PhIPData: A Container for PhIP-Seq Experiments
October 26, 2021
Contents
1 Installation
We recommend installing the stable release version of PhIPData in Bioconductor.
This can be done using BiocManager:
if (!require("BiocManager"))
install.packages("BiocManager")
BiocManager::install("PhIPData")To load the package:
library(PhIPData)2 Introduction
The PhIPData class is used to store experimental results from phage-immunoprecipitation sequencing (PhIP-set) experiments in a matrix-like container.
Building on the RangedSummarizedExperiment class, PhIPData contains all of functionality of SummarizedExperiments and includes additional operations to facilitate analysis with PhIP-seq data. Like SummarizedExperiments, a key feature of PhIPData is the coordination of metadata when subsetting PhIPData objects. For example, if you wanted to examine experimental data for peptides from one particular virus, you can subset the experimental data and the associated peptide annotation with one command. This ensures all metadata (for samples, peptides, etc.) remain synced with the experimental data throughout analysis.
3 Components of a PhIPData Object
As reflected in the figure below, the structure of a PhIPData object is nearly identical to the structure of a SummarizedExperiment/RangedSummarizedExperiment object.
Each object contains at least three assays of data. These assays are:
counts: matrix of raw read counts,logfc: matrix of log2 estimated fold-changes (in comparison to negative control samples),prob: matrix of probabilities (p-values or posterior probabilities) associated with whether a sample shows an enriched antibody response to the particular peptide.
Though counts typically contain integer values for the number of reads aligned to each peptide, PhIPData only requires that stored values are non-negative numeric values. Pseudocounts or non-integer count values can also be stored in the counts assay.
The rows of a PhIPData object represent peptides of interest and the columns represent samples. Sample and peptide metadata are stored in DataFrames. Each row of the metadata DataFrame specifies the peptide/sample, and the columns represent different features associated with the peptides/samples.
In addition to sample- and peptide-specific metadata, experimental metadata such as associated papers, experimental parameters, sequencing dates, etc. are stored in a list-like component named metadata.
Figure 1: Schematic of a PhIPData object
Commands used to access each component of the object are listed underneath its visual representation. Code in black indicates functions specific to PhIPData objects while functions in red extend SummarizedExperiment functions. Here, pd is a generic PhIPData object.
4 Creating a PhIPData object
library(dplyr)
library(readr)To demonstrate the PhIPData class and functions, we will use a simulated example data set. Suppose we have PhIP-seq data from 5 individuals for 1288 peptides derived from known human viruses.
set.seed(20210120)
# Read in peptide metadata -------------
virscan_file <- system.file("extdata", "virscan.tsv", package = "PhIPData")
virscan_info <- readr::read_tsv(virscan_file,
col_types = readr::cols(
pep_id = readr::col_character(),
pro_id = readr::col_character(),
pos_start = readr::col_double(),
pos_end = readr::col_double(),
UniProt_acc = readr::col_character(),
pep_dna = readr::col_character(),
pep_aa = readr::col_character(),
pro_len = readr::col_double(),
taxon_id = readr::col_double(),
species = readr::col_character(),
genus = readr::col_character(),
product = readr::col_character()
)) %>%
as.data.frame()## Warning: The following named parsers don't match the column names: pro_id,
## pos_start, pos_end, UniProt_acc, taxon_id, genus# Simulate data -------------
n_samples <- 5L
n_peps <- nrow(virscan_info)
counts_dat <- matrix(sample(1:1e6, n_samples*n_peps, replace = TRUE),
nrow = n_peps)
logfc_dat <- matrix(rnorm(n_samples*n_peps, mean = 0, sd = 10),
nrow = n_peps)
prob_dat <- matrix(rbeta(n_samples*n_peps, shape1 = 1, shape2 = 1),
nrow = n_peps)
# Sample metadata -------------
sample_meta <- data.frame(sample_name = paste0("sample", 1:n_samples),
gender = sample(c("M", "F"), n_samples, TRUE),
group = sample(c("ctrl", "trt", "beads"), n_samples, TRUE))
# Set row/column names -------------
rownames(counts_dat) <- rownames(logfc_dat) <-
rownames(prob_dat) <- rownames(virscan_info) <-
paste0("pep_", 1:n_peps)
colnames(counts_dat) <- colnames(logfc_dat) <-
colnames(prob_dat) <- rownames(sample_meta) <-
paste0("sample_", 1:n_samples)
# Experimental metadata -------------
exp_meta <- list(date_run = as.Date("2021/01/20"),
reads_per_sample = colSums(counts_dat))To create a PhIPData object, we will use the homonymous constructor PhIPData().
phip_obj <- PhIPData(counts_dat, logfc_dat, prob_dat,
virscan_info, sample_meta,
exp_meta)## ! Missing peptide start and end position information. Replacing missing values with 0.phip_obj## class: PhIPData
## dim: 1220 5
## metadata(2): date_run reads_per_sample
## assays(3): counts logfc prob
## rownames(1220): pep_1 pep_2 ... pep_1219 pep_1220
## rowData names(17): pep_id pep_dna ... interpro pep_name
## colnames(5): sample_1 sample_2 sample_3 sample_4 sample_5
## colData names(3): sample_name gender group
## beads-only name(1): beadsThe PhIPData() constructor is quite flexible; mismatched dimension names across assays and metadata are automatically corrected, and missing assays are initialized with empty matrices of the same dimensions. For more details on the constructor, type help(PhIPData).
5 Accessing and modifying components of PhIPData object
5.1 Assays
Assays store matrix-like data. For PhIPData objects, the assays counts, logfc, and prob are required. If any of these matrices were missing from the constructor, they are initialized with empty matrices of the same dimensions. Experimental data can be accessed via assays(phip_obj) or assay(phip_obj, i) command. assays(phip_obj) returns a list of all assays in the object, and list items can be accessed using the $ or [[ operators.
assays(phip_obj)## List of length 3
## names(3): counts logfc probhead(assays(phip_obj)$counts) # Returns the same as assays(phip_obj)[["counts"]]## sample_1 sample_2 sample_3 sample_4 sample_5
## pep_1 324495 667018 270181 294611 849922
## pep_2 331161 205615 676756 227825 556830
## pep_3 31804 884918 731625 845356 717778
## pep_4 286942 183405 887186 200386 944374
## pep_5 635124 19084 652887 852165 116878
## pep_6 69802 419044 693736 177505 332696While assays(phip_obj) returns a list of all assays in the PhIPData object, assay(phip_obj, i) returns a matrix of the specified assay. If i is missing, assay(phip_obj) defaults to the first assay (counts). i can be a character specifying the assay name or a numeric index of the assay.
head(assay(phip_obj, "logfc")) # Returns the same as assay(phip_obj, 2)## sample_1 sample_2 sample_3 sample_4 sample_5
## pep_1 0.4612149 1.4808873 8.453510 0.3323388 11.594683
## pep_2 -3.6724663 -6.2495714 5.059587 13.2961251 -9.111073
## pep_3 0.5431797 20.7380494 -12.923548 -15.2766681 11.129857
## pep_4 -0.9168914 -0.1049908 1.765798 0.4424645 21.279677
## pep_5 26.9799217 13.0302587 8.535700 10.7396498 4.032496
## pep_6 7.1479608 7.9733597 -12.267401 -0.7219982 -6.348123Since all PhIPData objects must contain the counts, logfc, and prob assays, we have defined three homonyous function to conveniently access and modify these assays.
head(counts(phip_obj))## sample_1 sample_2 sample_3 sample_4 sample_5
## pep_1 324495 667018 270181 294611 849922
## pep_2 331161 205615 676756 227825 556830
## pep_3 31804 884918 731625 845356 717778
## pep_4 286942 183405 887186 200386 944374
## pep_5 635124 19084 652887 852165 116878
## pep_6 69802 419044 693736 177505 332696head(logfc(phip_obj))## sample_1 sample_2 sample_3 sample_4 sample_5
## pep_1 0.4612149 1.4808873 8.453510 0.3323388 11.594683
## pep_2 -3.6724663 -6.2495714 5.059587 13.2961251 -9.111073
## pep_3 0.5431797 20.7380494 -12.923548 -15.2766681 11.129857
## pep_4 -0.9168914 -0.1049908 1.765798 0.4424645 21.279677
## pep_5 26.9799217 13.0302587 8.535700 10.7396498 4.032496
## pep_6 7.1479608 7.9733597 -12.267401 -0.7219982 -6.348123head(prob(phip_obj))## sample_1 sample_2 sample_3 sample_4 sample_5
## pep_1 0.1897952 0.7967117 0.556191953 0.68764287 0.35711378
## pep_2 0.5737116 0.4370929 0.404926804 0.07160597 0.79505023
## pep_3 0.8623420 0.5592806 0.003571328 0.70038398 0.86558768
## pep_4 0.6597397 0.6444327 0.456416743 0.69862702 0.15818377
## pep_5 0.9306944 0.6873269 0.294782634 0.20429868 0.06582967
## pep_6 0.4041099 0.2820968 0.933655137 0.11306558 0.77142544After a PhIPData object has been created, data for new and existing assays can be set using <-. Dimension names of the replacement assays are automatically corrected to be identical to the object names. As we expect assays to contain homogenous data, replacement assays are coerced into matrices. Replacement assays must also be on the same dimension of the existing object.
replacement_dat <- matrix(1, nrow = n_peps, ncol = n_samples)
# Replace the counts matrix -------------
head(counts(phip_obj))## sample_1 sample_2 sample_3 sample_4 sample_5
## pep_1 324495 667018 270181 294611 849922
## pep_2 331161 205615 676756 227825 556830
## pep_3 31804 884918 731625 845356 717778
## pep_4 286942 183405 887186 200386 944374
## pep_5 635124 19084 652887 852165 116878
## pep_6 69802 419044 693736 177505 332696counts(phip_obj) <- replacement_dat
head(counts(phip_obj))## sample_1 sample_2 sample_3 sample_4 sample_5
## pep_1 1 1 1 1 1
## pep_2 1 1 1 1 1
## pep_3 1 1 1 1 1
## pep_4 1 1 1 1 1
## pep_5 1 1 1 1 1
## pep_6 1 1 1 1 1# Add a new assay -----------
head(assay(phip_obj, "new_assay"))## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'head': 'assay(<PhIPData>, i="character", ...)' invalid subscript 'i'
## 'new_assay' not in names(assays(<PhIPData>))assay(phip_obj, "new_assay") <- replacement_dat
head(assay(phip_obj, "new_assay"))## sample_1 sample_2 sample_3 sample_4 sample_5
## pep_1 1 1 1 1 1
## pep_2 1 1 1 1 1
## pep_3 1 1 1 1 1
## pep_4 1 1 1 1 1
## pep_5 1 1 1 1 1
## pep_6 1 1 1 1 1# Returns and error because `counts`, `logfc`, and `prob` must be in the
# assays of a PhIPData object
assays(phip_obj) <- list(new_assay1 = replacement_dat,
new_assay2 = replacement_dat)## Error in `assays<-`(`*tmp*`, value = list(new_assay1 = structure(c(1, : `counts`, `logfc`, and `prob` assays must be included in a PhIPData object. The following assays are missing: counts, logfc, prob.5.2 Peptide metadata
Information associated with peptides can be accessed using peptideInfo(phip_obj) or the inherited rowRanges(phip_obj) function. Both functions return a GRanges object. GRanges objects behave similar to matrices and can be subsetted using the usual 2-dimensional methods. More information about GRanges objects can be found here.
Information about peptide positions in the protein sequence are stored as IRanges in the GRanges object. These are specified by pos_start and pos_end columns in the peptide metadata in the constructor. If these columns do not exist, the start and end positions are set to 0 by default.
# Only showing 2 columns for easier viewing
peptideInfo(phip_obj)[, 8:9]## GRanges object with 1220 ranges and 2 metadata columns:
## seqnames ranges strand | species
## <Rle> <IRanges> <Rle> | <character>
## pep_1 pep_1 0 * | Alphacoronavirus Fel..
## pep_2 pep_2 0 * | Alphacoronavirus Fel..
## pep_3 pep_3 0 * | Alphacoronavirus Fel..
## pep_4 pep_4 0 * | Alphacoronavirus Hum..
## pep_5 pep_5 0 * | Alphacoronavirus Hum..
## ... ... ... ... . ...
## pep_1216 pep_1216 0 * | Yellow fever virus g..
## pep_1217 pep_1217 0 * | Yellow fever virus g..
## pep_1218 pep_1218 0 * | Yellow fever virus g..
## pep_1219 pep_1219 0 * | Yellow fever virus g..
## pep_1220 pep_1220 0 * | Yellow fever virus g..
## interspecies_specific
## <character>
## pep_1 none
## pep_2 Bat_coronavirus_1B;B..
## pep_3 Human_coronavirus_HK..
## pep_4 none
## pep_5 Human_coronavirus_NL..
## ... ...
## pep_1216 Japanese_encephaliti..
## pep_1217 none
## pep_1218 none
## pep_1219 Murray_Valley_enceph..
## pep_1220 none
## -------
## seqinfo: 1220 sequences from an unspecified genome; no seqlengths5.3 Sample metadata
Sample metadata describing the samples can be extracted from the PhIPData object using sampleInfo(phip_obj) or the inherited function colData(phip_obj). Both functions return a DataFrame where each row corresponds to a sample and each column corresponds to some sample description.
sampleInfo(phip_obj)## DataFrame with 5 rows and 3 columns
## sample_name gender group
## <character> <character> <character>
## sample_1 sample1 M ctrl
## sample_2 sample2 F beads
## sample_3 sample3 F trt
## sample_4 sample4 F trt
## sample_5 sample5 M trtLike in SummarizedExperiments/RangedSummarizedExperiments, sample metadata can be accessed using the $ operator from the object. As demonstrated in the Subsetting section, this makes subetting data for a subgroup of samples very easy.
phip_obj$group## [1] "ctrl" "beads" "trt" "trt" "trt"5.4 Experimental metadata
Data associated with the experiment such as papers, date the samples were run, etc. can be accessed via the metadata(phip_obj) function. Experimental metadata is stored as a list, so it can be used to store any type of data.
metadata(phip_obj)## $date_run
## [1] "2021-01-20"
##
## $reads_per_sample
## sample_1 sample_2 sample_3 sample_4 sample_5
## 593423181 605066838 616075341 614115405 6135821696 Common operations on PhIPData objects
6.1 Subsetting
Like subsetting matrices and dataframes, [ can be used for two-dimensional subsetting of PhIPData objects.
phip_obj[1:10, 1:2]## class: PhIPData
## dim: 10 2
## metadata(2): date_run reads_per_sample
## assays(4): counts logfc prob new_assay
## rownames(10): pep_1 pep_2 ... pep_9 pep_10
## rowData names(17): pep_id pep_dna ... interpro pep_name
## colnames(2): sample_1 sample_2
## colData names(3): sample_name gender group
## beads-only name(1): beadsAs described in the Sample metadata section, $ operates on the sample metadata column names, so we can also use $ to select samples of a particular subgroups.
phip_obj[, phip_obj$group == "beads"]## class: PhIPData
## dim: 1220 1
## metadata(2): date_run reads_per_sample
## assays(4): counts logfc prob new_assay
## rownames(1220): pep_1 pep_2 ... pep_1219 pep_1220
## rowData names(17): pep_id pep_dna ... interpro pep_name
## colnames(1): sample_2
## colData names(3): sample_name gender group
## beads-only name(1): beadsIn addition to subsetting by row indices, PhIPData supports subsetting rows using peptide metadata information. subset(phip_obj, row_condition, column_condition) returns a PhIPData object with rows where the specified condition holds.
ebv_sub <- subset(phip_obj, grepl("Epstein-Barr virus", species))
peptideInfo(ebv_sub)[, "species"]## GRanges object with 23 ranges and 1 metadata column:
## seqnames ranges strand | species
## <Rle> <IRanges> <Rle> | <character>
## pep_516 pep_516 0 * | Lymphocryptovirus Ep..
## pep_517 pep_517 0 * | Lymphocryptovirus Ep..
## pep_518 pep_518 0 * | Lymphocryptovirus Ep..
## pep_519 pep_519 0 * | Lymphocryptovirus Ep..
## pep_520 pep_520 0 * | Lymphocryptovirus Ep..
## ... ... ... ... . ...
## pep_534 pep_534 0 * | Lymphocryptovirus Ep..
## pep_535 pep_535 0 * | Lymphocryptovirus Ep..
## pep_536 pep_536 0 * | Lymphocryptovirus Ep..
## pep_537 pep_537 0 * | Lymphocryptovirus Ep..
## pep_538 pep_538 0 * | Lymphocryptovirus Ep..
## -------
## seqinfo: 1220 sequences from an unspecified genome; no seqlengthsTo subset all beads-only samples from a PhIPData object, we can use the convenient wrapper function subsetBeads():
subsetBeads(phip_obj)## class: PhIPData
## dim: 1220 1
## metadata(2): date_run reads_per_sample
## assays(4): counts logfc prob new_assay
## rownames(1220): pep_1 pep_2 ... pep_1219 pep_1220
## rowData names(17): pep_id pep_dna ... interpro pep_name
## colnames(1): sample_2
## colData names(3): sample_name gender group
## beads-only name(1): beads6.2 PhIPData summaries
To assess the quality of the data, we are often interested in the number of reads per sample. This can be done using the librarySize() function. Names can be removed by setting the withDimnames parameter to FALSE.
librarySize(phip_obj)## sample_1 sample_2 sample_3 sample_4 sample_5
## 1220 1220 1220 1220 1220The proportion of sample reads pulled by each peptide can also be obtained via propReads(). Like librarySize(), names can also be removed by setting withDimnames to FALSE.
head(propReads(phip_obj))## sample_1 sample_2 sample_3 sample_4 sample_5
## pep_1 0.0008196721 0.0008196721 0.0008196721 0.0008196721 0.0008196721
## pep_2 0.0008196721 0.0008196721 0.0008196721 0.0008196721 0.0008196721
## pep_3 0.0008196721 0.0008196721 0.0008196721 0.0008196721 0.0008196721
## pep_4 0.0008196721 0.0008196721 0.0008196721 0.0008196721 0.0008196721
## pep_5 0.0008196721 0.0008196721 0.0008196721 0.0008196721 0.0008196721
## pep_6 0.0008196721 0.0008196721 0.0008196721 0.0008196721 0.00081967216.3 Using template libraries
Rather than re-importing peptide annotations, PhIPData allows the user to create and reuse existing libraries. By default, libraries are stored in the libraries folder of the PhIPData package. The library path can be set using setLibraryPath(new_path). Once set, the new path is stored in the PHIP_LIBRARY_PATH R environment variable. The current library path can be viewed with getLibraryPath().
getLibraryPath()## [1] "/tmp/RtmpEc2r2Y/Rinst1982d66e53c917/PhIPData/libraries"To save a library for future use, we can use makeLibrary(object, name_of_library). The peptide metadata should be in a matrix-like form such as a DataFrame or data.frame.
# Save virscan_info as the human_virus library
makeLibrary(virscan_info, "human_virus")The stored library can be accessed using getLibrary(library_name). We can then use the stored library to construct a new PhIPData object as follows.
PhIPData(counts = counts_dat,
sampleInfo = sample_meta,
peptideInfo = getLibrary("human_virus"))## ! Missing peptide start and end position information. Replacing missing values with 0.## class: PhIPData
## dim: 1220 5
## metadata(0):
## assays(3): counts logfc prob
## rownames(1220): pep_1 pep_2 ... pep_1219 pep_1220
## rowData names(17): pep_id pep_dna ... interpro pep_name
## colnames(5): sample_1 sample_2 sample_3 sample_4 sample_5
## colData names(3): sample_name gender group
## beads-only name(1): beads6.4 Using aliases
Peptides are often derived from species with long virus names. To quickly search up all viruses with “[Hh]uman immunodeficiency virus” in the species, you can create an alias of “HIV” to encode for the corresponding regex of interest. Alias’s can be managed with:
getAlias(key): return the regex for the aliaskey.setAlias(key, pattern): create or modify the alias forkey. If the key-pattern combination already exists in the database, then no changes are made. Otherwise, the pattern is replaced withpattern.deleteAlias(key): remove an key-pattern combination from the alias database.
# Create alias for HIV ----------
setAlias("hiv", "[Hh]uman immunodeficiency virus")
# Use alias ----------
hiv_sub <- subset(phip_obj, grepl(getAlias("hiv"), species))
peptideInfo(hiv_sub)[, "species"]## GRanges object with 42 ranges and 1 metadata column:
## seqnames ranges strand | species
## <Rle> <IRanges> <Rle> | <character>
## pep_855 pep_855 0 * | Primate lentivirus g..
## pep_856 pep_856 0 * | Primate lentivirus g..
## pep_857 pep_857 0 * | Primate lentivirus g..
## pep_858 pep_858 0 * | Primate lentivirus g..
## pep_859 pep_859 0 * | Primate lentivirus g..
## ... ... ... ... . ...
## pep_892 pep_892 0 * | Primate lentivirus g..
## pep_893 pep_893 0 * | Primate lentivirus g..
## pep_894 pep_894 0 * | Primate lentivirus g..
## pep_895 pep_895 0 * | Primate lentivirus g..
## pep_896 pep_896 0 * | Primate lentivirus g..
## -------
## seqinfo: 1220 sequences from an unspecified genome; no seqlengths# Remove alias from database -----------
deleteAlias("hiv")
# The following command returns an error that the virus does
# not exist in the alias database.
subset(phip_obj, grepl(getAlias("hiv"), species))## class: PhIPData
## dim: 0 5
## metadata(2): date_run reads_per_sample
## assays(4): counts logfc prob new_assay
## rownames(0):
## rowData names(17): pep_id pep_dna ... interpro pep_name
## colnames(5): sample_1 sample_2 sample_3 sample_4 sample_5
## colData names(3): sample_name gender group
## beads-only name(1): beadsAlias-pattern combinations are case senstive, so an entry with key “hiv” would differ from an entry with key “HIV.” Like libraries, by default the location of the alias database is in the extdata folder of the PhIPData package. The location to an .rda file with the key-pattern values in a dataframe called alias can be retreived and specified using the getAliasPath() and setAliasPath() functions, respectively.
6.5 Coercion from PhIPData to other containers
As packages for identifying differential experession are also commonly used in analyzing PhIP-seq data, the PhIPData package supports coercion from PhIPData objects to Lists, lists, DataFrame, and DGELists. The function as(phip_obj, "object_type") converts a PhIPData object to a object of object type.
For DGELists, the group slot is automatically populated with the group column in the sample metadata, if such a column exists.
# PhIPData to DGEList
as(phip_obj, "DGEList")## An object of class "DGEList"
## $counts
## sample_1 sample_2 sample_3 sample_4 sample_5
## pep_1 1 1 1 1 1
## pep_2 1 1 1 1 1
## pep_3 1 1 1 1 1
## pep_4 1 1 1 1 1
## pep_5 1 1 1 1 1
## 1215 more rows ...
##
## $samples
## group lib.size norm.factors sample_name gender
## sample_1 ctrl 1220 1 sample1 M
## sample_2 beads 1220 1 sample2 F
## sample_3 trt 1220 1 sample3 F
## sample_4 trt 1220 1 sample4 F
## sample_5 trt 1220 1 sample5 M
##
## $genes
## seqnames start end width strand pep_id
## pep_1 pep_1 0 0 1 * pep_88674
## pep_2 pep_2 0 0 1 * pep_88822
## pep_3 pep_3 0 0 1 * pep_88848
## pep_4 pep_4 0 0 1 * pep_38968
## pep_5 pep_5 0 0 1 * pep_39072
## pep_dna
## pep_1 GATTCCGGGAAGAAGGGTTTCCTTGACACCTTTAACCATCTGAACGAGCTTGAAGATGTCAAAGACATCAAAATTCAGACCATTAAGAACATTATCTGCCCAGATCTGTTGTTGGAGTTGGACTTCGGCGCTATCTGGTACCGCTGCATGCCTGCCTGCTCAGACAAA
## pep_2 TCCAAATGTTGGGTGGAACCAGACCTCTCTGTGGGTCCTCATGAGTTCTGTTCTCAACATACTCTGCAGATCGTGGGTCCAGATGGGGATTATTACCTTCCATACCCAGACCCATCCCGCATTTTGTCCGCTGGTGTCTTCGTCGATGATATCGTCAAGACTGACAAT
## pep_3 GATAGCAAAATTGGCCTTCAAGCCAAGCCTGAGACATGCGGGCTTTTCAAAGACTGCTCCAAAAGTGAACAGTACATCCCACCTGCCTATGCAACAACTTACATGTCTCTCAGTGATAACTTCAAGACTTCAGACGGGCTGGCTGTCAATATCGGCACAAAGGATGTT
## pep_4 GCCAACGGTGTTAAGGCTAAAGGGTATCCTCAATTCGCAGAACTGGTCCCTAGCACAGCCGCTATGCTGTTCGATAGCCATATCGTTAGCAAGGAGTCCGGGAATACCGTCGTGTTGACATTCACTACACGCGTTACTGTTCCTAAAGATCATCCTCACCTTGGTAAG
## pep_5 CCTGAGGTGAATGCAATTACTGTGACAACAGTTTTGGGCCAAACTTATTATCAGCCTATCCAACAAGCTCCTACAGGCATCACTGTCACATTGCTCTCAGGTGTGTTGTACGTTGACGGGCATCGCCTGGCCTCAGGGGTGCAAGTCCATAACCTGCCTGAGTACATG
## pep_aa pep_pos
## pep_1 DSGKKGFLDTFNHLNELEDVKDIKIQTIKNIICPDLLLELDFGAIWYRCMPACSDK 673_728
## pep_2 SKCWVEPDLSVGPHEFCSQHTLQIVGPDGDYYLPYPDPSRILSAGVFVDDIVKTDN 4817_4872
## pep_3 DSKIGLQAKPETCGLFKDCSKSEQYIPPAYATTYMSLSDNFKTSDGLAVNIGTKDV 5545_5600
## pep_4 ANGVKAKGYPQFAELVPSTAAMLFDSHIVSKESGNTVVLTFTTRVTVPKDHPHLGK 281_336
## pep_5 PEVNAITVTTVLGQTYYQPIQQAPTGITVTLLSGVLYVDGHRLASGVQVHNLPEYM 113_168
## pro_len uniprot_acc refseq species
## pep_1 6709 Q98VG9 <NA> Alphacoronavirus Feline coronavirus
## pep_2 6709 Q98VG9 <NA> Alphacoronavirus Feline coronavirus
## pep_3 6709 Q98VG9 <NA> Alphacoronavirus Feline coronavirus
## pep_4 389 P15130 NP_073556.1 Alphacoronavirus Human coronavirus 229E
## pep_5 225 P15422 NP_073555.1 Alphacoronavirus Human coronavirus 229E
## interspecies_specific
## pep_1 none
## pep_2 Bat_coronavirus_1B;B1PHI6;3908;GPHEFCS;Human_coronavirus_HKU1;P0C6X2;28681;GPHEFCS;Severe_acute_respiratory_syndrome-related_coronavirus;P0C6V9;28185;GPHEFCS;Human_coronavirus_229E;P0C6X1;28426;GPHEFCS;Betacoronavirus_1;A8R4C0;2781;GPHEFCS;Human_coronavirus_NL63;P0C6X5;29431;GPHEFCS
## pep_3 Human_coronavirus_HKU1;P0C6X2;28707;LFKDCSK;Bat_coronavirus_1B;B1PHI6;3935;CGLFKDC;Severe_acute_respiratory_syndrome-related_coronavirus;P0C6V9;28211;GLFKDCS;Human_coronavirus_229E;P0C6X1;28452;CGLFKDC;Betacoronavirus_1;A8R4C0;2807;LFKDCSK
## pep_4 none
## pep_5 Human_coronavirus_NL63;Q6Q1R9;61392;TLLSGVL
## product
## pep_1 Replicase_polyprotein_1ab_(pp1ab)_(ORF1ab_polyprotein)_[Cleaved_into:_Non-structural_protein_1_(nsp1)__Non-structural_protein_2_(nsp2)__Non-structural_protein_3_(nsp3)_(EC_3.4.19.12)_(EC_3.4.22.-)_(PL1-PRO_PL2-PRO)_(PLP1_PLP2)_(Papain-like_proteinases_1_2)_(p195)__Non-structural_protein_4_(nsp4)_(Peptide_HD2)__3C-like_proteinase_(3CL-PRO)_(3CLp)_(EC_3.4.22.-)_(M-PRO)_(nsp5)__Non-structural_protein_6_(nsp6)__Non-structural_protein_7_(nsp7)__Non-structural_protein_8_(nsp8)__Non-structural_protein_9_(nsp9)__Non-structural_protein_10_(nsp10)__Non-structural_protein_11_(nsp11)__RNA-directed_RNA_polymerase_(Pol)_(RdRp)_(EC_2.7.7.48)_(nsp12)__Helicase_(Hel)_(EC_3.6.4.12)_(EC_3.6.4.13)_(nsp13)__Exoribonuclease_(ExoN)_(EC_3.1.13.-)_(nsp14)__Uridylate-specific_endoribonuclease_(EC_3.1.-.-)_(NendoU)_(nsp15)__Putative_2'-O-methyl_transferase_(EC_2.1.1.-)_(nsp16)]
## pep_2 Replicase_polyprotein_1ab_(pp1ab)_(ORF1ab_polyprotein)_[Cleaved_into:_Non-structural_protein_1_(nsp1)__Non-structural_protein_2_(nsp2)__Non-structural_protein_3_(nsp3)_(EC_3.4.19.12)_(EC_3.4.22.-)_(PL1-PRO_PL2-PRO)_(PLP1_PLP2)_(Papain-like_proteinases_1_2)_(p195)__Non-structural_protein_4_(nsp4)_(Peptide_HD2)__3C-like_proteinase_(3CL-PRO)_(3CLp)_(EC_3.4.22.-)_(M-PRO)_(nsp5)__Non-structural_protein_6_(nsp6)__Non-structural_protein_7_(nsp7)__Non-structural_protein_8_(nsp8)__Non-structural_protein_9_(nsp9)__Non-structural_protein_10_(nsp10)__Non-structural_protein_11_(nsp11)__RNA-directed_RNA_polymerase_(Pol)_(RdRp)_(EC_2.7.7.48)_(nsp12)__Helicase_(Hel)_(EC_3.6.4.12)_(EC_3.6.4.13)_(nsp13)__Exoribonuclease_(ExoN)_(EC_3.1.13.-)_(nsp14)__Uridylate-specific_endoribonuclease_(EC_3.1.-.-)_(NendoU)_(nsp15)__Putative_2'-O-methyl_transferase_(EC_2.1.1.-)_(nsp16)]
## pep_3 Replicase_polyprotein_1ab_(pp1ab)_(ORF1ab_polyprotein)_[Cleaved_into:_Non-structural_protein_1_(nsp1)__Non-structural_protein_2_(nsp2)__Non-structural_protein_3_(nsp3)_(EC_3.4.19.12)_(EC_3.4.22.-)_(PL1-PRO_PL2-PRO)_(PLP1_PLP2)_(Papain-like_proteinases_1_2)_(p195)__Non-structural_protein_4_(nsp4)_(Peptide_HD2)__3C-like_proteinase_(3CL-PRO)_(3CLp)_(EC_3.4.22.-)_(M-PRO)_(nsp5)__Non-structural_protein_6_(nsp6)__Non-structural_protein_7_(nsp7)__Non-structural_protein_8_(nsp8)__Non-structural_protein_9_(nsp9)__Non-structural_protein_10_(nsp10)__Non-structural_protein_11_(nsp11)__RNA-directed_RNA_polymerase_(Pol)_(RdRp)_(EC_2.7.7.48)_(nsp12)__Helicase_(Hel)_(EC_3.6.4.12)_(EC_3.6.4.13)_(nsp13)__Exoribonuclease_(ExoN)_(EC_3.1.13.-)_(nsp14)__Uridylate-specific_endoribonuclease_(EC_3.1.-.-)_(NendoU)_(nsp15)__Putative_2'-O-methyl_transferase_(EC_2.1.1.-)_(nsp16)]
## pep_4 Nucleoprotein_(Nucleocapsid_protein)_(NC)_(Protein_N)
## pep_5 Membrane_protein_(M_protein)_(E1_glycoprotein)_(Matrix_glycoprotein)_(Membrane_glycoprotein)
## description
## pep_1 RecName: Full
## pep_2 RecName: Full
## pep_3 RecName: Full
## pep_4 RecName: Full
## pep_5 RecName: Full
## go
## pep_1 GO:0044172;GO:0033644;GO:0044220;GO:0016021;GO:0005524;GO:0004197;GO:0004519;GO:0016896;GO:0004386;GO:0008168;GO:0008242;GO:0003723;GO:0003968;GO:0008270;GO:0039520;GO:0039648;GO:0039548;GO:0006351;GO:0019079;GO:0019082
## pep_2 GO:0044172;GO:0033644;GO:0044220;GO:0016021;GO:0005524;GO:0004197;GO:0004519;GO:0016896;GO:0004386;GO:0008168;GO:0008242;GO:0003723;GO:0003968;GO:0008270;GO:0039520;GO:0039648;GO:0039548;GO:0006351;GO:0019079;GO:0019082
## pep_3 GO:0044172;GO:0033644;GO:0044220;GO:0016021;GO:0005524;GO:0004197;GO:0004519;GO:0016896;GO:0004386;GO:0008168;GO:0008242;GO:0003723;GO:0003968;GO:0008270;GO:0039520;GO:0039648;GO:0039548;GO:0006351;GO:0019079;GO:0019082
## pep_4 GO:0044172;GO:0044177;GO:0044196;GO:0044220;GO:0019013;GO:0042802;GO:0003723
## pep_5 GO:0044178;GO:0016021;GO:0019031;GO:0055036;GO:0039660;GO:0019058
## kegg
## pep_1 <NA>
## pep_2 <NA>
## pep_3 <NA>
## pep_4 vg:918763
## pep_5 vg:918762
## pfam
## pep_1 PF16688;PF16348;PF06478;PF01661;PF09401;PF06471;PF06460;PF08716;PF08717;PF08710;PF05409;PF01443;PF08715
## pep_2 PF16688;PF16348;PF06478;PF01661;PF09401;PF06471;PF06460;PF08716;PF08717;PF08710;PF05409;PF01443;PF08715
## pep_3 PF16688;PF16348;PF06478;PF01661;PF09401;PF06471;PF06460;PF08716;PF08717;PF08710;PF05409;PF01443;PF08715
## pep_4 PF00937
## pep_5 PF01635
## embl
## pep_1 DQ010921;DQ010921;AY994055;AF326575
## pep_2 DQ010921;DQ010921;AY994055;AF326575
## pep_3 DQ010921;DQ010921;AY994055;AF326575
## pep_4 J04419;X51325;AF304460
## pep_5 X15498;M33560;AF304460
## interpro
## pep_1 IPR027351;IPR032039;IPR032505;IPR009461;IPR027352;IPR002589;IPR009466;IPR014828;IPR014829;IPR014822;IPR027417;IPR008740;IPR013016;IPR009003;IPR009469;IPR018995;IPR029063;IPR014827
## pep_2 IPR027351;IPR032039;IPR032505;IPR009461;IPR027352;IPR002589;IPR009466;IPR014828;IPR014829;IPR014822;IPR027417;IPR008740;IPR013016;IPR009003;IPR009469;IPR018995;IPR029063;IPR014827
## pep_3 IPR027351;IPR032039;IPR032505;IPR009461;IPR027352;IPR002589;IPR009466;IPR014828;IPR014829;IPR014822;IPR027417;IPR008740;IPR013016;IPR009003;IPR009469;IPR018995;IPR029063;IPR014827
## pep_4 IPR001218
## pep_5 IPR002574
## pep_name
## pep_1 Alphacoronavirus Feline coronavirus|Replicase_polyprotein_1ab_(pp1ab)_(ORF1ab_polyprotein)_[Cleaved_into:_Non-structural_protein_1_(nsp1)__Non-structural_protein_2_(nsp2)__Non-structural_protein_3_(nsp3)_(EC_3.4.19.12)_(EC_3.4.22.-)_(PL1-PRO_PL2-PRO)_(PLP1_PLP2)_(Papain-like_proteinases_1_2)_(p195)__Non-structural_protein_4_(nsp4)_(Peptide_HD2)__3C-like_proteinase_(3CL-PRO)_(3CLp)_(EC_3.4.22.-)_(M-PRO)_(nsp5)__Non-structural_protein_6_(nsp6)__Non-structural_protein_7_(nsp7)__Non-structural_protein_8_(nsp8)__Non-structural_protein_9_(nsp9)__Non-structural_protein_10_(nsp10)__Non-structural_protein_11_(nsp11)__RNA-directed_RNA_polymerase_(Pol)_(RdRp)_(EC_2.7.7.48)_(nsp12)__Helicase_(Hel)_(EC_3.6.4.12)_(EC_3.6.4.13)_(nsp13)__Exoribonuclease_(ExoN)_(EC_3.1.13.-)_(nsp14)__Uridylate-specific_endoribonuclease_(EC_3.1.-.-)_(NendoU)_(nsp15)__Putative_2'-O-methyl_transferase_(EC_2.1.1.-)_(nsp16)]
## pep_2 Alphacoronavirus Feline coronavirus|Replicase_polyprotein_1ab_(pp1ab)_(ORF1ab_polyprotein)_[Cleaved_into:_Non-structural_protein_1_(nsp1)__Non-structural_protein_2_(nsp2)__Non-structural_protein_3_(nsp3)_(EC_3.4.19.12)_(EC_3.4.22.-)_(PL1-PRO_PL2-PRO)_(PLP1_PLP2)_(Papain-like_proteinases_1_2)_(p195)__Non-structural_protein_4_(nsp4)_(Peptide_HD2)__3C-like_proteinase_(3CL-PRO)_(3CLp)_(EC_3.4.22.-)_(M-PRO)_(nsp5)__Non-structural_protein_6_(nsp6)__Non-structural_protein_7_(nsp7)__Non-structural_protein_8_(nsp8)__Non-structural_protein_9_(nsp9)__Non-structural_protein_10_(nsp10)__Non-structural_protein_11_(nsp11)__RNA-directed_RNA_polymerase_(Pol)_(RdRp)_(EC_2.7.7.48)_(nsp12)__Helicase_(Hel)_(EC_3.6.4.12)_(EC_3.6.4.13)_(nsp13)__Exoribonuclease_(ExoN)_(EC_3.1.13.-)_(nsp14)__Uridylate-specific_endoribonuclease_(EC_3.1.-.-)_(NendoU)_(nsp15)__Putative_2'-O-methyl_transferase_(EC_2.1.1.-)_(nsp16)]
## pep_3 Alphacoronavirus Feline coronavirus|Replicase_polyprotein_1ab_(pp1ab)_(ORF1ab_polyprotein)_[Cleaved_into:_Non-structural_protein_1_(nsp1)__Non-structural_protein_2_(nsp2)__Non-structural_protein_3_(nsp3)_(EC_3.4.19.12)_(EC_3.4.22.-)_(PL1-PRO_PL2-PRO)_(PLP1_PLP2)_(Papain-like_proteinases_1_2)_(p195)__Non-structural_protein_4_(nsp4)_(Peptide_HD2)__3C-like_proteinase_(3CL-PRO)_(3CLp)_(EC_3.4.22.-)_(M-PRO)_(nsp5)__Non-structural_protein_6_(nsp6)__Non-structural_protein_7_(nsp7)__Non-structural_protein_8_(nsp8)__Non-structural_protein_9_(nsp9)__Non-structural_protein_10_(nsp10)__Non-structural_protein_11_(nsp11)__RNA-directed_RNA_polymerase_(Pol)_(RdRp)_(EC_2.7.7.48)_(nsp12)__Helicase_(Hel)_(EC_3.6.4.12)_(EC_3.6.4.13)_(nsp13)__Exoribonuclease_(ExoN)_(EC_3.1.13.-)_(nsp14)__Uridylate-specific_endoribonuclease_(EC_3.1.-.-)_(NendoU)_(nsp15)__Putative_2'-O-methyl_transferase_(EC_2.1.1.-)_(nsp16)]
## pep_4 Alphacoronavirus Human coronavirus 229E|Nucleoprotein_(Nucleocapsid_protein)_(NC)_(Protein_N)
## pep_5 Alphacoronavirus Human coronavirus 229E|Membrane_protein_(M_protein)_(E1_glycoprotein)_(Matrix_glycoprotein)_(Membrane_glycoprotein)
## 1215 more rows ...as(phip_obj, "DataFrame")## ! Metadata will be lost during coercion.## DataFrame with 6100 rows and 28 columns
## sample sample_name gender group peptide pos_start
## <character> <character> <character> <character> <character> <integer>
## 1 sample_1 sample1 M ctrl pep_1 0
## 2 sample_1 sample1 M ctrl pep_2 0
## 3 sample_1 sample1 M ctrl pep_3 0
## 4 sample_1 sample1 M ctrl pep_4 0
## 5 sample_1 sample1 M ctrl pep_5 0
## ... ... ... ... ... ... ...
## 6096 sample_5 sample5 M trt pep_1216 0
## 6097 sample_5 sample5 M trt pep_1217 0
## 6098 sample_5 sample5 M trt pep_1218 0
## 6099 sample_5 sample5 M trt pep_1219 0
## 6100 sample_5 sample5 M trt pep_1220 0
## pos_end pep_id pep_dna pep_aa
## <integer> <character> <character> <character>
## 1 0 pep_88674 GATTCCGGGAAGAAGGGTTT.. DSGKKGFLDTFNHLNELEDV..
## 2 0 pep_88822 TCCAAATGTTGGGTGGAACC.. SKCWVEPDLSVGPHEFCSQH..
## 3 0 pep_88848 GATAGCAAAATTGGCCTTCA.. DSKIGLQAKPETCGLFKDCS..
## 4 0 pep_38968 GCCAACGGTGTTAAGGCTAA.. ANGVKAKGYPQFAELVPSTA..
## 5 0 pep_39072 CCTGAGGTGAATGCAATTAC.. PEVNAITVTTVLGQTYYQPI..
## ... ... ... ... ...
## 6096 0 pep_63041 GCCTACCTCATTATTGGGAT.. AYLIIGILTLLSVVAANELG..
## 6097 0 pep_59593 TCCCGCATGTCTATGGCTAT.. SRMSMAMGTMAGSGYLMFLG..
## 6098 0 pep_62971 TCTGGCATTGCCCAATCTGC.. SGIAQSASVLSFMDKGVPFM..
## 6099 0 pep_25106 GTCACAGAGGGTGAACGCAC.. VTEGERTVRVLDTVEKWLAC..
## 6100 0 pep_25078 AAGACCTTCGAACGCGAGTA.. KTFEREYPTIKQKKPDFILA..
## pep_pos pro_len uniprot_acc refseq species
## <character> <numeric> <character> <character> <character>
## 1 673_728 6709 Q98VG9 NA Alphacoronavirus Fel..
## 2 4817_4872 6709 Q98VG9 NA Alphacoronavirus Fel..
## 3 5545_5600 6709 Q98VG9 NA Alphacoronavirus Fel..
## 4 281_336 389 P15130 NP_073556.1 Alphacoronavirus Hum..
## 5 113_168 225 P15422 NP_073555.1 Alphacoronavirus Hum..
## ... ... ... ... ... ...
## 6096 2241_2296 3412 Q1X881 NA Yellow fever virus g..
## 6097 2185_2240 3412 Q074N0 NA Yellow fever virus g..
## 6098 2325_2380 3412 Q1X880 NA Yellow fever virus g..
## 6099 2661_2716 3411 P03314 NP_041726.1 Yellow fever virus g..
## 6100 1877_1932 3411 P03314 NP_041726.1 Yellow fever virus g..
## interspecies_specific product description
## <character> <character> <character>
## 1 none Replicase_polyprotei.. RecName: Full
## 2 Bat_coronavirus_1B;B.. Replicase_polyprotei.. RecName: Full
## 3 Human_coronavirus_HK.. Replicase_polyprotei.. RecName: Full
## 4 none Nucleoprotein_(Nucle.. RecName: Full
## 5 Human_coronavirus_NL.. Membrane_protein_(M_.. RecName: Full
## ... ... ... ...
## 6096 Japanese_encephaliti.. Genome_polyprotein_[.. RecName: Full
## 6097 none Genome_polyprotein_[.. RecName: Full
## 6098 none Genome_polyprotein_[.. RecName: Full
## 6099 Murray_Valley_enceph.. Genome_polyprotein_[.. RecName: Full
## 6100 none Genome_polyprotein_[.. RecName: Full
## go kegg pfam
## <character> <character> <character>
## 1 GO:0044172;GO:003364.. NA PF16688;PF16348;PF06..
## 2 GO:0044172;GO:003364.. NA PF16688;PF16348;PF06..
## 3 GO:0044172;GO:003364.. NA PF16688;PF16348;PF06..
## 4 GO:0044172;GO:004417.. vg:918763 PF00937
## 5 GO:0044178;GO:001602.. vg:918762 PF01635
## ... ... ... ...
## 6096 GO:0005576;GO:004416.. NA PF01003;PF07652;PF02..
## 6097 GO:0005576;GO:004416.. NA PF01003;PF07652;PF02..
## 6098 GO:0005576;GO:004416.. NA PF01003;PF07652;PF02..
## 6099 GO:0005576;GO:004416.. vg:1502173 PF01003;PF07652;PF02..
## 6100 GO:0005576;GO:004416.. vg:1502173 PF01003;PF07652;PF02..
## embl interpro pep_name
## <character> <character> <character>
## 1 DQ010921;DQ010921;AY.. IPR027351;IPR032039;.. Alphacoronavirus Fel..
## 2 DQ010921;DQ010921;AY.. IPR027351;IPR032039;.. Alphacoronavirus Fel..
## 3 DQ010921;DQ010921;AY.. IPR027351;IPR032039;.. Alphacoronavirus Fel..
## 4 J04419;X51325;AF304460 IPR001218 Alphacoronavirus Hum..
## 5 X15498;M33560;AF304460 IPR002574 Alphacoronavirus Hum..
## ... ... ... ...
## 6096 AY968064 IPR011492;IPR000069;.. Yellow fever virus g..
## 6097 DQ235229 IPR011492;IPR000069;.. Yellow fever virus g..
## 6098 AY968065 IPR011492;IPR000069;.. Yellow fever virus g..
## 6099 X03700;X15062;U17066.. IPR011492;IPR000069;.. Yellow fever virus g..
## 6100 X03700;X15062;U17066.. IPR011492;IPR000069;.. Yellow fever virus g..
## counts logfc prob new_assay
## <numeric> <numeric> <numeric> <numeric>
## 1 1 0.461215 0.189795 1
## 2 1 -3.672466 0.573712 1
## 3 1 0.543180 0.862342 1
## 4 1 -0.916891 0.659740 1
## 5 1 26.979922 0.930694 1
## ... ... ... ... ...
## 6096 1 -3.28352 0.5288766 1
## 6097 1 -5.58370 0.7411337 1
## 6098 1 5.09689 0.9852727 1
## 6099 1 -11.33410 0.8072356 1
## 6100 1 13.70907 0.0439523 1While PhIPData objects can be roughly constructed from Lists, lists, and DGELists, the coercion functions only construct a bare bones PhIPData object (with only counts, logfc, and prob assays and sample and peptide information). Any additional list/object components may be discarded.
7 sessionInfo()
sessionInfo()## R version 4.1.1 (2021-08-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.14-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.14-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [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] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] readr_2.0.2 dplyr_1.0.7
## [3] PhIPData_1.2.0 SummarizedExperiment_1.24.0
## [5] Biobase_2.54.0 GenomicRanges_1.46.0
## [7] GenomeInfoDb_1.30.0 IRanges_2.28.0
## [9] S4Vectors_0.32.0 BiocGenerics_0.40.0
## [11] MatrixGenerics_1.6.0 matrixStats_0.61.0
## [13] BiocStyle_2.22.0
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.7 locfit_1.5-9.4 lattice_0.20-45
## [4] assertthat_0.2.1 digest_0.6.28 utf8_1.2.2
## [7] R6_2.5.1 evaluate_0.14 highr_0.9
## [10] pillar_1.6.4 zlibbioc_1.40.0 rlang_0.4.12
## [13] jquerylib_0.1.4 Matrix_1.3-4 rmarkdown_2.11
## [16] stringr_1.4.0 bit_4.0.4 RCurl_1.98-1.5
## [19] DelayedArray_0.20.0 compiler_4.1.1 xfun_0.27
## [22] pkgconfig_2.0.3 htmltools_0.5.2 tidyselect_1.1.1
## [25] tibble_3.1.5 GenomeInfoDbData_1.2.7 bookdown_0.24
## [28] edgeR_3.36.0 fansi_0.5.0 crayon_1.4.1
## [31] tzdb_0.1.2 bitops_1.0-7 grid_4.1.1
## [34] jsonlite_1.7.2 lifecycle_1.0.1 DBI_1.1.1
## [37] magrittr_2.0.1 cli_3.0.1 stringi_1.7.5
## [40] vroom_1.5.5 XVector_0.34.0 limma_3.50.0
## [43] bslib_0.3.1 ellipsis_0.3.2 generics_0.1.1
## [46] vctrs_0.3.8 tools_4.1.1 bit64_4.0.5
## [49] glue_1.4.2 purrr_0.3.4 hms_1.1.1
## [52] parallel_4.1.1 fastmap_1.1.0 yaml_2.2.1
## [55] BiocManager_1.30.16 knitr_1.36 sass_0.4.0