Bioconductor: Difference between revisions

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== Download stats ==
== Download stats ==
* See the overview vignette of [http://bioconductor.org/packages/release/bioc/html/BiocPkgTools.html BiocPkgTools]
* See the overview vignette of [http://bioconductor.org/packages/release/bioc/html/BiocPkgTools.html BiocPkgTools]
* Download stats for Bioconductor
** [http://bioconductor.org/packages/stats/ Download stats for Bioconductor software packages]
* bioconductor.riken.jp mirror in Japan
* bioconductor.riken.jp mirror in Japan
** [https://bioconductor.riken.jp/packages/stats/index.html Download stats for Bioconductor software packages]
** [https://bioconductor.riken.jp/packages/stats/index.html Download stats for Bioconductor software packages]

Revision as of 19:55, 1 August 2020

Project

Release News

Annual reports

http://bioconductor.org/about/annual-reports/

Download stats

From the director of the project

useR 2019 & youtube

Publications

https://www.bioconductor.org/help/publications/

Bioconductor User Support

https://support.bioconductor.org/

Mirrors

https://www.bioconductor.org/about/mirrors/

Github mirror

Japan

http://bioconductor.jp

Package source

Code search

http://search.bioconductor.jp/

Resource

Teaching resources from rafalab

BiocManager from CRAN

The reason for using BiocManager instead of biocLite() is mostly to stop sourcing an R script from URL which isn’t so safe. So biocLite() should not be recommended anymore.

It allows to have multiple versions of Bioconductor installed on the same computer. For example, R 3.5 works with Bioconductor 3.7 and 3.8.

On the other hand, setRepositories(ind=1:4) and install.packages() still lets you install Bioconductor packages.

Hacking Bioconductor

BiocPkgTools

BioCExplorer

Explore Bioconductor packages more nicely 

source("https://bioconductor.org/biocLite.R")
biocLite("BiocUpgrade")
biocLite("biocViews")
devtools::install_github("seandavi/BiocPkgTools")
devtools::install_github("shians/BioCExplorer")
library(BioCExplorer)
bioc_explore()

BiocExplorer.png

BiocViews

  • Software
    • AssayDomain
    • BiologicalQuestion
    • Infrastructure
    • ResearchField
    • StatisticalMethod
    • Technology
    • WorkflowStep
  • AnnotationData
    • ChipManufacturer
    • ChipName
    • CustomArray
    • CustomCDF
    • CustomDBSchema
    • FunctionalAnnotation
    • Organism
    • PackageType
    • SequenceAnnotation
  • ExperimentData
    • AssayDomainData
    • DiseaseModel
    • OrganismData
    • PackageTypeData
    • RepositoryData
    • ReproducibleResearch
    • SpecimenSource
    • TechnologyData
  • Workflow
    • AnnotationWorkflow
    • BasicWorkflow
    • DifferentialSplicingWorkflow
    • EpigeneticsWorkflow
    • GeneExpressionWorkflow
    • GenomicVariantsWorkflow
    • ImmunoOncologyWorkflow
    • ProteomicsWorkflow
    • ResourceQueryingWorkflow
    • SingleCellWorkflow

Annotation packages

  • Introduction to Bioconductor annotation resources 2020 Bioc workshop
    • Functions 
      select(annopkg, keys, columns, keytype): keytype is optional
mapIds(annopkg, keys, column, keytype, multiVals): keytype is required, multiVals is used to handle duplicates.
    • Packages 
      TxDb packages - eg TxDb.Hsapiens.UCSC.hg19.knownGene. It contains positional information.
EnsDb packages - eg EnsDb.Hsapiens.v79

Gene centric

library(hgu133a.db)
library(AnnotationDbi)
k <- head(keys(hgu133a.db, keytype="PROBEID"))
k
# [1] "1007_s_at" "1053_at"   "117_at"    "121_at"    "1255_g_at" "1294_at"
# then call select
select(hgu133a.db, keys=k, columns=c("SYMBOL","GENENAME"), keytype="PROBEID")

# 'select()' returned 1:many mapping between keys and columns
#     PROBEID  SYMBOL                                     GENENAME
# 1 1007_s_at    DDR1  discoidin domain receptor tyrosine kinase 1
# 2 1007_s_at MIR4640                                microRNA 4640
# 3   1053_at    RFC2               replication factor C subunit 2
# 4    117_at   HSPA6 heat shock protein family A (Hsp70) member 6
# 5    121_at    PAX8                                 paired box 8
# 6 1255_g_at  GUCA1A               guanylate cyclase activator 1A
# 7   1294_at    UBA7  ubiquitin like modifier activating enzyme 7
# 8   1294_at MIR5193                                microRNA 5193

Genomic centric

  • GRanges()
  • GrangesList()

GRanges() and GRangesLists act like data.frames and lists, and can be subsetted using the [ function.

SummarizedExperiment objects

SummarizedExperiment objects are like ExpressionSets, but the row-wise annotations are GRanges, so you can subset by genomic locations.

SummarizedExperiment objects are popular objects for representing expression data and other rectangular data (feature x sample data).

Web based

Workflow

Using Bioconductor for Sequence Data

Some packages

Biobase, GEOquery and limma

How to create an ExpressionSet object from scratch? Here we use the code from GEO2R to help to do this task. 

library(Biobase)
library(GEOquery)
library(limma)

# Load series and platform data from GEO

gset <- getGEO("GSE32474", GSEMatrix =TRUE, AnnotGPL=TRUE)
if (length(gset) > 1) idx <- grep("GPL570", attr(gset, "names")) else idx <- 1
gset <- gset[[idx]]
# save(gset, file = "~/Downloads/gse32474_gset.rda")
# load("~/Downloads/gse32474_gset.rda")
table(pData(gset)[, "cell line:ch1"])
pData(gset)

# Create an ExpressionSet object from scratch
# We take a shortcut to obtain the pheno data and feature data matrices 
# from the output of getGEO()
phenoDat <- new("AnnotatedDataFrame",
                 data=pData(gset))
featureDat <- new("AnnotatedDataFrame",
                  data=fData(gset))
exampleSet <- ExpressionSet(assayData=exprs(gset),
                            phenoData=phenoDat,
                            featureData=featureDat,
                            annotation="hgu133plus2")
gset <- exampleSet

# Make proper column names to match toptable 
fvarLabels(gset) <- make.names(fvarLabels(gset))

# group names for all samples
gsms <- paste0("00000000111111111XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX",
               "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX",
               "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX",
               "XXXXXXXXXXXXXXXXXXXXXXXX")
sml <- c()
for (i in 1:nchar(gsms)) { sml[i] <- substr(gsms,i,i) }

# Subset an ExpressionSet by eliminating samples marked as "X"
sel <- which(sml != "X")
sml <- sml[sel]
gset <- gset[ ,sel]

# Decide if it is necessary to do a log2 transformation
ex <- exprs(gset)
qx <- as.numeric(quantile(ex, c(0., 0.25, 0.5, 0.75, 0.99, 1.0), na.rm=T))
LogC <- (qx[5] > 100) ||
  (qx[6]-qx[1] > 50 && qx[2] > 0) ||
  (qx[2] > 0 && qx[2] < 1 && qx[4] > 1 && qx[4] < 2)
if (LogC) { ex[which(ex <= 0)] <- NaN
exprs(gset) <- log2(ex) }

# Set up the data and proceed with analysis
sml <- paste("G", sml, sep="")    # set group names
fl <- as.factor(sml)
gset$description <- fl
design <- model.matrix(~ description + 0, gset)
colnames(design) <- levels(fl)
fit <- lmFit(gset, design)
cont.matrix <- makeContrasts(G1-G0, levels=design)
fit2 <- contrasts.fit(fit, cont.matrix)
fit2 <- eBayes(fit2, 0.01)
tT <- topTable(fit2, adjust="fdr", sort.by="B", number=250)

# Display the result with selected columns
tT <- subset(tT, select=c("ID","adj.P.Val","P.Value","t","B","logFC","Gene.symbol","Gene.title"))
tT[1:2, ]
#                  ID  adj.P.Val      P.Value        t        B    logFC Gene.symbol
# 209108_at 209108_at 0.08400054 4.438757e-06 6.686977 3.786222 3.949088      TSPAN6
# 204975_at 204975_at 0.08400054 6.036355e-06 6.520775 3.550036 2.919995        EMP2
#                             Gene.title
# 209108_at                 tetraspanin 6
# 204975_at epithelial membrane protein 2

Biostrings

library(Biostrings) 
library(BSgenome.Hsapiens.UCSC.hg19) 
vmatchPattern("GCGATCGC", Hsapiens)

plyranges

http://bioconductor.org/packages/devel/bioc/vignettes/plyranges/inst/doc/an-introduction.html

Misc

Package release history

https://support.bioconductor.org/p/69657/

Search the DESCRIPTION file (eg. VariantAnnotation package) in github and the release information can be found there.

Papers/Overview

Using R and Bioconductor in Clinical Genomics and Transcriptomics 2019

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