB.UTF-8 LC_COLLATE=en_GB.UTF-8 LC_MONETARY=en_GB.UTF-8
## [6] LC_MESSAGES=en_GB.UTF-8 LC_PAPER=C LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] methods stats graphics grDevices utils datasets base
##
## other attached packages:
## [1] ggplot2_0.9.3.1 reshape_0.8.4 plyr_1.8 edgeR_3.2.4 limma_3.16.7 oscR_0.1.1
##
## loaded via a namespace (and not attached):
## [1] codetools_0.2-8 colorspace_1.2-2 dichromat_2.0-0 digest_0.6.3 evaluate_0.4.3 formatR_0.7 grid_3.0.1
## [8] gtable_0.1.2 knitr_1.2 labeling_0.1 MASS_7.3-26 munsell_0.4 proto_0.3-10 RColorBrewer_1.0-5
## [15] reshape2_1.2.2 scales_0.2.3 stringr_0.6.2 tools_3.0.1