Last updated: 2020-01-21
Checks: 7 0
Knit directory: 20170327_Psen2S4Ter_RNASeq/
This reproducible R Markdown analysis was created with workflowr (version 1.6.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20200119)
was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.
Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish
or wflow_git_commit
). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
Ignored files:
Ignored: .Rhistory
Ignored: .Rproj.user/
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote
), click on the hyperlinks in the table below to view them.
File | Version | Author | Date | Message |
---|---|---|---|---|
Rmd | c560637 | Steve Ped | 2020-01-20 | Started DE analysis |
Rmd | bc12101 | Steve Ped | 2020-01-20 | Added bash pipeline |
html | bc12101 | Steve Ped | 2020-01-20 | Added bash pipeline |
library(ngsReports)
library(tidyverse)
library(magrittr)
library(edgeR)
library(AnnotationHub)
library(ensembldb)
library(scales)
library(pander)
library(cowplot)
library(corrplot)
if (interactive()) setwd(here::here())
theme_set(theme_bw())
panderOptions("big.mark", ",")
panderOptions("table.split.table", Inf)
panderOptions("table.style", "rmarkdown")
twoCols <- c(rgb(0.8, 0.1, 0.1), rgb(0.2, 0.2, 0.8))
ah <- AnnotationHub() %>%
subset(species == "Danio rerio") %>%
subset(rdataclass == "EnsDb")
ensDb <- ah[["AH74989"]]
grTrans <- transcripts(ensDb)
trLengths <- exonsBy(ensDb, "tx") %>%
width() %>%
vapply(sum, integer(1))
mcols(grTrans)$length <- trLengths[names(grTrans)]
samples <- read_csv("data/samples.csv") %>%
mutate(reads = str_extract(fqName, "R[12]")) %>%
dplyr::rename(Filename = fqName)
labels <- structure(
samples$sampleID,
names = samples$Filename
)
In order to perform adequate QC, an EnsDb
object was obtained for Ensembl release 98 using the AnnotationHub
package. This provided the GC content and length for each of the 65,905 transcripts contained in that release.
Metadata for each fastq file was also loaded. Reads were provided as paired-end reads, with \(n=4\) samples for each genotype.
rawFqc <- list.files(
path = "data/0_rawData/FastQC/",
pattern = "zip",
full.names = TRUE
) %>%
FastqcDataList()
Library Sizes for the raw, unprocessed dataset ranged between 27,979,654 and 37,144,975 reads.
r1 <- grepl("R1", fqName(rawFqc))
plotReadTotals(rawFqc[r1], labels = labels[r1], barCols = twoCols)
Version | Author | Date |
---|---|---|
bc12101 | Steve Ped | 2020-01-20 |
As this was a total RNA dataset, GC content will provide a clear marker of the success of rRNA depletion. This was plotted, with all R2 reads showing a far greater spike in GC content at 81%, which is likely due to incomplete rRNA depletion. In particular, the mutant samples appeared most significantly affected raising possibility that overall GC content may be affected in these samples.
gcPlots <- list(
r1 = plotGcContent(
x = rawFqc[r1],
labels = labels[r1],
plotType = "line",
gcType = "Transcriptome",
species = "Drerio"
),
r2 = plotGcContent(
x = rawFqc[!r1],
labels = labels[!r1],
plotType = "line",
gcType = "Transcriptome",
species = "Drerio"
)
)
lg <- get_legend(gcPlots$r2 + theme(legend.position = "bottom"))
plot_grid(
plot_grid(
r1 = gcPlots$r1 +
ggtitle("R1: GC Distribution", subtitle = c()) +
theme(legend.position = "none"),
r2 = gcPlots$r2 +
ggtitle("R2: GC Distribution", subtitle = c()) +
theme(legend.position = "none")
),
lg = lg,
nrow = 2,
rel_heights = c(5,2)
)
Version | Author | Date |
---|---|---|
bc12101 | Steve Ped | 2020-01-20 |
rawGC <- getModule(rawFqc, "Per_sequence_GC") %>%
group_by(Filename) %>%
mutate(Freq = Count / sum(Count)) %>%
dplyr::filter(GC_Content > 70) %>%
summarise(Freq = sum(Freq)) %>%
arrange(desc(Freq)) %>%
left_join(samples)
rawGC %>%
ggplot(aes(sampleID, Freq, fill = reads)) +
geom_bar(stat = "identity", position = "dodge") +
facet_wrap(~genotype, scales = "free_x") +
scale_y_continuous(labels = percent) +
scale_fill_manual(values = twoCols) +
labs(x = "Sample", y = "Percent of Total")
Version | Author | Date |
---|---|---|
bc12101 | Steve Ped | 2020-01-20 |
The top 30 Overrepresented sequences were analysed using blastn
and most were found to be mitochondrial rRNA sequences.
getModule(rawFqc, "Overrep") %>%
group_by(Sequence, Possible_Source) %>%
summarise(`Found In` = n(), `Highest Percentage` = max(Percentage)) %>%
arrange(desc(`Highest Percentage`), desc(`Found In`)) %>%
ungroup() %>%
dplyr::slice(1:30) %>%
mutate(`Highest Percentage` = percent_format(0.01)(`Highest Percentage`/100)) %>%
pander(
justify = "llrr",
caption = paste(
"*Top", nrow(.),"Overrepresented sequences.",
"The number of samples they were found in is shown,",
"along with the percentage of the most 'contaminated' sample.*"
)
)
Sequence | Possible_Source | Found In | Highest Percentage |
---|---|---|---|
GTGGGTTCAGGTAATTAATTTAAAGCTACTTTCGTGTTTGGGCCTCTAGC | No Hit | 12 | 1.72% |
CTGGGGGAGCGGCCGCCCCGCGGCGCCCCCTCTCGTTCCCGTCTCCGGAG | No Hit | 10 | 1.69% |
CCGCTGTATTACTCAGGCTGCACTGCAGTGTCTATTCACAGGCGCGATCC | No Hit | 12 | 1.32% |
GGCCCGGCGCACGTCCAGAGTCGCCGCCGCACACCGCAGCGCATCCCCCC | No Hit | 9 | 1.31% |
CTCCTGAAAAGGTTGTATCCTTTGTTAAAGGGGCTGTACCCTCTTTAACT | No Hit | 11 | 1.11% |
GGTTCAGGTAATTAATTTAAAGCTACTTTCGTGTTTGGGCCTCTAGCATC | No Hit | 12 | 1.09% |
GGGGTGTACGAAGCTGAACTTTTATTCATCTCCCAGACAACCAGCTATTG | No Hit | 12 | 1.07% |
GGCCCGGCGCACGTCCAGAGTCGCCGCCGCGCACCGCAGCGCATCCCCCC | No Hit | 10 | 1.03% |
CGAGAGGCTCTAGTTGATATACTACGGCGTAAAGGGTGGTTAAGGAACAA | No Hit | 12 | 1.01% |
GGGGGAGCGGCCGCCCCGCGGCGCCCCCTCTCGTTCCCGTCTCCGGAGCG | No Hit | 9 | 0.87% |
CCTCCTTCAAGTATTGTTTCATGTTACATTTTCGTATATTCTGGGGTAGA | No Hit | 12 | 0.82% |
CCCGCTGTATTACTCAGGCTGCACTGCAGTGTCTATTCACAGGCGCGATC | No Hit | 12 | 0.79% |
GTTCAGGTAATTAATTTAAAGCTACTTTCGTGTTTGGGCCTCTAGCATCT | No Hit | 12 | 0.79% |
GGGTTCAGGTAATTAATTTAAAGCTACTTTCGTGTTTGGGCCTCTAGCAT | No Hit | 12 | 0.78% |
CGGGTCGGGTGGGTGGCCGGCATCACCGCGGACCTCGGGCGCCCTTTTGG | No Hit | 12 | 0.73% |
GGGCCTCTAGCATCTAAAAGCGTATAACAGTTAAAGGGCCGTTTGGCTTT | No Hit | 11 | 0.68% |
CAGTGGCGTGCGCCTGTAATCCAAGCTACTGGGAGGCTGAGGCTGGCGGA | No Hit | 11 | 0.64% |
CGGGTCGGGTGGGTAGCCGGCATCACCGCGGACCTCGGGCGCCCTTTTGG | No Hit | 12 | 0.57% |
CTTAGACGACCTGGTAGTCCAAGGCTCCCCCAGGAGCACCATATCGATAC | No Hit | 11 | 0.54% |
AGCTGGGGAGATCCGCGAGAAGGGCCCGGCGCACGTCCAGAGTCGCCGCC | No Hit | 11 | 0.53% |
GGCCTCTAGCATCTAAAAGCGTATAACAGTTAAAGGGCCGTTTGGCTTTA | No Hit | 10 | 0.53% |
CAGCCTATTTAACTTAGGGCCAACCCGTCTCTGTGGCAATAGAGTGGGAA | No Hit | 12 | 0.51% |
GGGTGGGTGGCCGGCATCACCGCGGACCTCGGGCGCCCTTTTGGACGTGG | No Hit | 10 | 0.50% |
GGGAGCGGCCGCCCCGCGGCGCCCCCTCTCGTTCCCGTCTCCGGAGCGCG | No Hit | 9 | 0.49% |
CTGGGAGATGAATAAAAGTTCAGCTTCGTACACCCCAAATTAAAAAATTA | No Hit | 10 | 0.48% |
GCCTATTTAACTTAGGGCCAACCCGTCTCTGTGGCAATAGAGTGGGAAGA | No Hit | 12 | 0.47% |
GGTCGGGTGGGTGGCCGGCATCACCGCGGACCTCGGGCGCCCTTTTGGAC | No Hit | 11 | 0.45% |
CCCCCGAACCCTTCCAAGCCGAACCGGAGCCGGTCGCGGCGCACCGCCGA | No Hit | 10 | 0.45% |
GTCGGGTGGGTGGCCGGCATCACCGCGGACCTCGGGCGCCCTTTTGGACG | No Hit | 10 | 0.43% |
GCCCACTACGACAACGTGTTTTGTAAATTATGATCTTTATTCTCCTGAAA | No Hit | 10 | 0.43% |
trimFqc <- list.files(
path = "data/1_trimmedData/FastQC/",
pattern = "zip",
full.names = TRUE
) %>%
FastqcDataList()
trimStats <- readTotals(rawFqc) %>%
dplyr::rename(Raw = Total_Sequences) %>%
left_join(readTotals(trimFqc), by = "Filename") %>%
dplyr::rename(Trimmed = Total_Sequences) %>%
dplyr::filter(grepl("R1", Filename)) %>%
mutate(
Discarded = 1 - Trimmed/Raw,
Retained = Trimmed / Raw
)
After adapter trimming between 4.16% and 5.08% of reads were discarded. No other improvement was noted.
Trimmed reads were:
STAR 2.7.0d
and summarised to each gene using featureCounts
. These counts were to be used for all gene-level analysisEnsDb
object, these transcripts are excluded by default from the reference transcriptome provided by Ensembl.cpm(kallisto$counts) %>%
.[c("ENSDART00000006381.8", "psen2S4Ter"),] %>%
as.data.frame() %>%
rownames_to_column("psen2") %>%
pivot_longer(cols = contains("kall"), names_to = "Sample", values_to = "CPM") %>%
mutate(
Sample = basename(Sample),
psen2 = case_when(
psen2 == "psen2S4Ter" ~ "S4Ter",
psen2 != "psen2S4Ter" ~ "WT"
)
) %>%
left_join(samples, by = c("Sample" = "sampleName")) %>%
ggplot(aes(sampleID, CPM, fill = psen2)) +
geom_bar(stat = "identity") +
scale_fill_manual(values = twoCols) +
facet_wrap(~genotype, nrow = 1, scales = "free_x")
Version | Author | Date |
---|---|---|
bc12101 | Steve Ped | 2020-01-20 |
As all samples demonstrated the expected expression patterns, no mislabelling was detected in this dataset.
gcInfo <- kallisto$counts %>%
as.data.frame() %>%
rownames_to_column("tx_id") %>%
dplyr::filter(
tx_id != "psen2S4Ter"
) %>%
as_tibble() %>%
pivot_longer(
cols = contains("kall"),
names_to = "sampleName",
values_to = "counts"
) %>%
dplyr::filter(
counts > 0
) %>%
mutate(
sampleName = basename(sampleName),
tx_id_version = tx_id,
tx_id = str_replace(tx_id, "(ENSDART[0-9]+).[0-9]+", "\\1")
) %>%
left_join(
mcols(grTrans) %>% as.data.frame()
) %>%
dplyr::select(
ends_with("id"), sampleName, counts, gc_content, length
) %>%
split(f = .$sampleName) %>%
lapply(function(x){
DataFrame(
gc = Rle(x$gc_content/100, x$counts),
logLen = Rle(log10(x$length), x$counts)
)
}
)
gcSummary <- gcInfo %>%
vapply(function(x){
c(mean(x$gc), sd(x$gc), mean(x$logLen), sd(x$logLen))
}, numeric(4)
) %>%
t() %>%
set_colnames(
c("mn_gc", "sd_gc", "mn_logLen", "sd_logLen")
) %>%
as.data.frame() %>%
rownames_to_column("sampleName") %>%
as_tibble() %>%
left_join(dplyr::filter(samples, reads == "R1")) %>%
dplyr::select(starts_with("sample"), genotype, contains("_"))
gcCors <- rawGC %>%
dplyr::filter(reads == "R1") %>%
dplyr::select(starts_with("sample"), genotype, Freq, reads) %>%
left_join(gcSummary) %>%
dplyr::select(Freq, mn_gc, mn_logLen) %>%
cor()
The initial QC steps identified an unusually large proportion of reads with >70% GC content. As such an exploration of any residual impacts this may have on the remainder of the library was performed.
A run length encoded (RLE) vector was formed for each sample taking the number of reads for each transcript as the run lengths, and both the GC content and length of each transcript as the values. Transcript lengths were transformed to the log10 scale due to the wide variety of lengths contained in the transcriptome.
From these RLEs, the mean GC and mean length was calculated for each sample, and these values were compared to the proportion of raw reads with > 70% GC, taking these values from the R1 libraries only.
Given the more successful rRNA-removal in the wild-type samples, this dataset is likely to contain significant GC and Length bias which is non-biological in origin.
a <- gcSummary %>%
left_join(rawGC) %>%
dplyr::filter(reads == "R1") %>%
ggplot(aes(Freq, mn_logLen)) +
geom_point(aes(colour = genotype), size = 3) +
geom_smooth(method = "lm") +
scale_shape_manual(values = c(19, 1)) +
labs(
x = "Proportion of initial library with > 70% GC",
y = "Mean log(length)",
colour = "Genotype"
)
b <- gcSummary %>%
left_join(rawGC) %>%
dplyr::filter(reads == "R1") %>%
ggplot(aes(Freq, mn_gc)) +
geom_point(aes(colour = genotype), size = 3) +
geom_smooth(method = "lm") +
scale_shape_manual(values = c(19, 1)) +
scale_y_continuous(labels = percent) +
labs(
x = "Proportion of initial library with > 70% GC",
y = "Mean GC Content",
colour = "Genotype"
)
plot_grid(
a + theme(legend.position = "none"),
b + theme(legend.position = "none"),
get_legend(b),
nrow = 1,
rel_widths = c(4,4,1)
)
Version | Author | Date |
---|---|---|
bc12101 | Steve Ped | 2020-01-20 |
An initial PCA was performed using transcript-level counts to assess general patterns in the data. Transcripts were included if >12 reads over all libraries were allocated to that identifiers. Correlations were checked between the first three components and mean GC content (as described above), mean log10 transcript length, genotype and the proportion of the initial libraries with GC content > 70%.
The confounding of genotype and GC variability may need careful handling in this dataset.
transDGE <- kallisto$counts %>%
set_colnames(basename(colnames(.))) %>%
divide_by(kallisto$annotation$Overdispersion) %>%
.[rowSums(.) > 12,] %>%
DGEList(
samples = tibble(
sampleName = colnames(.)
) %>%
left_join(samples) %>%
dplyr::select(sampleName, sample = sampleID, genotype) %>%
distinct(sampleName, .keep_all = TRUE)
)
pca <- cpm(transDGE, log = TRUE) %>%
t() %>%
prcomp()
pca$x %>%
as.data.frame() %>%
rownames_to_column("sampleName") %>%
left_join(gcSummary) %>%
as_tibble() %>%
left_join(
dplyr::filter(rawGC, reads == "R1")
) %>%
dplyr::select(
PC1, PC2, PC3,
Mean_GC = mn_gc,
Mean_Length = mn_logLen,
Initial_GC70 = Freq,
genotype
) %>%
mutate(genotype = as.numeric(as.factor(genotype))) %>%
cor() %>%
corrplot(
type = "lower",
diag = FALSE,
addCoef.col = 1, addCoefasPercent = TRUE
)
Version | Author | Date |
---|---|---|
bc12101 | Steve Ped | 2020-01-20 |
a <- pca$x %>%
as.data.frame() %>%
rownames_to_column("sampleName") %>%
left_join(
dplyr::filter(rawGC, reads == "R1")
) %>%
as_tibble() %>%
ggplot(aes(PC1, PC2, colour = genotype, size = Freq)) +
geom_point() +
labs(
x = paste0("PC1 (", percent(summary(pca)$importance["Proportion of Variance","PC1"]),")"),
y = paste0("PC2 (", percent(summary(pca)$importance["Proportion of Variance","PC2"]),")"),
size = "Raw GC > 70%",
colour = "Genotype"
)
b <- pca$x %>%
as.data.frame() %>%
rownames_to_column("sampleName") %>%
left_join(gcSummary) %>%
left_join(
dplyr::filter(rawGC, reads == "R1")
) %>%
as_tibble() %>%
ggplot(aes(PC1, mn_gc)) +
geom_point(aes(colour = genotype, size = Freq)) +
geom_smooth(method = "lm") +
scale_y_continuous(labels = percent) +
labs(
x = paste0("PC1 (", percent(summary(pca)$importance["Proportion of Variance","PC1"]),")"),
y = "Mean GC",
size = "Raw GC > 70%",
colour = "Genotype"
)
plot_grid(
plot_grid(
a + theme(legend.position = "none"),
b + theme(legend.position = "none")
),
get_legend(b + theme(legend.position = "bottom")),
nrow = 2,
rel_heights = c(4,1)
)
Version | Author | Date |
---|---|---|
bc12101 | Steve Ped | 2020-01-20 |
devtools::session_info()
─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 3.6.2 (2019-12-12)
os Ubuntu 18.04.3 LTS
system x86_64, linux-gnu
ui X11
language en_AU:en
collate en_AU.UTF-8
ctype en_AU.UTF-8
tz Australia/Adelaide
date 2020-01-21
─ Packages ───────────────────────────────────────────────────────────────────
package * version date lib source
AnnotationDbi * 1.48.0 2019-10-29 [2] Bioconductor
AnnotationFilter * 1.10.0 2019-10-29 [2] Bioconductor
AnnotationHub * 2.18.0 2019-10-29 [2] Bioconductor
askpass 1.1 2019-01-13 [2] CRAN (R 3.6.0)
assertthat 0.2.1 2019-03-21 [2] CRAN (R 3.6.0)
backports 1.1.5 2019-10-02 [2] CRAN (R 3.6.1)
Biobase * 2.46.0 2019-10-29 [2] Bioconductor
BiocFileCache * 1.10.2 2019-11-08 [2] Bioconductor
BiocGenerics * 0.32.0 2019-10-29 [2] Bioconductor
BiocManager 1.30.10 2019-11-16 [2] CRAN (R 3.6.1)
BiocParallel 1.20.1 2019-12-21 [2] Bioconductor
BiocVersion 3.10.1 2019-06-06 [2] Bioconductor
biomaRt 2.42.0 2019-10-29 [2] Bioconductor
Biostrings 2.54.0 2019-10-29 [2] Bioconductor
bit 1.1-15.1 2020-01-14 [2] CRAN (R 3.6.2)
bit64 0.9-7 2017-05-08 [2] CRAN (R 3.6.0)
bitops 1.0-6 2013-08-17 [2] CRAN (R 3.6.0)
blob 1.2.0 2019-07-09 [2] CRAN (R 3.6.1)
broom 0.5.3 2019-12-14 [2] CRAN (R 3.6.2)
callr 3.4.0 2019-12-09 [2] CRAN (R 3.6.2)
cellranger 1.1.0 2016-07-27 [2] CRAN (R 3.6.0)
cli 2.0.1 2020-01-08 [2] CRAN (R 3.6.2)
cluster 2.1.0 2019-06-19 [2] CRAN (R 3.6.1)
colorspace 1.4-1 2019-03-18 [2] CRAN (R 3.6.0)
corrplot * 0.84 2017-10-16 [2] CRAN (R 3.6.0)
cowplot * 1.0.0 2019-07-11 [2] CRAN (R 3.6.1)
crayon 1.3.4 2017-09-16 [2] CRAN (R 3.6.0)
curl 4.3 2019-12-02 [2] CRAN (R 3.6.2)
data.table 1.12.8 2019-12-09 [2] CRAN (R 3.6.2)
DBI 1.1.0 2019-12-15 [2] CRAN (R 3.6.2)
dbplyr * 1.4.2 2019-06-17 [2] CRAN (R 3.6.0)
DelayedArray 0.12.2 2020-01-06 [2] Bioconductor
desc 1.2.0 2018-05-01 [2] CRAN (R 3.6.0)
devtools 2.2.1 2019-09-24 [2] CRAN (R 3.6.1)
digest 0.6.23 2019-11-23 [2] CRAN (R 3.6.1)
dplyr * 0.8.3 2019-07-04 [2] CRAN (R 3.6.1)
edgeR * 3.28.0 2019-10-29 [2] Bioconductor
ellipsis 0.3.0 2019-09-20 [2] CRAN (R 3.6.1)
ensembldb * 2.10.2 2019-11-20 [2] Bioconductor
evaluate 0.14 2019-05-28 [2] CRAN (R 3.6.0)
FactoMineR 2.1 2020-01-17 [2] CRAN (R 3.6.2)
fansi 0.4.1 2020-01-08 [2] CRAN (R 3.6.2)
farver 2.0.3 2020-01-16 [2] CRAN (R 3.6.2)
fastmap 1.0.1 2019-10-08 [2] CRAN (R 3.6.1)
flashClust 1.01-2 2012-08-21 [2] CRAN (R 3.6.1)
forcats * 0.4.0 2019-02-17 [2] CRAN (R 3.6.0)
fs 1.3.1 2019-05-06 [2] CRAN (R 3.6.0)
generics 0.0.2 2018-11-29 [2] CRAN (R 3.6.0)
GenomeInfoDb * 1.22.0 2019-10-29 [2] Bioconductor
GenomeInfoDbData 1.2.2 2019-11-21 [2] Bioconductor
GenomicAlignments 1.22.1 2019-11-12 [2] Bioconductor
GenomicFeatures * 1.38.0 2019-10-29 [2] Bioconductor
GenomicRanges * 1.38.0 2019-10-29 [2] Bioconductor
ggdendro 0.1-20 2016-04-27 [2] CRAN (R 3.6.0)
ggplot2 * 3.2.1 2019-08-10 [2] CRAN (R 3.6.1)
ggrepel 0.8.1 2019-05-07 [2] CRAN (R 3.6.0)
git2r 0.26.1 2019-06-29 [2] CRAN (R 3.6.1)
glue 1.3.1 2019-03-12 [2] CRAN (R 3.6.0)
gtable 0.3.0 2019-03-25 [2] CRAN (R 3.6.0)
haven 2.2.0 2019-11-08 [2] CRAN (R 3.6.1)
highr 0.8 2019-03-20 [2] CRAN (R 3.6.0)
hms 0.5.3 2020-01-08 [2] CRAN (R 3.6.2)
htmltools 0.4.0 2019-10-04 [2] CRAN (R 3.6.1)
htmlwidgets 1.5.1 2019-10-08 [2] CRAN (R 3.6.1)
httpuv 1.5.2 2019-09-11 [2] CRAN (R 3.6.1)
httr 1.4.1 2019-08-05 [2] CRAN (R 3.6.1)
hwriter 1.3.2 2014-09-10 [2] CRAN (R 3.6.0)
interactiveDisplayBase 1.24.0 2019-10-29 [2] Bioconductor
IRanges * 2.20.2 2020-01-13 [2] Bioconductor
jpeg 0.1-8.1 2019-10-24 [2] CRAN (R 3.6.1)
jsonlite 1.6 2018-12-07 [2] CRAN (R 3.6.0)
kableExtra 1.1.0 2019-03-16 [2] CRAN (R 3.6.1)
knitr 1.27 2020-01-16 [2] CRAN (R 3.6.2)
labeling 0.3 2014-08-23 [2] CRAN (R 3.6.0)
later 1.0.0 2019-10-04 [2] CRAN (R 3.6.1)
lattice 0.20-38 2018-11-04 [4] CRAN (R 3.6.0)
latticeExtra 0.6-29 2019-12-19 [2] CRAN (R 3.6.2)
lazyeval 0.2.2 2019-03-15 [2] CRAN (R 3.6.0)
leaps 3.1 2020-01-16 [2] CRAN (R 3.6.2)
lifecycle 0.1.0 2019-08-01 [2] CRAN (R 3.6.1)
limma * 3.42.0 2019-10-29 [2] Bioconductor
locfit 1.5-9.1 2013-04-20 [2] CRAN (R 3.6.0)
lubridate 1.7.4 2018-04-11 [2] CRAN (R 3.6.0)
magrittr * 1.5 2014-11-22 [2] CRAN (R 3.6.0)
MASS 7.3-51.5 2019-12-20 [4] CRAN (R 3.6.2)
Matrix 1.2-18 2019-11-27 [2] CRAN (R 3.6.1)
matrixStats 0.55.0 2019-09-07 [2] CRAN (R 3.6.1)
memoise 1.1.0 2017-04-21 [2] CRAN (R 3.6.0)
mime 0.8 2019-12-19 [2] CRAN (R 3.6.2)
modelr 0.1.5 2019-08-08 [2] CRAN (R 3.6.1)
munsell 0.5.0 2018-06-12 [2] CRAN (R 3.6.0)
ngsReports * 1.1.2 2019-10-16 [1] Bioconductor
nlme 3.1-143 2019-12-10 [4] CRAN (R 3.6.2)
openssl 1.4.1 2019-07-18 [2] CRAN (R 3.6.1)
pander * 0.6.3 2018-11-06 [2] CRAN (R 3.6.0)
pillar 1.4.3 2019-12-20 [2] CRAN (R 3.6.2)
pkgbuild 1.0.6 2019-10-09 [2] CRAN (R 3.6.1)
pkgconfig 2.0.3 2019-09-22 [2] CRAN (R 3.6.1)
pkgload 1.0.2 2018-10-29 [2] CRAN (R 3.6.0)
plotly 4.9.1 2019-11-07 [2] CRAN (R 3.6.1)
plyr 1.8.5 2019-12-10 [2] CRAN (R 3.6.2)
png 0.1-7 2013-12-03 [2] CRAN (R 3.6.0)
prettyunits 1.1.0 2020-01-09 [2] CRAN (R 3.6.2)
processx 3.4.1 2019-07-18 [2] CRAN (R 3.6.1)
progress 1.2.2 2019-05-16 [2] CRAN (R 3.6.0)
promises 1.1.0 2019-10-04 [2] CRAN (R 3.6.1)
ProtGenerics 1.18.0 2019-10-29 [2] Bioconductor
ps 1.3.0 2018-12-21 [2] CRAN (R 3.6.0)
purrr * 0.3.3 2019-10-18 [2] CRAN (R 3.6.1)
R6 2.4.1 2019-11-12 [2] CRAN (R 3.6.1)
rappdirs 0.3.1 2016-03-28 [2] CRAN (R 3.6.0)
RColorBrewer 1.1-2 2014-12-07 [2] CRAN (R 3.6.0)
Rcpp 1.0.3 2019-11-08 [2] CRAN (R 3.6.1)
RCurl 1.95-4.13 2020-01-16 [2] CRAN (R 3.6.2)
readr * 1.3.1 2018-12-21 [2] CRAN (R 3.6.0)
readxl 1.3.1 2019-03-13 [2] CRAN (R 3.6.0)
remotes 2.1.0 2019-06-24 [2] CRAN (R 3.6.0)
reprex 0.3.0 2019-05-16 [2] CRAN (R 3.6.0)
reshape2 1.4.3 2017-12-11 [2] CRAN (R 3.6.0)
rhdf5 2.30.1 2019-11-26 [2] Bioconductor
Rhdf5lib 1.8.0 2019-10-29 [2] Bioconductor
rlang 0.4.2 2019-11-23 [2] CRAN (R 3.6.1)
rmarkdown 2.0 2019-12-12 [2] CRAN (R 3.6.2)
rprojroot 1.3-2 2018-01-03 [2] CRAN (R 3.6.0)
Rsamtools 2.2.1 2019-11-06 [2] Bioconductor
RSQLite 2.2.0 2020-01-07 [2] CRAN (R 3.6.2)
rstudioapi 0.10 2019-03-19 [2] CRAN (R 3.6.0)
rtracklayer 1.46.0 2019-10-29 [2] Bioconductor
rvest 0.3.5 2019-11-08 [2] CRAN (R 3.6.1)
S4Vectors * 0.24.2 2020-01-13 [2] Bioconductor
scales * 1.1.0 2019-11-18 [2] CRAN (R 3.6.1)
scatterplot3d 0.3-41 2018-03-14 [2] CRAN (R 3.6.1)
sessioninfo 1.1.1 2018-11-05 [2] CRAN (R 3.6.0)
shiny 1.4.0 2019-10-10 [2] CRAN (R 3.6.1)
ShortRead 1.44.1 2019-12-19 [2] Bioconductor
stringi 1.4.5 2020-01-11 [2] CRAN (R 3.6.2)
stringr * 1.4.0 2019-02-10 [2] CRAN (R 3.6.0)
SummarizedExperiment 1.16.1 2019-12-19 [2] Bioconductor
testthat 2.3.1 2019-12-01 [2] CRAN (R 3.6.1)
tibble * 2.1.3 2019-06-06 [2] CRAN (R 3.6.0)
tidyr * 1.0.0 2019-09-11 [2] CRAN (R 3.6.1)
tidyselect 0.2.5 2018-10-11 [2] CRAN (R 3.6.0)
tidyverse * 1.3.0 2019-11-21 [2] CRAN (R 3.6.1)
truncnorm 1.0-8 2018-02-27 [2] CRAN (R 3.6.0)
usethis 1.5.1 2019-07-04 [2] CRAN (R 3.6.1)
vctrs 0.2.1 2019-12-17 [2] CRAN (R 3.6.2)
viridisLite 0.3.0 2018-02-01 [2] CRAN (R 3.6.0)
webshot 0.5.2 2019-11-22 [2] CRAN (R 3.6.1)
whisker 0.4 2019-08-28 [2] CRAN (R 3.6.1)
withr 2.1.2 2018-03-15 [2] CRAN (R 3.6.0)
workflowr * 1.6.0 2019-12-19 [2] CRAN (R 3.6.2)
xfun 0.12 2020-01-13 [2] CRAN (R 3.6.2)
XML 3.99-0.2 2020-01-18 [2] CRAN (R 3.6.2)
xml2 1.2.2 2019-08-09 [2] CRAN (R 3.6.1)
xtable 1.8-4 2019-04-21 [2] CRAN (R 3.6.0)
XVector 0.26.0 2019-10-29 [2] Bioconductor
yaml 2.2.0 2018-07-25 [2] CRAN (R 3.6.0)
zeallot 0.1.0 2018-01-28 [2] CRAN (R 3.6.0)
zlibbioc 1.32.0 2019-10-29 [2] Bioconductor
zoo 1.8-7 2020-01-10 [2] CRAN (R 3.6.2)
[1] /home/steveped/R/x86_64-pc-linux-gnu-library/3.6
[2] /usr/local/lib/R/site-library
[3] /usr/lib/R/site-library
[4] /usr/lib/R/library