This article collects notes on Bioconductor packages, made available here to faciliate their use and extensions.
pkgs <- c("AnnotationDbi", "AnnotationFilter", "ComplexHeatmap", "DESeq2", "EnsDb.Hsapiens.v86",
"FlowSorted.DLPFC.450k", "GeneNet", "GenomicFeatures", "IlluminaHumanMethylation450kmanifest",
"OUTRIDER","RColorBrewer", "RMariaDB", "Rgraphviz", "S4Vectors", "SummarizedExperiment",
"TxDb.Hsapiens.UCSC.hg38.knownGene", "bladderbatch", "clusterProfiler",
"corpcor", "doParallel", "ensembldb", "fdrtool", "graph", "graphite", "heatmaply",
"minfi", "org.Hs.eg.db", "plyr", "quantro", "recount3", "sva")
for (p in pkgs) if (length(grep(paste("^package:", p, "$", sep=""), search())) == 0) {
if (!requireNamespace(p)) warning(paste0("This vignette needs package `", p, "'; please install"))
}
invisible(suppressMessages(lapply(pkgs, require, character.only = TRUE)))1 liftover
See inst/turbomanin the source, https://github.com/jinghuazhao/pQTLtools/tree/master/inst/turboman, or turboman/ directory in the installed package.
2 Normalisation
2.1 ComBat
This is the documentation example, based on Bioconductor 3.14.
data(bladderdata, package="bladderbatch")
edat <- bladderEset[1:50]
pheno <- Biobase::pData(edat)
batch <- pheno$batch
table(batch)
#> batch
#> 1 2 3 4 5
#> 11 18 4 5 19
quantro::matboxplot(edat,batch,cex.axis=0.6,notch=TRUE,pch=19,ylab="Expression")
Figure 2.1: ComBat example
quantro::matdensity(edat,batch,xlab=" ",ylab="density")
legend("topleft",legend=1:5,col=1:5,lty=1)
Figure 2.2: ComBat example
# 1. parametric adjustment
combat_edata1 <- sva::ComBat(dat=edat, batch=batch, par.prior=TRUE, prior.plots=TRUE)
#> Found5batches
#> Adjusting for0covariate(s) or covariate level(s)
#> Standardizing Data across genes
#> Fitting L/S model and finding priors
Figure 2.3: ComBat example
#> Finding parametric adjustments
#> Adjusting the Data
# 2. non-parametric adjustment, mean-only version
combat_edata2 <- sva::ComBat(dat=edat, batch=batch, par.prior=FALSE, mean.only=TRUE)
#> Using the 'mean only' version of ComBat
#> Found5batches
#> Adjusting for0covariate(s) or covariate level(s)
#> Standardizing Data across genes
#> Fitting L/S model and finding priors
#> Finding nonparametric adjustments
#> Adjusting the Data
# 3. reference-batch version, with covariates
mod <- model.matrix(~as.factor(cancer), data=pheno)
combat_edata3 <- sva::ComBat(dat=edat, batch=batch, mod=mod, par.prior=TRUE, ref.batch=3, prior.plots=TRUE)
#> Using batch =3as a reference batch (this batch won't change)
#> Found5batches
#> Adjusting for2covariate(s) or covariate level(s)
#> Standardizing Data across genes
#> Fitting L/S model and finding priors
Figure 2.4: ComBat example
#> Finding parametric adjustments
#> Adjusting the Data2.2 quantro
This is also adapted from the package vignette but with FlowSorted.DLPFC.450k in place of FlowSorted.
data(FlowSorted.DLPFC.450k,package="FlowSorted.DLPFC.450k")
p <- getBeta(FlowSorted.DLPFC.450k,offset=100)
pd <- Biobase::pData(FlowSorted.DLPFC.450k)
quantro::matboxplot(p, groupFactor = pd$CellType, xaxt = "n", main = "Beta Values", pch=19)
Figure 2.5: quantro example
quantro::matdensity(p, groupFactor = pd$CellType, xlab = " ", ylab = "density",
main = "Beta Values", brewer.n = 8, brewer.name = "Dark2")
legend('top', c("NeuN_neg", "NeuN_pos"), col = c(1, 2), lty = 1, lwd = 3)
Figure 2.6: quantro example
qtest <- quantro::quantro(object = p, groupFactor = pd$CellType)
#> [quantro] Average medians of the distributions are
#> not equal across groups.
#> [quantro] Calculating the quantro test statistic.
#> [quantro] No permutation testing performed.
#> Use B > 0 for permutation testing.
if (FALSE)
{
doParallel::registerDoParallel(cores=10)
qtestPerm <- quantro::quantro(p, groupFactor = pd$CellType, B = 1000)
quantro::quantroPlot(qtestPerm)
}3 Outlier detection in RNA-Seq
The following is adapted from package OUTRIDER,
ctsFile <- system.file('extdata', 'KremerNBaderSmall.tsv', package='OUTRIDER')
ctsTable <- read.table(ctsFile, check.names=FALSE)
ods <- OUTRIDER::OutriderDataSet(countData=ctsTable)
ods <- OUTRIDER::filterExpression(ods, minCounts=TRUE, filterGenes=TRUE)
#> 229 genes did not pass the filter due to zero counts. This is 22.9% of the genes.
ods <- OUTRIDER::OUTRIDER(ods)
#> Sun Oct 13 08:58:38 2024: SizeFactor estimation ...
#> Sun Oct 13 08:58:38 2024: Controlling for confounders ...
#> Using estimated q with: 23
#> Sun Oct 13 08:58:38 2024: Using the autoencoder implementation for controlling.
#> [1] "Sun Oct 13 08:58:41 2024: Initial PCA loss: 4.73997327486604"
#> [1] "Sun Oct 13 08:58:44 2024: Iteration: 1 loss: 4.1941928513778"
#> [1] "Sun Oct 13 08:58:46 2024: Iteration: 2 loss: 4.17533494991789"
#> [1] "Sun Oct 13 08:58:48 2024: Iteration: 3 loss: 4.16629394417218"
#> [1] "Sun Oct 13 08:58:49 2024: Iteration: 4 loss: 4.16116322481112"
#> [1] "Sun Oct 13 08:58:51 2024: Iteration: 5 loss: 4.15778153531172"
#> [1] "Sun Oct 13 08:58:52 2024: Iteration: 6 loss: 4.1552156569908"
#> [1] "Sun Oct 13 08:58:53 2024: Iteration: 7 loss: 4.15349342022442"
#> [1] "Sun Oct 13 08:58:54 2024: Iteration: 8 loss: 4.15179068270742"
#> [1] "Sun Oct 13 08:58:55 2024: Iteration: 9 loss: 4.15084764641122"
#> [1] "Sun Oct 13 08:58:56 2024: Iteration: 10 loss: 4.14995079973815"
#> [1] "Sun Oct 13 08:58:57 2024: Iteration: 11 loss: 4.14922407983485"
#> [1] "Sun Oct 13 08:58:58 2024: Iteration: 12 loss: 4.14898308414091"
#> [1] "Sun Oct 13 08:58:59 2024: Iteration: 13 loss: 4.14837365500828"
#> [1] "Sun Oct 13 08:59:00 2024: Iteration: 14 loss: 4.1477217113268"
#> [1] "Sun Oct 13 08:59:01 2024: Iteration: 15 loss: 4.14695790295889"
#> Time difference of 19.24515 secs
#> [1] "Sun Oct 13 08:59:01 2024: 15 Final nb-AE loss: 4.14695790295889"
#> Sun Oct 13 08:59:01 2024: Used the autoencoder implementation for controlling.
#> Sun Oct 13 08:59:01 2024: P-value calculation ...
#> Sun Oct 13 08:59:05 2024: Zscore calculation ...
res <- OUTRIDER::results(ods)
knitr::kable(res,caption="A check list of outliers")| geneID | sampleID | pValue | padjust | zScore | l2fc | rawcounts | meanRawcounts | normcounts | meanCorrected | theta | aberrant | AberrantBySample | AberrantByGene | padj_rank |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ATAD3C | MUC1360 | 0.0e+00 | 0.0000001 | 5.28 | 1.88 | 948 | 82.29 | 248.30 | 67.33 | 16.49 | TRUE | 1 | 1 | 1 |
| NBPF15 | MUC1351 | 0.0e+00 | 0.0000043 | 5.76 | 0.77 | 7591 | 4224.88 | 7019.13 | 4121.25 | 112.12 | TRUE | 2 | 1 | 1 |
| MSTO1 | MUC1367 | 0.0e+00 | 0.0000181 | -6.26 | -0.81 | 761 | 1327.87 | 728.28 | 1276.02 | 153.50 | TRUE | 1 | 1 | 1 |
| HDAC1 | MUC1350 | 0.0e+00 | 0.0001460 | -5.83 | -0.77 | 2215 | 3805.56 | 2143.23 | 3648.91 | 136.84 | TRUE | 1 | 1 | 1 |
| DCAF6 | MUC1374 | 1.0e-07 | 0.0003961 | -5.68 | -0.62 | 2348 | 4869.53 | 3082.96 | 4724.25 | 196.33 | TRUE | 1 | 1 | 1 |
| NBPF16 | MUC1351 | 3.0e-07 | 0.0007275 | 4.82 | 0.67 | 4014 | 2459.90 | 3818.18 | 2402.36 | 107.84 | TRUE | 2 | 1 | 2 |
| FAM102B | MUC1363 | 1.2e-06 | 0.0065107 | -5.38 | -1.28 | 455 | 1138.75 | 442.45 | 1076.65 | 42.49 | TRUE | 1 | 1 | 1 |
| LOC100288142 | MUC1361 | 3.2e-06 | 0.0179562 | 4.24 | 0.84 | 637 | 356.12 | 621.54 | 345.76 | 57.39 | TRUE | 1 | 1 | 1 |
| TARDBP | MUC0486 | 7.1e-06 | 0.0394571 | -4.57 | -0.32 | 5911 | 5780.34 | 4447.90 | 5565.44 | 465.01 | TRUE | 1 | 1 | 1 |
| ZMPSTE24 | MUC1370 | 7.2e-06 | 0.0400441 | 4.29 | 0.39 | 6180 | 4026.77 | 5105.04 | 3905.53 | 267.48 | TRUE | 1 | 1 | 1 |
OUTRIDER::plotQQ(ods, res["geneID"],global=TRUE)
Figure 3.1: Q-Q plot for Kremer data
4 Differential expression
ex <- DESeq2::makeExampleDESeqDataSet(m=4)
dds <- DESeq2::DESeq(ex)
#> estimating size factors
#> estimating dispersions
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> final dispersion estimates
#> fitting model and testing
res <- DESeq2::results(dds, contrast=c("condition","B","A"))
rld <- DESeq2::rlogTransformation(ex, blind=TRUE)
dat <- DESeq2::plotPCA(rld, intgroup=c("condition"),returnData=TRUE)
#> using ntop=500 top features by variance
percentVar <- round(100 * attr(dat,"percentVar"))
ggplot2::ggplot(dat, ggplot2::aes(PC1, PC2, color=condition, shape=condition)) +
ggplot2::geom_point(size=3) +
ggplot2::xlab(paste0("PC1:",percentVar[1],"% variance")) +
ggplot2::ylab(paste0("PC2:",percentVar[2],"% variance"))
Figure 4.1: DESeq2 example
ex$condition <- relevel(ex$condition, ref="B")
dds2 <- DESeq2::DESeq(dds)
#> using pre-existing size factors
#> estimating dispersions
#> found already estimated dispersions, replacing these
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> final dispersion estimates
#> fitting model and testing
res <- DESeq2::results(dds2)
knitr::kable(head(as.data.frame(res)))| baseMean | log2FoldChange | lfcSE | stat | pvalue | padj | |
|---|---|---|---|---|---|---|
| gene1 | 14.884419 | 0.0520755 | 1.1470172 | 0.0454008 | 0.9637879 | 0.9993243 |
| gene2 | 11.548805 | 0.4634034 | 1.2386004 | 0.3741347 | 0.7083041 | 0.9979958 |
| gene3 | 2.169024 | -1.0421572 | 2.6625736 | -0.3914097 | 0.6954944 | 0.9979958 |
| gene4 | 18.260784 | -0.1780404 | 1.0134155 | -0.1756835 | 0.8605426 | 0.9979958 |
| gene5 | 34.661516 | 0.4515120 | 0.7486085 | 0.6031350 | 0.5464189 | 0.9979958 |
| gene6 | 53.316984 | -0.4287130 | 0.6632957 | -0.6463377 | 0.5180607 | 0.9979958 |
See the package in action from a snakemake workflow1.
5 Gene co-expression and network analysis
A simple network is furnished with the GeneNet documentation example,
## A random network with 40 nodes
# it contains 780=40*39/2 edges of which 5 percent (=39) are non-zero
true.pcor <- GeneNet::ggm.simulate.pcor(40)
# A data set with 40 observations
m.sim <- GeneNet::ggm.simulate.data(40, true.pcor)
# A simple estimate of partial correlations
estimated.pcor <- corpcor::cor2pcor( cor(m.sim) )
# A comparison of estimated and true values
sum((true.pcor-estimated.pcor)^2)
#> [1] 690.4249
# A slightly better estimate ...
estimated.pcor.2 <- GeneNet::ggm.estimate.pcor(m.sim)
#> Estimating optimal shrinkage intensity lambda (correlation matrix): 0.3524
sum((true.pcor-estimated.pcor.2)^2)
#> [1] 9.368209
## ecoli data
data(ecoli, package="GeneNet")
# partial correlation matrix
inferred.pcor <- GeneNet::ggm.estimate.pcor(ecoli)
#> Estimating optimal shrinkage intensity lambda (correlation matrix): 0.1804
# p-values, q-values and posterior probabilities for each potential edge
test.results <- GeneNet::network.test.edges(inferred.pcor)
#> Estimate (local) false discovery rates (partial correlations):
#> Step 1... determine cutoff point
#> Step 2... estimate parameters of null distribution and eta0
#> Step 3... compute p-values and estimate empirical PDF/CDF
#> Step 4... compute q-values and local fdr
#> Step 5... prepare for plotting
Figure 5.1: GeneNet example
# best 20 edges (strongest correlation)
test.results[1:20,]
#> pcor node1 node2 pval qval prob
#> 1 0.23185664 51 53 2.220446e-16 3.612205e-13 1.0000000
#> 2 0.22405545 52 53 2.220446e-16 3.612205e-13 1.0000000
#> 3 0.21507824 51 52 2.220446e-16 3.612205e-13 1.0000000
#> 4 0.17328863 7 93 3.108624e-15 3.792816e-12 0.9999945
#> 5 -0.13418892 29 86 1.120811e-09 1.093997e-06 0.9999516
#> 6 0.12594697 21 72 1.103836e-08 8.978558e-06 0.9998400
#> 7 0.11956105 28 86 5.890921e-08 3.853588e-05 0.9998400
#> 8 -0.11723897 26 80 1.060525e-07 5.816169e-05 0.9998400
#> 9 -0.11711625 72 89 1.093654e-07 5.930496e-05 0.9972804
#> 10 0.10658013 20 21 1.366610e-06 5.925272e-04 0.9972804
#> 11 0.10589778 21 73 1.596859e-06 6.678426e-04 0.9972804
#> 12 0.10478689 20 91 2.053403e-06 8.024421e-04 0.9972804
#> 13 0.10420836 7 52 2.338381e-06 8.778601e-04 0.9944557
#> 14 0.10236077 87 95 3.525185e-06 1.224963e-03 0.9944557
#> 15 0.10113550 27 95 4.610442e-06 1.500047e-03 0.9920084
#> 16 0.09928954 21 51 6.868355e-06 2.046548e-03 0.9920084
#> 17 0.09791914 21 88 9.192370e-06 2.520615e-03 0.9920084
#> 18 0.09719685 18 95 1.070232e-05 2.790101e-03 0.9920084
#> 19 0.09621791 28 90 1.313007e-05 3.171816e-03 0.9920084
#> 20 0.09619099 12 80 1.320373e-05 3.182525e-03 0.9920084
# network containing edges with prob > 0.9 (i.e. local fdr < 0.1)
net <- GeneNet::extract.network(test.results, cutoff.ggm=0.9)
#>
#> Significant edges: 65
#> Corresponding to 1.26 % of possible edges
net
#> pcor node1 node2 pval qval prob
#> 1 0.23185664 51 53 2.220446e-16 3.612205e-13 1.0000000
#> 2 0.22405545 52 53 2.220446e-16 3.612205e-13 1.0000000
#> 3 0.21507824 51 52 2.220446e-16 3.612205e-13 1.0000000
#> 4 0.17328863 7 93 3.108624e-15 3.792816e-12 0.9999945
#> 5 -0.13418892 29 86 1.120811e-09 1.093997e-06 0.9999516
#> 6 0.12594697 21 72 1.103836e-08 8.978558e-06 0.9998400
#> 7 0.11956105 28 86 5.890921e-08 3.853588e-05 0.9998400
#> 8 -0.11723897 26 80 1.060525e-07 5.816169e-05 0.9998400
#> 9 -0.11711625 72 89 1.093654e-07 5.930496e-05 0.9972804
#> 10 0.10658013 20 21 1.366610e-06 5.925272e-04 0.9972804
#> 11 0.10589778 21 73 1.596859e-06 6.678426e-04 0.9972804
#> 12 0.10478689 20 91 2.053403e-06 8.024421e-04 0.9972804
#> 13 0.10420836 7 52 2.338381e-06 8.778601e-04 0.9944557
#> 14 0.10236077 87 95 3.525185e-06 1.224963e-03 0.9944557
#> 15 0.10113550 27 95 4.610442e-06 1.500047e-03 0.9920084
#> 16 0.09928954 21 51 6.868355e-06 2.046548e-03 0.9920084
#> 17 0.09791914 21 88 9.192370e-06 2.520615e-03 0.9920084
#> 18 0.09719685 18 95 1.070232e-05 2.790101e-03 0.9920084
#> 19 0.09621791 28 90 1.313007e-05 3.171816e-03 0.9920084
#> 20 0.09619099 12 80 1.320373e-05 3.182525e-03 0.9920084
#> 21 0.09576091 89 95 1.443542e-05 3.354775e-03 0.9891317
#> 22 0.09473210 7 51 1.784126e-05 3.864824e-03 0.9891317
#> 23 -0.09386896 53 58 2.127621e-05 4.313589e-03 0.9891317
#> 24 -0.09366615 29 83 2.217012e-05 4.421098e-03 0.9891317
#> 25 -0.09341148 21 89 2.334320e-05 4.556945e-03 0.9810727
#> 26 -0.09156391 49 93 3.380042e-05 5.955970e-03 0.9810727
#> 27 -0.09150710 80 90 3.418362e-05 6.002081e-03 0.9810727
#> 28 0.09101505 7 53 3.767965e-05 6.408100e-03 0.9810727
#> 29 0.09050688 21 84 4.164469e-05 6.838780e-03 0.9810727
#> 30 0.08965490 72 73 4.919364e-05 7.581864e-03 0.9810727
#> 31 -0.08934025 29 99 5.229603e-05 7.861414e-03 0.9810727
#> 32 -0.08906819 9 95 5.512706e-05 8.104756e-03 0.9810727
#> 33 0.08888345 2 49 5.713142e-05 8.270670e-03 0.9810727
#> 34 0.08850681 86 90 6.143361e-05 8.610158e-03 0.9810727
#> 35 0.08805868 17 53 6.695168e-05 9.015173e-03 0.9810727
#> 36 0.08790809 28 48 6.890882e-05 9.151288e-03 0.9810727
#> 37 0.08783471 33 58 6.988208e-05 9.217594e-03 0.9682377
#> 38 -0.08705796 7 49 8.101242e-05 1.021362e-02 0.9682377
#> 39 0.08645033 20 46 9.086544e-05 1.102466e-02 0.9682377
#> 40 0.08609950 48 86 9.705860e-05 1.150392e-02 0.9682377
#> 41 0.08598769 21 52 9.911455e-05 1.165816e-02 0.9682377
#> 42 0.08555275 32 95 1.075098e-04 1.226435e-02 0.9682377
#> 43 0.08548231 17 51 1.089310e-04 1.236336e-02 0.9424722
#> 44 0.08470370 80 83 1.258658e-04 1.382356e-02 0.9424722
#> 45 0.08442510 80 82 1.325062e-04 1.437067e-02 0.9174573
#> 46 0.08271606 80 93 1.810274e-04 1.845631e-02 0.9174573
#> 47 0.08235175 46 91 1.933328e-04 1.941579e-02 0.9174573
#> 48 0.08217787 25 95 1.994788e-04 1.988432e-02 0.9174573
#> 49 -0.08170331 29 87 2.171998e-04 2.119714e-02 0.9174573
#> 50 0.08123632 19 29 2.360716e-04 2.253605e-02 0.9174573
#> 51 0.08101702 51 84 2.454546e-04 2.318024e-02 0.9174573
#> 52 0.08030748 16 93 2.782642e-04 2.532795e-02 0.9174573
#> 53 0.08006503 28 52 2.903869e-04 2.608270e-02 0.9174573
#> 54 -0.07941656 41 80 3.252832e-04 2.814823e-02 0.9174573
#> 55 0.07941410 54 89 3.254228e-04 2.815619e-02 0.9174573
#> 56 -0.07934653 28 80 3.292783e-04 2.837510e-02 0.9174573
#> 57 0.07916783 29 92 3.396801e-04 2.895701e-02 0.9174573
#> 58 -0.07866905 17 86 3.703634e-04 3.060292e-02 0.9174573
#> 59 0.07827749 16 29 3.962445e-04 3.191461e-02 0.9174573
#> 60 -0.07808262 73 89 4.097451e-04 3.257289e-02 0.9174573
#> 61 0.07766261 52 67 4.403164e-04 3.400206e-02 0.9174573
#> 62 0.07762917 25 87 4.428395e-04 3.411636e-02 0.9174573
#> 63 -0.07739378 9 93 4.609871e-04 3.492294e-02 0.9174573
#> 64 0.07738885 31 80 4.613746e-04 3.493987e-02 0.9174573
#> 65 -0.07718681 80 94 4.775134e-04 3.563443e-02 0.9174573
# significant based on FDR cutoff Q=0.05?
num.significant.1 <- sum(test.results$qval <= 0.05)
test.results[1:num.significant.1,]
#> pcor node1 node2 pval qval prob
#> 1 0.23185664 51 53 2.220446e-16 3.612205e-13 1.0000000
#> 2 0.22405545 52 53 2.220446e-16 3.612205e-13 1.0000000
#> 3 0.21507824 51 52 2.220446e-16 3.612205e-13 1.0000000
#> 4 0.17328863 7 93 3.108624e-15 3.792816e-12 0.9999945
#> 5 -0.13418892 29 86 1.120811e-09 1.093997e-06 0.9999516
#> 6 0.12594697 21 72 1.103836e-08 8.978558e-06 0.9998400
#> 7 0.11956105 28 86 5.890921e-08 3.853588e-05 0.9998400
#> 8 -0.11723897 26 80 1.060525e-07 5.816169e-05 0.9998400
#> 9 -0.11711625 72 89 1.093654e-07 5.930496e-05 0.9972804
#> 10 0.10658013 20 21 1.366610e-06 5.925272e-04 0.9972804
#> 11 0.10589778 21 73 1.596859e-06 6.678426e-04 0.9972804
#> 12 0.10478689 20 91 2.053403e-06 8.024421e-04 0.9972804
#> 13 0.10420836 7 52 2.338381e-06 8.778601e-04 0.9944557
#> 14 0.10236077 87 95 3.525185e-06 1.224963e-03 0.9944557
#> 15 0.10113550 27 95 4.610442e-06 1.500047e-03 0.9920084
#> 16 0.09928954 21 51 6.868355e-06 2.046548e-03 0.9920084
#> 17 0.09791914 21 88 9.192370e-06 2.520615e-03 0.9920084
#> 18 0.09719685 18 95 1.070232e-05 2.790101e-03 0.9920084
#> 19 0.09621791 28 90 1.313007e-05 3.171816e-03 0.9920084
#> 20 0.09619099 12 80 1.320373e-05 3.182525e-03 0.9920084
#> 21 0.09576091 89 95 1.443542e-05 3.354775e-03 0.9891317
#> 22 0.09473210 7 51 1.784126e-05 3.864824e-03 0.9891317
#> 23 -0.09386896 53 58 2.127621e-05 4.313589e-03 0.9891317
#> 24 -0.09366615 29 83 2.217012e-05 4.421098e-03 0.9891317
#> 25 -0.09341148 21 89 2.334320e-05 4.556945e-03 0.9810727
#> 26 -0.09156391 49 93 3.380042e-05 5.955970e-03 0.9810727
#> 27 -0.09150710 80 90 3.418362e-05 6.002081e-03 0.9810727
#> 28 0.09101505 7 53 3.767965e-05 6.408100e-03 0.9810727
#> 29 0.09050688 21 84 4.164469e-05 6.838780e-03 0.9810727
#> 30 0.08965490 72 73 4.919364e-05 7.581864e-03 0.9810727
#> 31 -0.08934025 29 99 5.229603e-05 7.861414e-03 0.9810727
#> 32 -0.08906819 9 95 5.512706e-05 8.104756e-03 0.9810727
#> 33 0.08888345 2 49 5.713142e-05 8.270670e-03 0.9810727
#> 34 0.08850681 86 90 6.143361e-05 8.610158e-03 0.9810727
#> 35 0.08805868 17 53 6.695168e-05 9.015173e-03 0.9810727
#> 36 0.08790809 28 48 6.890882e-05 9.151288e-03 0.9810727
#> 37 0.08783471 33 58 6.988208e-05 9.217594e-03 0.9682377
#> 38 -0.08705796 7 49 8.101242e-05 1.021362e-02 0.9682377
#> 39 0.08645033 20 46 9.086544e-05 1.102466e-02 0.9682377
#> 40 0.08609950 48 86 9.705860e-05 1.150392e-02 0.9682377
#> 41 0.08598769 21 52 9.911455e-05 1.165816e-02 0.9682377
#> 42 0.08555275 32 95 1.075098e-04 1.226435e-02 0.9682377
#> 43 0.08548231 17 51 1.089310e-04 1.236336e-02 0.9424722
#> 44 0.08470370 80 83 1.258658e-04 1.382356e-02 0.9424722
#> 45 0.08442510 80 82 1.325062e-04 1.437067e-02 0.9174573
#> 46 0.08271606 80 93 1.810274e-04 1.845631e-02 0.9174573
#> 47 0.08235175 46 91 1.933328e-04 1.941579e-02 0.9174573
#> 48 0.08217787 25 95 1.994788e-04 1.988432e-02 0.9174573
#> 49 -0.08170331 29 87 2.171998e-04 2.119714e-02 0.9174573
#> 50 0.08123632 19 29 2.360716e-04 2.253605e-02 0.9174573
#> 51 0.08101702 51 84 2.454546e-04 2.318024e-02 0.9174573
#> 52 0.08030748 16 93 2.782642e-04 2.532795e-02 0.9174573
#> 53 0.08006503 28 52 2.903869e-04 2.608270e-02 0.9174573
#> 54 -0.07941656 41 80 3.252832e-04 2.814823e-02 0.9174573
#> 55 0.07941410 54 89 3.254228e-04 2.815619e-02 0.9174573
#> 56 -0.07934653 28 80 3.292783e-04 2.837510e-02 0.9174573
#> 57 0.07916783 29 92 3.396801e-04 2.895701e-02 0.9174573
#> 58 -0.07866905 17 86 3.703634e-04 3.060292e-02 0.9174573
#> 59 0.07827749 16 29 3.962445e-04 3.191461e-02 0.9174573
#> 60 -0.07808262 73 89 4.097451e-04 3.257289e-02 0.9174573
#> 61 0.07766261 52 67 4.403164e-04 3.400206e-02 0.9174573
#> 62 0.07762917 25 87 4.428395e-04 3.411636e-02 0.9174573
#> 63 -0.07739378 9 93 4.609871e-04 3.492294e-02 0.9174573
#> 64 0.07738885 31 80 4.613746e-04 3.493987e-02 0.9174573
#> 65 -0.07718681 80 94 4.775134e-04 3.563443e-02 0.9174573
#> 66 0.07706275 27 58 4.876830e-04 3.606178e-02 0.8297811
#> 67 -0.07610709 16 83 5.730531e-04 4.085919e-02 0.8297811
#> 68 0.07550557 53 84 6.337142e-04 4.406471e-02 0.8297811
# significant based on "local fdr" cutoff (prob > 0.9)?
num.significant.2 <- sum(test.results$prob > 0.9)
test.results[test.results$prob > 0.9,]
#> pcor node1 node2 pval qval prob
#> 1 0.23185664 51 53 2.220446e-16 3.612205e-13 1.0000000
#> 2 0.22405545 52 53 2.220446e-16 3.612205e-13 1.0000000
#> 3 0.21507824 51 52 2.220446e-16 3.612205e-13 1.0000000
#> 4 0.17328863 7 93 3.108624e-15 3.792816e-12 0.9999945
#> 5 -0.13418892 29 86 1.120811e-09 1.093997e-06 0.9999516
#> 6 0.12594697 21 72 1.103836e-08 8.978558e-06 0.9998400
#> 7 0.11956105 28 86 5.890921e-08 3.853588e-05 0.9998400
#> 8 -0.11723897 26 80 1.060525e-07 5.816169e-05 0.9998400
#> 9 -0.11711625 72 89 1.093654e-07 5.930496e-05 0.9972804
#> 10 0.10658013 20 21 1.366610e-06 5.925272e-04 0.9972804
#> 11 0.10589778 21 73 1.596859e-06 6.678426e-04 0.9972804
#> 12 0.10478689 20 91 2.053403e-06 8.024421e-04 0.9972804
#> 13 0.10420836 7 52 2.338381e-06 8.778601e-04 0.9944557
#> 14 0.10236077 87 95 3.525185e-06 1.224963e-03 0.9944557
#> 15 0.10113550 27 95 4.610442e-06 1.500047e-03 0.9920084
#> 16 0.09928954 21 51 6.868355e-06 2.046548e-03 0.9920084
#> 17 0.09791914 21 88 9.192370e-06 2.520615e-03 0.9920084
#> 18 0.09719685 18 95 1.070232e-05 2.790101e-03 0.9920084
#> 19 0.09621791 28 90 1.313007e-05 3.171816e-03 0.9920084
#> 20 0.09619099 12 80 1.320373e-05 3.182525e-03 0.9920084
#> 21 0.09576091 89 95 1.443542e-05 3.354775e-03 0.9891317
#> 22 0.09473210 7 51 1.784126e-05 3.864824e-03 0.9891317
#> 23 -0.09386896 53 58 2.127621e-05 4.313589e-03 0.9891317
#> 24 -0.09366615 29 83 2.217012e-05 4.421098e-03 0.9891317
#> 25 -0.09341148 21 89 2.334320e-05 4.556945e-03 0.9810727
#> 26 -0.09156391 49 93 3.380042e-05 5.955970e-03 0.9810727
#> 27 -0.09150710 80 90 3.418362e-05 6.002081e-03 0.9810727
#> 28 0.09101505 7 53 3.767965e-05 6.408100e-03 0.9810727
#> 29 0.09050688 21 84 4.164469e-05 6.838780e-03 0.9810727
#> 30 0.08965490 72 73 4.919364e-05 7.581864e-03 0.9810727
#> 31 -0.08934025 29 99 5.229603e-05 7.861414e-03 0.9810727
#> 32 -0.08906819 9 95 5.512706e-05 8.104756e-03 0.9810727
#> 33 0.08888345 2 49 5.713142e-05 8.270670e-03 0.9810727
#> 34 0.08850681 86 90 6.143361e-05 8.610158e-03 0.9810727
#> 35 0.08805868 17 53 6.695168e-05 9.015173e-03 0.9810727
#> 36 0.08790809 28 48 6.890882e-05 9.151288e-03 0.9810727
#> 37 0.08783471 33 58 6.988208e-05 9.217594e-03 0.9682377
#> 38 -0.08705796 7 49 8.101242e-05 1.021362e-02 0.9682377
#> 39 0.08645033 20 46 9.086544e-05 1.102466e-02 0.9682377
#> 40 0.08609950 48 86 9.705860e-05 1.150392e-02 0.9682377
#> 41 0.08598769 21 52 9.911455e-05 1.165816e-02 0.9682377
#> 42 0.08555275 32 95 1.075098e-04 1.226435e-02 0.9682377
#> 43 0.08548231 17 51 1.089310e-04 1.236336e-02 0.9424722
#> 44 0.08470370 80 83 1.258658e-04 1.382356e-02 0.9424722
#> 45 0.08442510 80 82 1.325062e-04 1.437067e-02 0.9174573
#> 46 0.08271606 80 93 1.810274e-04 1.845631e-02 0.9174573
#> 47 0.08235175 46 91 1.933328e-04 1.941579e-02 0.9174573
#> 48 0.08217787 25 95 1.994788e-04 1.988432e-02 0.9174573
#> 49 -0.08170331 29 87 2.171998e-04 2.119714e-02 0.9174573
#> 50 0.08123632 19 29 2.360716e-04 2.253605e-02 0.9174573
#> 51 0.08101702 51 84 2.454546e-04 2.318024e-02 0.9174573
#> 52 0.08030748 16 93 2.782642e-04 2.532795e-02 0.9174573
#> 53 0.08006503 28 52 2.903869e-04 2.608270e-02 0.9174573
#> 54 -0.07941656 41 80 3.252832e-04 2.814823e-02 0.9174573
#> 55 0.07941410 54 89 3.254228e-04 2.815619e-02 0.9174573
#> 56 -0.07934653 28 80 3.292783e-04 2.837510e-02 0.9174573
#> 57 0.07916783 29 92 3.396801e-04 2.895701e-02 0.9174573
#> 58 -0.07866905 17 86 3.703634e-04 3.060292e-02 0.9174573
#> 59 0.07827749 16 29 3.962445e-04 3.191461e-02 0.9174573
#> 60 -0.07808262 73 89 4.097451e-04 3.257289e-02 0.9174573
#> 61 0.07766261 52 67 4.403164e-04 3.400206e-02 0.9174573
#> 62 0.07762917 25 87 4.428395e-04 3.411636e-02 0.9174573
#> 63 -0.07739378 9 93 4.609871e-04 3.492294e-02 0.9174573
#> 64 0.07738885 31 80 4.613746e-04 3.493987e-02 0.9174573
#> 65 -0.07718681 80 94 4.775134e-04 3.563443e-02 0.9174573
# parameters of the mixture distribution used to compute p-values etc.
c <- fdrtool::fdrtool(corpcor::sm2vec(inferred.pcor), statistic="correlation")
#> Step 1... determine cutoff point
#> Step 2... estimate parameters of null distribution and eta0
#> Step 3... compute p-values and estimate empirical PDF/CDF
#> Step 4... compute q-values and local fdr
#> Step 5... prepare for plotting
c$param
#> cutoff N.cens eta0 eta0.SE kappa kappa.SE
#> [1,] 0.03553068 4352 0.9474623 0.005656465 2043.378 94.72266
## A random network with 20 nodes and 10 percent (=19) edges
true.pcor <- GeneNet::ggm.simulate.pcor(20, 0.1)
# convert to edge list
test.results <- GeneNet::ggm.list.edges(true.pcor)[1:19,]
nlab <- LETTERS[1:20]
# graphviz
# network.make.dot(filename="test.dot", test.results, nlab, main = "A graph")
# system("fdp -T svg -o test.svg test.dot")
# Rgraphviz
gr <- GeneNet::network.make.graph( test.results, nlab)
gr
#> A graphNEL graph with directed edges
#> Number of Nodes = 20
#> Number of Edges = 38
num.nodes(gr)
#> [1] 20
edge.info(gr)
#> $weight
#> A~P B~Q D~T D~H E~I F~K F~N F~P
#> 0.62144 0.57165 -0.51638 -0.54659 -0.99976 -0.18859 -0.20179 0.26387
#> F~S H~N H~Q J~O K~L K~N K~M N~T
#> -0.56248 0.10034 0.40986 -0.99914 0.23367 0.36702 0.57442 0.13150
#> N~Q N~R P~Q
#> 0.26044 -0.34616 -0.09189
#>
#> $dir
#> A~P B~Q D~T D~H E~I F~K F~N F~P F~S H~N H~Q
#> "none" "none" "none" "none" "none" "none" "none" "none" "none" "none" "none"
#> J~O K~L K~N K~M N~T N~Q N~R P~Q
#> "none" "none" "none" "none" "none" "none" "none" "none"
gr2 <- GeneNet::network.make.graph( test.results, nlab, drop.singles=TRUE)
gr2
#> A graphNEL graph with directed edges
#> Number of Nodes = 18
#> Number of Edges = 38
GeneNet::num.nodes(gr2)
#> [1] 18
GeneNet::edge.info(gr2)
#> $weight
#> A~P B~Q D~T D~H E~I F~K F~N F~P
#> 0.62144 0.57165 -0.51638 -0.54659 -0.99976 -0.18859 -0.20179 0.26387
#> F~S H~N H~Q J~O K~L K~N K~M N~T
#> -0.56248 0.10034 0.40986 -0.99914 0.23367 0.36702 0.57442 0.13150
#> N~Q N~R P~Q
#> 0.26044 -0.34616 -0.09189
#>
#> $dir
#> A~P B~Q D~T D~H E~I F~K F~N F~P F~S H~N H~Q
#> "none" "none" "none" "none" "none" "none" "none" "none" "none" "none" "none"
#> J~O K~L K~N K~M N~T N~Q N~R P~Q
#> "none" "none" "none" "none" "none" "none" "none" "none"
# plot network
plot(gr, "fdp")
#> Warning in arrows(tail_from[1], tail_from[2], tail_to[1], tail_to[2], col =
#> edgeColor, : zero-length arrow is of indeterminate angle and so skipped
#> Warning in arrows(head_from[1], head_from[2], head_to[1], head_to[2], col =
#> edgeColor, : zero-length arrow is of indeterminate angle and so skipped
#> Warning in arrows(tail_from[1], tail_from[2], tail_to[1], tail_to[2], col =
#> edgeColor, : zero-length arrow is of indeterminate angle and so skipped
#> Warning in arrows(head_from[1], head_from[2], head_to[1], head_to[2], col =
#> edgeColor, : zero-length arrow is of indeterminate angle and so skipped
#> Warning in arrows(tail_from[1], tail_from[2], tail_to[1], tail_to[2], col =
#> edgeColor, : zero-length arrow is of indeterminate angle and so skipped
#> Warning in arrows(head_from[1], head_from[2], head_to[1], head_to[2], col =
#> edgeColor, : zero-length arrow is of indeterminate angle and so skipped
Figure 5.2: GeneNet example
plot(gr2, "fdp")
#> Warning in arrows(tail_from[1], tail_from[2], tail_to[1], tail_to[2], col =
#> edgeColor, : zero-length arrow is of indeterminate angle and so skipped
#> Warning in arrows(tail_from[1], tail_from[2], tail_to[1], tail_to[2], col =
#> edgeColor, : zero-length arrow is of indeterminate angle and so skipped
#> Warning in arrows(tail_from[1], tail_from[2], tail_to[1], tail_to[2], col =
#> edgeColor, : zero-length arrow is of indeterminate angle and so skipped
#> Warning in arrows(head_from[1], head_from[2], head_to[1], head_to[2], col =
#> edgeColor, : zero-length arrow is of indeterminate angle and so skipped
#> Warning in arrows(tail_from[1], tail_from[2], tail_to[1], tail_to[2], col =
#> edgeColor, : zero-length arrow is of indeterminate angle and so skipped
#> Warning in arrows(head_from[1], head_from[2], head_to[1], head_to[2], col =
#> edgeColor, : zero-length arrow is of indeterminate angle and so skipped
Figure 5.3: GeneNet example
A side-by-side heatmaps
set.seed(123454321)
m <- matrix(runif(2500),50)
r <- cor(m)
g <- as.matrix(r>=0.7)+0
f1 <- ComplexHeatmap::Heatmap(r)
f2 <- ComplexHeatmap::Heatmap(g)
f <- f1+f2
ComplexHeatmap::draw(f)
Figure 5.4: Heatmaps
df <- heatmaply::normalize(mtcars)
hm <- heatmaply::heatmaply(df,k_col=5,k_row=5,
colors = grDevices::colorRampPalette(RColorBrewer::brewer.pal(3, "RdBu"))(256))
htmlwidgets::saveWidget(hm,file="heatmaply.html")
htmltools::tags$iframe(src = "heatmaply.html", width = "100%", height = "550px")so we have heatmaply.html and a module analysis with WGCNA,
pwr <- c(1:10, seq(from=12, to=30, by=2))
sft <- WGCNA::pickSoftThreshold(dat, powerVector=pwr, verbose=5)
ADJ <- abs(cor(dat, method="pearson", use="pairwise.complete.obs"))^6
dissADJ <- 1-ADJ
dissTOM <- WGCNA::TOMdist(ADJ)
TOM <- WGCNA::TOMsimilarityFromExpr(dat)
Tree <- hclust(as.dist(1-TOM), method="average")
for(j in pwr)
{
pam_name <- paste0("pam",j)
assign(pam_name, cluster::pam(as.dist(dissADJ),j))
pamTOM_name <- paste0("pamTOM",j)
assign(pamTOM_name,cluster::pam(as.dist(dissTOM),j))
tc <- table(get(pam_name)$clustering,get(pamTOM_name)$clustering)
print(tc)
print(diag(tc))
}
colorStaticTOM <- as.character(WGCNA::cutreeStaticColor(Tree,cutHeight=.99,minSize=5))
colorDynamicTOM <- WGCNA::labels2colors(cutreeDynamic(Tree,method="tree",minClusterSize=5))
Colors <- data.frame(pamTOM6$clustering,colorStaticTOM,colorDynamicTOM)
WGCNA::plotDendroAndColors(Tree, Colors, dendroLabels=FALSE, hang=0.03, addGuide=TRUE, guideHang=0.05)
meg <- WGCNA::moduleEigengenes(dat, color=1:ncol(dat), softPower=6)6 Meta-data
This section is based on package recount3.
hs <- recount3::available_projects()
dim(subset(hs,file_source=="gtex"))
recount3::annotation_options("human")
blood_rse <- recount3::create_rse(subset(hs,project=="BLOOD"))
S4Vectors::metadata(blood_rse)
SummarizedExperiment::rowRanges(blood_rse)
colnames(SummarizedExperiment::colData(blood_rse))[1:20]
recount3::expand_sra_attributes(blood_rse)7 Pathway and enrichment analysis
reactome <- graphite::pathways("hsapiens", "reactome")
kegg <- graphite::pathways("hsapiens","kegg")
pharmgkb <- graphite::pathways("hsapiens","pharmgkb")
nodes(kegg[[21]])
#> [1] "ENTREZID:102724560" "ENTREZID:10993" "ENTREZID:113675"
#> [4] "ENTREZID:132158" "ENTREZID:1610" "ENTREZID:1738"
#> [7] "ENTREZID:1757" "ENTREZID:189" "ENTREZID:211"
#> [10] "ENTREZID:212" "ENTREZID:23464" "ENTREZID:2593"
#> [13] "ENTREZID:26227" "ENTREZID:2628" "ENTREZID:27232"
#> [16] "ENTREZID:2731" "ENTREZID:275" "ENTREZID:29958"
#> [19] "ENTREZID:29968" "ENTREZID:441531" "ENTREZID:501"
#> [22] "ENTREZID:51268" "ENTREZID:5223" "ENTREZID:5224"
#> [25] "ENTREZID:55349" "ENTREZID:5723" "ENTREZID:635"
#> [28] "ENTREZID:63826" "ENTREZID:6470" "ENTREZID:6472"
#> [31] "ENTREZID:64902" "ENTREZID:669" "ENTREZID:875"
#> [34] "ENTREZID:9380" "ENTREZID:1491"
kegg_t2g <- ldply(lapply(kegg, nodes), data.frame)
names(kegg_t2g) <- c("gs_name", "gene_symbol")
VEGF <- subset(kegg_t2g,gs_name=="VEGF signaling pathway")[[2]]
eKEGG <- clusterProfiler::enricher(gene=VEGF, TERM2GENE = kegg_t2g,
universe=,
pAdjustMethod = "BH",
pvalueCutoff = 0.1, qvalueCutoff = 0.05,
minGSSize = 10, maxGSSize = 500)8 Transcript databases
An overview of annotation is available2.
options(width=200)
# columns(org.Hs.eg.db)
# keyref <- keys(org.Hs.eg.db, keytype="ENTREZID")
# symbol_uniprot <- select(org.Hs.eg.db,keys=keyref,columns = c("SYMBOL","UNIPROT"))
# subset(symbol_uniprot,SYMBOL=="MC4R")
x <- EnsDb.Hsapiens.v86
ensembldb::listColumns(x, "protein", skip.keys=TRUE)
#> [1] "tx_id" "protein_id" "protein_sequence"
ensembldb::listGenebiotypes(x)
#> [1] "protein_coding" "unitary_pseudogene" "unprocessed_pseudogene" "processed_pseudogene" "processed_transcript"
#> [6] "transcribed_unprocessed_pseudogene" "antisense" "transcribed_unitary_pseudogene" "polymorphic_pseudogene" "lincRNA"
#> [11] "sense_intronic" "transcribed_processed_pseudogene" "sense_overlapping" "IG_V_pseudogene" "pseudogene"
#> [16] "TR_V_gene" "3prime_overlapping_ncRNA" "IG_V_gene" "bidirectional_promoter_lncRNA" "snRNA"
#> [21] "miRNA" "misc_RNA" "snoRNA" "rRNA" "Mt_tRNA"
#> [26] "Mt_rRNA" "IG_C_gene" "IG_J_gene" "TR_J_gene" "TR_C_gene"
#> [31] "TR_V_pseudogene" "TR_J_pseudogene" "IG_D_gene" "ribozyme" "IG_C_pseudogene"
#> [36] "TR_D_gene" "TEC" "IG_J_pseudogene" "scRNA" "scaRNA"
#> [41] "vaultRNA" "sRNA" "macro_lncRNA" "non_coding" "IG_pseudogene"
#> [46] "LRG_gene"
ensembldb::listTxbiotypes(x)
#> [1] "protein_coding" "processed_transcript" "nonsense_mediated_decay" "retained_intron" "unitary_pseudogene"
#> [6] "TEC" "miRNA" "misc_RNA" "non_stop_decay" "unprocessed_pseudogene"
#> [11] "processed_pseudogene" "transcribed_unprocessed_pseudogene" "lincRNA" "antisense" "transcribed_unitary_pseudogene"
#> [16] "polymorphic_pseudogene" "sense_intronic" "transcribed_processed_pseudogene" "sense_overlapping" "IG_V_pseudogene"
#> [21] "pseudogene" "TR_V_gene" "3prime_overlapping_ncRNA" "IG_V_gene" "bidirectional_promoter_lncRNA"
#> [26] "snRNA" "snoRNA" "rRNA" "Mt_tRNA" "Mt_rRNA"
#> [31] "IG_C_gene" "IG_J_gene" "TR_J_gene" "TR_C_gene" "TR_V_pseudogene"
#> [36] "TR_J_pseudogene" "IG_D_gene" "ribozyme" "IG_C_pseudogene" "TR_D_gene"
#> [41] "IG_J_pseudogene" "scRNA" "scaRNA" "vaultRNA" "sRNA"
#> [46] "macro_lncRNA" "non_coding" "IG_pseudogene" "LRG_gene"
ensembldb::listTables(x)
#> $gene
#> [1] "gene_id" "gene_name" "gene_biotype" "gene_seq_start" "gene_seq_end" "seq_name" "seq_strand" "seq_coord_system" "symbol"
#>
#> $tx
#> [1] "tx_id" "tx_biotype" "tx_seq_start" "tx_seq_end" "tx_cds_seq_start" "tx_cds_seq_end" "gene_id" "tx_name"
#>
#> $tx2exon
#> [1] "tx_id" "exon_id" "exon_idx"
#>
#> $exon
#> [1] "exon_id" "exon_seq_start" "exon_seq_end"
#>
#> $chromosome
#> [1] "seq_name" "seq_length" "is_circular"
#>
#> $protein
#> [1] "tx_id" "protein_id" "protein_sequence"
#>
#> $uniprot
#> [1] "protein_id" "uniprot_id" "uniprot_db" "uniprot_mapping_type"
#>
#> $protein_domain
#> [1] "protein_id" "protein_domain_id" "protein_domain_source" "interpro_accession" "prot_dom_start" "prot_dom_end"
#>
#> $entrezgene
#> [1] "gene_id" "entrezid"
#>
#> $metadata
#> [1] "name" "value"
ensembldb::metadata(x)
#> name value
#> 1 Db type EnsDb
#> 2 Type of Gene ID Ensembl Gene ID
#> 3 Supporting package ensembldb
#> 4 Db created by ensembldb package from Bioconductor
#> 5 script_version 0.3.0
#> 6 Creation time Thu May 18 16:32:27 2017
#> 7 ensembl_version 86
#> 8 ensembl_host localhost
#> 9 Organism homo_sapiens
#> 10 taxonomy_id 9606
#> 11 genome_build GRCh38
#> 12 DBSCHEMAVERSION 2.0
ensembldb::organism(x)
#> [1] "Homo sapiens"
ensembldb::returnFilterColumns(x)
#> [1] TRUE
ensembldb::seqinfo(x)
#> Seqinfo object with 357 sequences (1 circular) from GRCh38 genome:
#> seqnames seqlengths isCircular genome
#> X 156040895 FALSE GRCh38
#> 20 64444167 FALSE GRCh38
#> 1 248956422 FALSE GRCh38
#> 6 170805979 FALSE GRCh38
#> 3 198295559 FALSE GRCh38
#> ... ... ... ...
#> LRG_239 114904 FALSE GRCh38
#> LRG_311 115492 FALSE GRCh38
#> LRG_721 33396 FALSE GRCh38
#> LRG_741 231167 FALSE GRCh38
#> LRG_93 22459 FALSE GRCh38
ensembldb::seqlevels(x)
#> [1] "1" "10" "11" "12"
#> [5] "13" "14" "15" "16"
#> [9] "17" "18" "19" "2"
#> [13] "20" "21" "22" "3"
#> [17] "4" "5" "6" "7"
#> [21] "8" "9" "CHR_HG107_PATCH" "CHR_HG126_PATCH"
#> [25] "CHR_HG1311_PATCH" "CHR_HG1342_HG2282_PATCH" "CHR_HG1362_PATCH" "CHR_HG142_HG150_NOVEL_TEST"
#> [29] "CHR_HG151_NOVEL_TEST" "CHR_HG1651_PATCH" "CHR_HG1832_PATCH" "CHR_HG2021_PATCH"
#> [33] "CHR_HG2022_PATCH" "CHR_HG2023_PATCH" "CHR_HG2030_PATCH" "CHR_HG2058_PATCH"
#> [37] "CHR_HG2062_PATCH" "CHR_HG2063_PATCH" "CHR_HG2066_PATCH" "CHR_HG2072_PATCH"
#> [41] "CHR_HG2095_PATCH" "CHR_HG2104_PATCH" "CHR_HG2116_PATCH" "CHR_HG2128_PATCH"
#> [45] "CHR_HG2191_PATCH" "CHR_HG2213_PATCH" "CHR_HG2217_PATCH" "CHR_HG2232_PATCH"
#> [49] "CHR_HG2233_PATCH" "CHR_HG2235_PATCH" "CHR_HG2239_PATCH" "CHR_HG2247_PATCH"
#> [53] "CHR_HG2249_PATCH" "CHR_HG2288_HG2289_PATCH" "CHR_HG2290_PATCH" "CHR_HG2291_PATCH"
#> [57] "CHR_HG2334_PATCH" "CHR_HG26_PATCH" "CHR_HG986_PATCH" "CHR_HSCHR10_1_CTG1"
#> [61] "CHR_HSCHR10_1_CTG2" "CHR_HSCHR10_1_CTG3" "CHR_HSCHR10_1_CTG4" "CHR_HSCHR10_1_CTG6"
#> [65] "CHR_HSCHR11_1_CTG1_2" "CHR_HSCHR11_1_CTG5" "CHR_HSCHR11_1_CTG6" "CHR_HSCHR11_1_CTG7"
#> [69] "CHR_HSCHR11_1_CTG8" "CHR_HSCHR11_2_CTG1" "CHR_HSCHR11_2_CTG1_1" "CHR_HSCHR11_3_CTG1"
#> [73] "CHR_HSCHR12_1_CTG1" "CHR_HSCHR12_1_CTG2_1" "CHR_HSCHR12_2_CTG1" "CHR_HSCHR12_2_CTG2"
#> [77] "CHR_HSCHR12_2_CTG2_1" "CHR_HSCHR12_3_CTG2" "CHR_HSCHR12_3_CTG2_1" "CHR_HSCHR12_4_CTG2"
#> [81] "CHR_HSCHR12_4_CTG2_1" "CHR_HSCHR12_5_CTG2" "CHR_HSCHR12_5_CTG2_1" "CHR_HSCHR12_6_CTG2_1"
#> [85] "CHR_HSCHR13_1_CTG1" "CHR_HSCHR13_1_CTG3" "CHR_HSCHR13_1_CTG5" "CHR_HSCHR13_1_CTG8"
#> [89] "CHR_HSCHR14_1_CTG1" "CHR_HSCHR14_2_CTG1" "CHR_HSCHR14_3_CTG1" "CHR_HSCHR14_7_CTG1"
#> [93] "CHR_HSCHR15_1_CTG1" "CHR_HSCHR15_1_CTG3" "CHR_HSCHR15_1_CTG8" "CHR_HSCHR15_2_CTG3"
#> [97] "CHR_HSCHR15_2_CTG8" "CHR_HSCHR15_3_CTG3" "CHR_HSCHR15_3_CTG8" "CHR_HSCHR15_4_CTG8"
#> [101] "CHR_HSCHR15_5_CTG8" "CHR_HSCHR15_6_CTG8" "CHR_HSCHR16_1_CTG1" "CHR_HSCHR16_1_CTG3_1"
#> [105] "CHR_HSCHR16_2_CTG3_1" "CHR_HSCHR16_3_CTG1" "CHR_HSCHR16_4_CTG1" "CHR_HSCHR16_4_CTG3_1"
#> [109] "CHR_HSCHR16_5_CTG1" "CHR_HSCHR16_CTG2" "CHR_HSCHR17_10_CTG4" "CHR_HSCHR17_1_CTG1"
#> [113] "CHR_HSCHR17_1_CTG2" "CHR_HSCHR17_1_CTG4" "CHR_HSCHR17_1_CTG5" "CHR_HSCHR17_1_CTG9"
#> [117] "CHR_HSCHR17_2_CTG1" "CHR_HSCHR17_2_CTG2" "CHR_HSCHR17_2_CTG4" "CHR_HSCHR17_2_CTG5"
#> [121] "CHR_HSCHR17_3_CTG2" "CHR_HSCHR17_3_CTG4" "CHR_HSCHR17_4_CTG4" "CHR_HSCHR17_5_CTG4"
#> [125] "CHR_HSCHR17_6_CTG4" "CHR_HSCHR17_7_CTG4" "CHR_HSCHR17_8_CTG4" "CHR_HSCHR17_9_CTG4"
#> [129] "CHR_HSCHR18_1_CTG1_1" "CHR_HSCHR18_1_CTG2_1" "CHR_HSCHR18_2_CTG1_1" "CHR_HSCHR18_2_CTG2"
#> [133] "CHR_HSCHR18_2_CTG2_1" "CHR_HSCHR18_3_CTG2_1" "CHR_HSCHR18_5_CTG1_1" "CHR_HSCHR18_ALT21_CTG2_1"
#> [137] "CHR_HSCHR18_ALT2_CTG2_1" "CHR_HSCHR19KIR_ABC08_A1_HAP_CTG3_1" "CHR_HSCHR19KIR_ABC08_AB_HAP_C_P_CTG3_1" "CHR_HSCHR19KIR_ABC08_AB_HAP_T_P_CTG3_1"
#> [141] "CHR_HSCHR19KIR_FH05_A_HAP_CTG3_1" "CHR_HSCHR19KIR_FH05_B_HAP_CTG3_1" "CHR_HSCHR19KIR_FH06_A_HAP_CTG3_1" "CHR_HSCHR19KIR_FH06_BA1_HAP_CTG3_1"
#> [145] "CHR_HSCHR19KIR_FH08_A_HAP_CTG3_1" "CHR_HSCHR19KIR_FH08_BAX_HAP_CTG3_1" "CHR_HSCHR19KIR_FH13_A_HAP_CTG3_1" "CHR_HSCHR19KIR_FH13_BA2_HAP_CTG3_1"
#> [149] "CHR_HSCHR19KIR_FH15_A_HAP_CTG3_1" "CHR_HSCHR19KIR_FH15_B_HAP_CTG3_1" "CHR_HSCHR19KIR_G085_A_HAP_CTG3_1" "CHR_HSCHR19KIR_G085_BA1_HAP_CTG3_1"
#> [153] "CHR_HSCHR19KIR_G248_A_HAP_CTG3_1" "CHR_HSCHR19KIR_G248_BA2_HAP_CTG3_1" "CHR_HSCHR19KIR_GRC212_AB_HAP_CTG3_1" "CHR_HSCHR19KIR_GRC212_BA1_HAP_CTG3_1"
#> [157] "CHR_HSCHR19KIR_LUCE_A_HAP_CTG3_1" "CHR_HSCHR19KIR_LUCE_BDEL_HAP_CTG3_1" "CHR_HSCHR19KIR_RP5_B_HAP_CTG3_1" "CHR_HSCHR19KIR_RSH_A_HAP_CTG3_1"
#> [161] "CHR_HSCHR19KIR_RSH_BA2_HAP_CTG3_1" "CHR_HSCHR19KIR_T7526_A_HAP_CTG3_1" "CHR_HSCHR19KIR_T7526_BDEL_HAP_CTG3_1" "CHR_HSCHR19LRC_COX1_CTG3_1"
#> [165] "CHR_HSCHR19LRC_COX2_CTG3_1" "CHR_HSCHR19LRC_LRC_I_CTG3_1" "CHR_HSCHR19LRC_LRC_J_CTG3_1" "CHR_HSCHR19LRC_LRC_S_CTG3_1"
#> [169] "CHR_HSCHR19LRC_LRC_T_CTG3_1" "CHR_HSCHR19LRC_PGF1_CTG3_1" "CHR_HSCHR19LRC_PGF2_CTG3_1" "CHR_HSCHR19_1_CTG2"
#> [173] "CHR_HSCHR19_1_CTG3_1" "CHR_HSCHR19_2_CTG2" "CHR_HSCHR19_2_CTG3_1" "CHR_HSCHR19_3_CTG2"
#> [177] "CHR_HSCHR19_3_CTG3_1" "CHR_HSCHR19_4_CTG2" "CHR_HSCHR19_4_CTG3_1" "CHR_HSCHR19_5_CTG2"
#> [181] "CHR_HSCHR1_1_CTG11" "CHR_HSCHR1_1_CTG3" "CHR_HSCHR1_1_CTG31" "CHR_HSCHR1_1_CTG32_1"
#> [185] "CHR_HSCHR1_2_CTG3" "CHR_HSCHR1_2_CTG31" "CHR_HSCHR1_2_CTG32_1" "CHR_HSCHR1_3_CTG3"
#> [189] "CHR_HSCHR1_3_CTG31" "CHR_HSCHR1_3_CTG32_1" "CHR_HSCHR1_4_CTG3" "CHR_HSCHR1_4_CTG31"
#> [193] "CHR_HSCHR1_5_CTG3" "CHR_HSCHR1_5_CTG32_1" "CHR_HSCHR1_ALT2_1_CTG32_1" "CHR_HSCHR20_1_CTG1"
#> [197] "CHR_HSCHR20_1_CTG2" "CHR_HSCHR20_1_CTG3" "CHR_HSCHR20_1_CTG4" "CHR_HSCHR21_2_CTG1_1"
#> [201] "CHR_HSCHR21_3_CTG1_1" "CHR_HSCHR21_4_CTG1_1" "CHR_HSCHR21_5_CTG2" "CHR_HSCHR21_6_CTG1_1"
#> [205] "CHR_HSCHR21_8_CTG1_1" "CHR_HSCHR22_1_CTG1" "CHR_HSCHR22_1_CTG2" "CHR_HSCHR22_1_CTG3"
#> [209] "CHR_HSCHR22_1_CTG4" "CHR_HSCHR22_1_CTG5" "CHR_HSCHR22_1_CTG6" "CHR_HSCHR22_1_CTG7"
#> [213] "CHR_HSCHR22_2_CTG1" "CHR_HSCHR22_3_CTG1" "CHR_HSCHR22_4_CTG1" "CHR_HSCHR22_5_CTG1"
#> [217] "CHR_HSCHR22_6_CTG1" "CHR_HSCHR22_7_CTG1" "CHR_HSCHR22_8_CTG1" "CHR_HSCHR2_1_CTG1"
#> [221] "CHR_HSCHR2_1_CTG15" "CHR_HSCHR2_1_CTG5" "CHR_HSCHR2_1_CTG7" "CHR_HSCHR2_1_CTG7_2"
#> [225] "CHR_HSCHR2_2_CTG1" "CHR_HSCHR2_2_CTG15" "CHR_HSCHR2_2_CTG7" "CHR_HSCHR2_2_CTG7_2"
#> [229] "CHR_HSCHR2_3_CTG1" "CHR_HSCHR2_3_CTG15" "CHR_HSCHR2_3_CTG7_2" "CHR_HSCHR2_4_CTG1"
#> [233] "CHR_HSCHR2_6_CTG7_2" "CHR_HSCHR3_1_CTG1" "CHR_HSCHR3_1_CTG2_1" "CHR_HSCHR3_1_CTG3"
#> [237] "CHR_HSCHR3_2_CTG2_1" "CHR_HSCHR3_2_CTG3" "CHR_HSCHR3_3_CTG1" "CHR_HSCHR3_3_CTG3"
#> [241] "CHR_HSCHR3_4_CTG2_1" "CHR_HSCHR3_4_CTG3" "CHR_HSCHR3_5_CTG2_1" "CHR_HSCHR3_5_CTG3"
#> [245] "CHR_HSCHR3_6_CTG3" "CHR_HSCHR3_7_CTG3" "CHR_HSCHR3_8_CTG3" "CHR_HSCHR3_9_CTG3"
#> [249] "CHR_HSCHR4_11_CTG12" "CHR_HSCHR4_1_CTG12" "CHR_HSCHR4_1_CTG4" "CHR_HSCHR4_1_CTG6"
#> [253] "CHR_HSCHR4_1_CTG9" "CHR_HSCHR4_2_CTG12" "CHR_HSCHR4_2_CTG4" "CHR_HSCHR4_3_CTG12"
#> [257] "CHR_HSCHR4_4_CTG12" "CHR_HSCHR4_5_CTG12" "CHR_HSCHR4_6_CTG12" "CHR_HSCHR4_7_CTG12"
#> [261] "CHR_HSCHR4_8_CTG12" "CHR_HSCHR4_9_CTG12" "CHR_HSCHR5_1_CTG1" "CHR_HSCHR5_1_CTG1_1"
#> [265] "CHR_HSCHR5_1_CTG5" "CHR_HSCHR5_2_CTG1" "CHR_HSCHR5_2_CTG1_1" "CHR_HSCHR5_2_CTG5"
#> [269] "CHR_HSCHR5_3_CTG1" "CHR_HSCHR5_3_CTG5" "CHR_HSCHR5_4_CTG1" "CHR_HSCHR5_4_CTG1_1"
#> [273] "CHR_HSCHR5_5_CTG1" "CHR_HSCHR5_6_CTG1" "CHR_HSCHR5_7_CTG1" "CHR_HSCHR6_1_CTG10"
#> [277] "CHR_HSCHR6_1_CTG2" "CHR_HSCHR6_1_CTG3" "CHR_HSCHR6_1_CTG4" "CHR_HSCHR6_1_CTG5"
#> [281] "CHR_HSCHR6_1_CTG6" "CHR_HSCHR6_1_CTG7" "CHR_HSCHR6_1_CTG8" "CHR_HSCHR6_1_CTG9"
#> [285] "CHR_HSCHR6_8_CTG1" "CHR_HSCHR6_MHC_APD_CTG1" "CHR_HSCHR6_MHC_COX_CTG1" "CHR_HSCHR6_MHC_DBB_CTG1"
#> [289] "CHR_HSCHR6_MHC_MANN_CTG1" "CHR_HSCHR6_MHC_MCF_CTG1" "CHR_HSCHR6_MHC_QBL_CTG1" "CHR_HSCHR6_MHC_SSTO_CTG1"
#> [293] "CHR_HSCHR7_1_CTG1" "CHR_HSCHR7_1_CTG4_4" "CHR_HSCHR7_1_CTG6" "CHR_HSCHR7_1_CTG7"
#> [297] "CHR_HSCHR7_2_CTG1" "CHR_HSCHR7_2_CTG4_4" "CHR_HSCHR7_2_CTG6" "CHR_HSCHR7_2_CTG7"
#> [301] "CHR_HSCHR7_3_CTG6" "CHR_HSCHR8_1_CTG1" "CHR_HSCHR8_1_CTG6" "CHR_HSCHR8_1_CTG7"
#> [305] "CHR_HSCHR8_2_CTG1" "CHR_HSCHR8_2_CTG7" "CHR_HSCHR8_3_CTG1" "CHR_HSCHR8_3_CTG7"
#> [309] "CHR_HSCHR8_4_CTG1" "CHR_HSCHR8_4_CTG7" "CHR_HSCHR8_5_CTG1" "CHR_HSCHR8_5_CTG7"
#> [313] "CHR_HSCHR8_6_CTG1" "CHR_HSCHR8_7_CTG1" "CHR_HSCHR8_8_CTG1" "CHR_HSCHR8_9_CTG1"
#> [317] "CHR_HSCHR9_1_CTG1" "CHR_HSCHR9_1_CTG2" "CHR_HSCHR9_1_CTG3" "CHR_HSCHR9_1_CTG4"
#> [321] "CHR_HSCHR9_1_CTG5" "CHR_HSCHR9_1_CTG6" "CHR_HSCHRX_1_CTG3" "CHR_HSCHRX_2_CTG12"
#> [325] "CHR_HSCHRX_2_CTG3" "GL000009.2" "GL000194.1" "GL000195.1"
#> [329] "GL000205.2" "GL000213.1" "GL000216.2" "GL000218.1"
#> [333] "GL000219.1" "GL000220.1" "GL000225.1" "KI270442.1"
#> [337] "KI270711.1" "KI270713.1" "KI270721.1" "KI270726.1"
#> [341] "KI270727.1" "KI270728.1" "KI270731.1" "KI270733.1"
#> [345] "KI270734.1" "KI270744.1" "KI270750.1" "LRG_183"
#> [349] "LRG_187" "LRG_239" "LRG_311" "LRG_721"
#> [353] "LRG_741" "LRG_93" "MT" "X"
#> [357] "Y"
ensembldb::updateEnsDb(x)
#> EnsDb for Ensembl:
#> |Backend: SQLite
#> |Db type: EnsDb
#> |Type of Gene ID: Ensembl Gene ID
#> |Supporting package: ensembldb
#> |Db created by: ensembldb package from Bioconductor
#> |script_version: 0.3.0
#> |Creation time: Thu May 18 16:32:27 2017
#> |ensembl_version: 86
#> |ensembl_host: localhost
#> |Organism: homo_sapiens
#> |taxonomy_id: 9606
#> |genome_build: GRCh38
#> |DBSCHEMAVERSION: 2.0
#> | No. of genes: 63970.
#> | No. of transcripts: 216741.
#> |Protein data available.
ensembldb::genes(x, columns=c("gene_name"),
filter=list(SeqNameFilter("X"), AnnotationFilter::GeneBiotypeFilter("protein_coding")))
#> GRanges object with 841 ranges and 3 metadata columns:
#> seqnames ranges strand | gene_name gene_id gene_biotype
#> <Rle> <IRanges> <Rle> | <character> <character> <character>
#> ENSG00000182378 X 276322-303356 + | PLCXD1 ENSG00000182378 protein_coding
#> ENSG00000178605 X 304529-318819 - | GTPBP6 ENSG00000178605 protein_coding
#> ENSG00000167393 X 333963-386955 - | PPP2R3B ENSG00000167393 protein_coding
#> ENSG00000185960 X 624344-659411 + | SHOX ENSG00000185960 protein_coding
#> ENSG00000205755 X 1187549-1212750 - | CRLF2 ENSG00000205755 protein_coding
#> ... ... ... ... . ... ... ...
#> ENSG00000277745 X 155459415-155460005 - | H2AFB3 ENSG00000277745 protein_coding
#> ENSG00000185973 X 155490115-155669944 - | TMLHE ENSG00000185973 protein_coding
#> ENSG00000168939 X 155767812-155782459 + | SPRY3 ENSG00000168939 protein_coding
#> ENSG00000124333 X 155881293-155943769 + | VAMP7 ENSG00000124333 protein_coding
#> ENSG00000124334 X 155997581-156010817 + | IL9R ENSG00000124334 protein_coding
#> -------
#> seqinfo: 1 sequence from GRCh38 genome
ensembldb ::transcripts(x, columns=ensembldb::listColumns(x, "tx"),
filter = AnnotationFilter::AnnotationFilterList(), order.type = "asc", return.type = "GRanges")
#> GRanges object with 216741 ranges and 6 metadata columns:
#> seqnames ranges strand | tx_id tx_biotype tx_cds_seq_start tx_cds_seq_end gene_id tx_name
#> <Rle> <IRanges> <Rle> | <character> <character> <integer> <integer> <character> <character>
#> ENST00000456328 1 11869-14409 + | ENST00000456328 processed_transcript <NA> <NA> ENSG00000223972 ENST00000456328
#> ENST00000450305 1 12010-13670 + | ENST00000450305 transcribed_unproces.. <NA> <NA> ENSG00000223972 ENST00000450305
#> ENST00000488147 1 14404-29570 - | ENST00000488147 unprocessed_pseudogene <NA> <NA> ENSG00000227232 ENST00000488147
#> ENST00000619216 1 17369-17436 - | ENST00000619216 miRNA <NA> <NA> ENSG00000278267 ENST00000619216
#> ENST00000473358 1 29554-31097 + | ENST00000473358 lincRNA <NA> <NA> ENSG00000243485 ENST00000473358
#> ... ... ... ... . ... ... ... ... ... ...
#> ENST00000420810 Y 26549425-26549743 + | ENST00000420810 processed_pseudogene <NA> <NA> ENSG00000224240 ENST00000420810
#> ENST00000456738 Y 26586642-26591601 - | ENST00000456738 unprocessed_pseudogene <NA> <NA> ENSG00000227629 ENST00000456738
#> ENST00000435945 Y 26594851-26634652 - | ENST00000435945 unprocessed_pseudogene <NA> <NA> ENSG00000237917 ENST00000435945
#> ENST00000435741 Y 26626520-26627159 - | ENST00000435741 processed_pseudogene <NA> <NA> ENSG00000231514 ENST00000435741
#> ENST00000431853 Y 56855244-56855488 + | ENST00000431853 processed_pseudogene <NA> <NA> ENSG00000235857 ENST00000431853
#> -------
#> seqinfo: 357 sequences (1 circular) from GRCh38 genome
txdbEnsemblGRCh38 <- GenomicFeatures::makeTxDbFromEnsembl(organism="Homo sapiens", release=98)
#> Warning in call_fun_in_txdbmaker("makeTxDbFromEnsembl", ...): makeTxDbFromEnsembl() has moved to the txdbmaker package. Please call txdbmaker::makeTxDbFromEnsembl() to get rid of this warning.
#> Fetch transcripts and genes from Ensembl ... OK
#> (fetched 250194 transcripts from 67946 genes)
#> Fetch exons and CDS from Ensembl ... OK
#> Fetch chromosome names and lengths from Ensembl ...OK
#> Gather the metadata ... OK
#> Make the TxDb object ... OK
txdb <- as.list(txdbEnsemblGRCh38)
lapply(txdb,head)
#> $transcripts
#> tx_id tx_name tx_type tx_chrom tx_strand tx_start tx_end
#> 1 1 ENST00000636745 lncRNA CHR_HG107_PATCH + 1049876 1055745
#> 2 2 ENST00000636387 lncRNA CHR_HG107_PATCH + 1052607 1055745
#> 3 3 ENST00000643422 protein_coding CHR_HG107_PATCH + 1075018 1112365
#> 4 4 ENST00000645631 protein_coding CHR_HG107_PATCH + 1075018 1112365
#> 5 5 ENST00000636567 protein_coding CHR_HG107_PATCH + 1159911 1203106
#> 6 6 ENST00000636545 protein_coding CHR_HG107_PATCH - 1012823 1036718
#>
#> $splicings
#> tx_id exon_rank exon_id exon_name exon_chrom exon_strand exon_start exon_end cds_id cds_name cds_start cds_end
#> 1 1 1 1 ENSE00003797146 CHR_HG107_PATCH + 1049876 1049958 NA <NA> NA NA
#> 2 1 2 2 ENSE00003795151 CHR_HG107_PATCH + 1051619 1051839 NA <NA> NA NA
#> 3 1 3 4 ENSE00003793692 CHR_HG107_PATCH + 1054235 1054388 NA <NA> NA NA
#> 4 1 4 5 ENSE00003797325 CHR_HG107_PATCH + 1055110 1055745 NA <NA> NA NA
#> 5 2 1 3 ENSE00003798310 CHR_HG107_PATCH + 1052607 1055745 NA <NA> NA NA
#> 6 3 1 6 ENSE00003815958 CHR_HG107_PATCH + 1075018 1075093 1 ENSP00000494473 1075018 1075093
#>
#> $genes
#> tx_id gene_id
#> 1 1 ENSG00000283640
#> 2 2 ENSG00000283640
#> 3 3 ENSG00000284971
#> 4 4 ENSG00000284971
#> 5 5 ENSG00000283158
#> 6 6 ENSG00000283350
#>
#> $chrominfo
#> chrom length is_circular
#> 1 CHR_HG107_PATCH 135088590 NA
#> 2 CHR_HG109_PATCH 58617934 NA
#> 3 CHR_HG126_PATCH 198295908 NA
#> 4 CHR_HG1277_PATCH 133754853 NA
#> 5 CHR_HG1296_PATCH 190208697 NA
#> 6 CHR_HG1298_PATCH 190196285 NA
txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene
# liverExprs <- quantifyExpressionsFromBWs(txdb = txdb,BWfiles=,experimentalDesign=)10 Bioconductor/CRAN packages
| Package | Description |
|---|---|
| Bioconductor | |
| AnnotationDbi | AnnotationDb objects and their progeny, methods etc. |
| Biobase | Base functions for Bioconductor |
| clusterProfiler | Functional profiles for genes and gene clusters |
| ComplexHeatmap | Make complex heatmaps |
| DESSeq2 | Differential gene expression analysis based on the negative binomial distribution |
| edgeR | Empirical analysis of digital gene expression |
| EnsDb.Hsapiens.v86 | Exposes an annotation databases generated from Ensembl |
| ensembldb | Retrieve annotation data from an Ensembl based package |
| FlowSorted.DLPFC.450k | Illumina HumanMethylation data on sorted frontal cortex cell populations |
| graphite | GRAPH Interaction from pathway topological environment |
| IlluminaHumanMethylation450kmanifest | Annotation for Illumina’s 450k methylation arrays |
| INSPEcT | Quantification of the intronic and exonic gene features and the post-transcriptional regulation analysis |
| org.Hs.eg.db | Conversion of Entrez ID – gene symbols |
| OUTRIDER | OUTlier in RNA-Seq fInDER |
| Pi | Priority index, leveraging genetic evidence to prioritise drug targets at the gene and pathway level |
| quantro | A test for when to use quantile normalisation |
| recount3 | Interface to uniformly processed RNA-seq data |
| Rgraphiz | Interfaces R with the AT&T graphviz library for plotting R graph objects from the graph package |
| sva | Surrogate Variable Analysis |
| TxDb.Hsapiens.UCSC.hg38.knownGene | Annotation of the human genome |
| CRAN | |
| doParallel | Foreach Parallel Adaptor for the ‘parallel’ Package |
| GeneNet | Modeling and Inferring Gene Networks |
| ggplot2 | Data Visualisations Using the grammar of graphics |
| heatmaply | Interactive Cluster Heat Maps Using plotly and ggplot2
|
| pheatmap | results visualisation |
| plyr | Splitting, applying and combining data |
| RColorBrewer | ColorBrewer Palettes |
| WGCNA | Weighted correlation network analysis |
References
1.
Köster, J., Forster, J., Schmeier, S. & Salazar, V. snakemake-workflows/rna-seq-star-deseq2: Version 1.2.0. (2021) doi:10.5281/zenodo.5245549.
2.
Carlson, M. R. J., Pagès, H., Arora, S., Obenchain, V. & Morgan, M. Genomic annotation resources in R/Bioconductor. in Statistical genomics: Methods and protocols (eds. Mathé, E. & Davis, S.) 67–69 (Springer New York, New York, NY, 2016). doi:10.1007/978-1-4939-3578-9_4.