This article collects notes on Bioconductor packages, made available here to faciliate their use and extensions.
pkgs <- c("AnnotationDbi", "ComplexHeatmap", "DESeq2", "EnsDb.Hsapiens.v86",
"FlowSorted.DLPFC.450k", "GeneNet", "GenomicFeatures", "RColorBrewer",
"RMariaDB", "Rgraphviz", "S4Vectors", "SummarizedExperiment",
"TxDb.Hsapiens.UCSC.hg38.knownGene", "bladderbatch", "clusterProfiler",
"corpcor", "ensembldb", "fdrtool", "graph", "graphite", "heatmaply",
"minfi", "org.Hs.eg.db", "plyr", "quantro", "recount3", "sva")
for (p in pkgs) if (length(grep(paste("^package:", p, "$", sep=""), search())) == 0) {
if (!requireNamespace(p)) warning(paste0("This vignette needs package `", p, "'; please install"))
}
invisible(suppressMessages(lapply(pkgs, require, character.only = TRUE)))1 Normalisation
1.1 ComBat
This is the documentation example, based on Bioconductor 3.14.
data(bladderdata)
dat <- bladderEset[1:50,]
pheno = pData(dat)
edata = exprs(dat)
batch = pheno$batch
mod = model.matrix(~as.factor(cancer), data=pheno)
# 1. parametric adjustment
combat_edata1 = ComBat(dat=edata, batch=batch, mod=NULL, 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
#> Finding parametric adjustments
#> Adjusting the Data
# 2. non-parametric adjustment, mean-only version
combat_edata2 = ComBat(dat=edata, batch=batch, mod=NULL, 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
combat_edata3 = ComBat(dat=edata, 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
#> Finding parametric adjustments
#> Adjusting the Data
1.2 quantro
Again, this is adapted from the package vignette. noting that FlowSorted.DLPFC.450k instead of FlowSorted is used.
p <- getBeta(FlowSorted.DLPFC.450k,offset=100)
#> Loading required package: IlluminaHumanMethylation450kmanifest
pd <- pData(FlowSorted.DLPFC.450k)
matdensity(p, groupFactor = pd$CellType, xlab = " ", ylab = "density",
main = "Beta Values", brewer.n = 8, brewer.name = "Dark2")
legend('top', c("NeuN_neg", "NeuN_pos"), col = c(1, 2), lty = 1, lwd = 3)
matboxplot(p, groupFactor = pd$CellType, xaxt = "n", main = "Beta Values")
qtest <- quantro(object = p, groupFactor = pd$CellType)
#> [quantro] Average medians of the distributions are
#> not equal across groups.
#> [quantro] Calculating the quantro test statistic.
#> [quantro] No permutation testing performed.
#> Use B > 0 for permutation testing.
# commented since `R CMD check` returns error with multithreads
# registerDoParallel(cores=10)
# qtestPerm <- quantro(p, groupFactor = pd$CellType, B = 1000)
# qtestPerm
# quantroPlot(qtestPerm)2 Differential expression
ex <- makeExampleDESeqDataSet(m=4)
dds <- DESeq(ex)
#> estimating size factors
#> estimating dispersions
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> final dispersion estimates
#> fitting model and testing
res <- results(dds, contrast=c("condition","B","A"))
rld <- rlogTransformation(ex, blind=TRUE)
dat <- plotPCA(rld, intgroup=c("condition"),returnData=TRUE)
percentVar <- round(100 * attr(dat,"percentVar"))
ggplot(dat, aes(PC1, PC2, color=condition, shape=condition)) +
geom_point(size=3) +
xlab(paste0("PC1:",percentVar[1],"% variance")) +
ylab(paste0("PC2:",percentVar[2],"% variance"))
ex$condition <- relevel(ex$condition, ref="B")
dds2 <- 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 <- results(dds2)
knitr::kable(head(as.data.frame(res)))| baseMean | log2FoldChange | lfcSE | stat | pvalue | padj | |
|---|---|---|---|---|---|---|
| gene1 | 2.977665 | -3.5158300 | 2.710881 | -1.2969326 | 0.1946544 | 0.995703 |
| gene2 | 78.159032 | 0.7832720 | 0.601392 | 1.3024316 | 0.1927689 | 0.995703 |
| gene3 | 5.881795 | -1.3195039 | 1.926098 | -0.6850659 | 0.4933024 | 0.995703 |
| gene4 | 7.554798 | -0.5325548 | 1.456830 | -0.3655573 | 0.7146955 | 0.995703 |
| gene5 | 2.423358 | 1.9512279 | 2.971140 | 0.6567271 | 0.5113564 | 0.995703 |
| gene6 | 17.405397 | 0.4217535 | 1.038947 | 0.4059431 | 0.6847844 | 0.995703 |
See the package in action from a snakemake workflow1.
3 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 <- ggm.simulate.pcor(40)
# A data set with 40 observations
m.sim <- ggm.simulate.data(40, true.pcor)
# A simple estimate of partial correlations
estimated.pcor <- cor2pcor( cor(m.sim) )
# A comparison of estimated and true values
sum((true.pcor-estimated.pcor)^2)
#> [1] 666.9864
# A slightly better estimate ...
estimated.pcor.2 <- ggm.estimate.pcor(m.sim)
#> Estimating optimal shrinkage intensity lambda (correlation matrix): 0.1726
sum((true.pcor-estimated.pcor.2)^2)
#> [1] 9.37608
## ecoli data
data(ecoli)
# partial correlation matrix
inferred.pcor <- 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 <- 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
# 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 1.120812e-09 1.093997e-06 0.9999516
#> 6 0.12594697 21 72 1.103836e-08 8.978563e-06 0.9998400
#> 7 0.11956105 28 86 5.890924e-08 3.853590e-05 0.9998400
#> 8 -0.11723897 26 80 1.060526e-07 5.816172e-05 0.9998400
#> 9 -0.11711625 72 89 1.093655e-07 5.930499e-05 0.9972804
#> 10 0.10658013 20 21 1.366610e-06 5.925275e-04 0.9972804
#> 11 0.10589778 21 73 1.596859e-06 6.678429e-04 0.9972804
#> 12 0.10478689 20 91 2.053403e-06 8.024425e-04 0.9972804
#> 13 0.10420836 7 52 2.338382e-06 8.778605e-04 0.9944557
#> 14 0.10236077 87 95 3.525186e-06 1.224964e-03 0.9944557
#> 15 0.10113550 27 95 4.610444e-06 1.500047e-03 0.9920084
#> 16 0.09928954 21 51 6.868357e-06 2.046549e-03 0.9920084
#> 17 0.09791914 21 88 9.192373e-06 2.520616e-03 0.9920084
#> 18 0.09719685 18 95 1.070232e-05 2.790102e-03 0.9920084
#> 19 0.09621791 28 90 1.313007e-05 3.171817e-03 0.9920084
#> 20 0.09619099 12 80 1.320374e-05 3.182526e-03 0.9920084
# network containing edges with prob > 0.9 (i.e. local fdr < 0.1)
net <- 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 1.120812e-09 1.093997e-06 0.9999516
#> 6 0.12594697 21 72 1.103836e-08 8.978563e-06 0.9998400
#> 7 0.11956105 28 86 5.890924e-08 3.853590e-05 0.9998400
#> 8 -0.11723897 26 80 1.060526e-07 5.816172e-05 0.9998400
#> 9 -0.11711625 72 89 1.093655e-07 5.930499e-05 0.9972804
#> 10 0.10658013 20 21 1.366610e-06 5.925275e-04 0.9972804
#> 11 0.10589778 21 73 1.596859e-06 6.678429e-04 0.9972804
#> 12 0.10478689 20 91 2.053403e-06 8.024425e-04 0.9972804
#> 13 0.10420836 7 52 2.338382e-06 8.778605e-04 0.9944557
#> 14 0.10236077 87 95 3.525186e-06 1.224964e-03 0.9944557
#> 15 0.10113550 27 95 4.610444e-06 1.500047e-03 0.9920084
#> 16 0.09928954 21 51 6.868357e-06 2.046549e-03 0.9920084
#> 17 0.09791914 21 88 9.192373e-06 2.520616e-03 0.9920084
#> 18 0.09719685 18 95 1.070232e-05 2.790102e-03 0.9920084
#> 19 0.09621791 28 90 1.313007e-05 3.171817e-03 0.9920084
#> 20 0.09619099 12 80 1.320374e-05 3.182526e-03 0.9920084
#> 21 0.09576091 89 95 1.443542e-05 3.354777e-03 0.9891317
#> 22 0.09473210 7 51 1.784126e-05 3.864825e-03 0.9891317
#> 23 -0.09386896 53 58 2.127622e-05 4.313590e-03 0.9891317
#> 24 -0.09366615 29 83 2.217013e-05 4.421099e-03 0.9891317
#> 25 -0.09341148 21 89 2.334321e-05 4.556947e-03 0.9810727
#> 26 -0.09156391 49 93 3.380043e-05 5.955972e-03 0.9810727
#> 27 -0.09150710 80 90 3.418363e-05 6.002083e-03 0.9810727
#> 28 0.09101505 7 53 3.767966e-05 6.408102e-03 0.9810727
#> 29 0.09050688 21 84 4.164471e-05 6.838782e-03 0.9810727
#> 30 0.08965490 72 73 4.919365e-05 7.581866e-03 0.9810727
#> 31 -0.08934025 29 99 5.229604e-05 7.861416e-03 0.9810727
#> 32 -0.08906819 9 95 5.512708e-05 8.104759e-03 0.9810727
#> 33 0.08888345 2 49 5.713144e-05 8.270673e-03 0.9810727
#> 34 0.08850681 86 90 6.143363e-05 8.610161e-03 0.9810727
#> 35 0.08805868 17 53 6.695170e-05 9.015175e-03 0.9810727
#> 36 0.08790809 28 48 6.890884e-05 9.151291e-03 0.9810727
#> 37 0.08783471 33 58 6.988211e-05 9.217597e-03 0.9682377
#> 38 -0.08705796 7 49 8.101244e-05 1.021362e-02 0.9682377
#> 39 0.08645033 20 46 9.086547e-05 1.102466e-02 0.9682377
#> 40 0.08609950 48 86 9.705862e-05 1.150392e-02 0.9682377
#> 41 0.08598769 21 52 9.911458e-05 1.165816e-02 0.9682377
#> 42 0.08555275 32 95 1.075099e-04 1.226435e-02 0.9682377
#> 43 0.08548231 17 51 1.089311e-04 1.236337e-02 0.9424721
#> 44 0.08470370 80 83 1.258659e-04 1.382356e-02 0.9424721
#> 45 0.08442510 80 82 1.325062e-04 1.437068e-02 0.9174573
#> 46 0.08271606 80 93 1.810275e-04 1.845632e-02 0.9174573
#> 47 0.08235175 46 91 1.933329e-04 1.941579e-02 0.9174573
#> 48 0.08217787 25 95 1.994788e-04 1.988432e-02 0.9174573
#> 49 -0.08170331 29 87 2.171999e-04 2.119715e-02 0.9174573
#> 50 0.08123632 19 29 2.360716e-04 2.253606e-02 0.9174573
#> 51 0.08101702 51 84 2.454547e-04 2.318024e-02 0.9174573
#> 52 0.08030748 16 93 2.782643e-04 2.532796e-02 0.9174573
#> 53 0.08006503 28 52 2.903870e-04 2.608271e-02 0.9174573
#> 54 -0.07941656 41 80 3.252833e-04 2.814824e-02 0.9174573
#> 55 0.07941410 54 89 3.254229e-04 2.815620e-02 0.9174573
#> 56 -0.07934653 28 80 3.292784e-04 2.837511e-02 0.9174573
#> 57 0.07916783 29 92 3.396802e-04 2.895702e-02 0.9174573
#> 58 -0.07866905 17 86 3.703635e-04 3.060293e-02 0.9174573
#> 59 0.07827749 16 29 3.962446e-04 3.191462e-02 0.9174573
#> 60 -0.07808262 73 89 4.097452e-04 3.257290e-02 0.9174573
#> 61 0.07766261 52 67 4.403165e-04 3.400207e-02 0.9174573
#> 62 0.07762917 25 87 4.428396e-04 3.411637e-02 0.9174573
#> 63 -0.07739378 9 93 4.609872e-04 3.492295e-02 0.9174573
#> 64 0.07738885 31 80 4.613747e-04 3.493988e-02 0.9174573
#> 65 -0.07718681 80 94 4.775136e-04 3.563444e-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 1.120812e-09 1.093997e-06 0.9999516
#> 6 0.12594697 21 72 1.103836e-08 8.978563e-06 0.9998400
#> 7 0.11956105 28 86 5.890924e-08 3.853590e-05 0.9998400
#> 8 -0.11723897 26 80 1.060526e-07 5.816172e-05 0.9998400
#> 9 -0.11711625 72 89 1.093655e-07 5.930499e-05 0.9972804
#> 10 0.10658013 20 21 1.366610e-06 5.925275e-04 0.9972804
#> 11 0.10589778 21 73 1.596859e-06 6.678429e-04 0.9972804
#> 12 0.10478689 20 91 2.053403e-06 8.024425e-04 0.9972804
#> 13 0.10420836 7 52 2.338382e-06 8.778605e-04 0.9944557
#> 14 0.10236077 87 95 3.525186e-06 1.224964e-03 0.9944557
#> 15 0.10113550 27 95 4.610444e-06 1.500047e-03 0.9920084
#> 16 0.09928954 21 51 6.868357e-06 2.046549e-03 0.9920084
#> 17 0.09791914 21 88 9.192373e-06 2.520616e-03 0.9920084
#> 18 0.09719685 18 95 1.070232e-05 2.790102e-03 0.9920084
#> 19 0.09621791 28 90 1.313007e-05 3.171817e-03 0.9920084
#> 20 0.09619099 12 80 1.320374e-05 3.182526e-03 0.9920084
#> 21 0.09576091 89 95 1.443542e-05 3.354777e-03 0.9891317
#> 22 0.09473210 7 51 1.784126e-05 3.864825e-03 0.9891317
#> 23 -0.09386896 53 58 2.127622e-05 4.313590e-03 0.9891317
#> 24 -0.09366615 29 83 2.217013e-05 4.421099e-03 0.9891317
#> 25 -0.09341148 21 89 2.334321e-05 4.556947e-03 0.9810727
#> 26 -0.09156391 49 93 3.380043e-05 5.955972e-03 0.9810727
#> 27 -0.09150710 80 90 3.418363e-05 6.002083e-03 0.9810727
#> 28 0.09101505 7 53 3.767966e-05 6.408102e-03 0.9810727
#> 29 0.09050688 21 84 4.164471e-05 6.838782e-03 0.9810727
#> 30 0.08965490 72 73 4.919365e-05 7.581866e-03 0.9810727
#> 31 -0.08934025 29 99 5.229604e-05 7.861416e-03 0.9810727
#> 32 -0.08906819 9 95 5.512708e-05 8.104759e-03 0.9810727
#> 33 0.08888345 2 49 5.713144e-05 8.270673e-03 0.9810727
#> 34 0.08850681 86 90 6.143363e-05 8.610161e-03 0.9810727
#> 35 0.08805868 17 53 6.695170e-05 9.015175e-03 0.9810727
#> 36 0.08790809 28 48 6.890884e-05 9.151291e-03 0.9810727
#> 37 0.08783471 33 58 6.988211e-05 9.217597e-03 0.9682377
#> 38 -0.08705796 7 49 8.101244e-05 1.021362e-02 0.9682377
#> 39 0.08645033 20 46 9.086547e-05 1.102466e-02 0.9682377
#> 40 0.08609950 48 86 9.705862e-05 1.150392e-02 0.9682377
#> 41 0.08598769 21 52 9.911458e-05 1.165816e-02 0.9682377
#> 42 0.08555275 32 95 1.075099e-04 1.226435e-02 0.9682377
#> 43 0.08548231 17 51 1.089311e-04 1.236337e-02 0.9424721
#> 44 0.08470370 80 83 1.258659e-04 1.382356e-02 0.9424721
#> 45 0.08442510 80 82 1.325062e-04 1.437068e-02 0.9174573
#> 46 0.08271606 80 93 1.810275e-04 1.845632e-02 0.9174573
#> 47 0.08235175 46 91 1.933329e-04 1.941579e-02 0.9174573
#> 48 0.08217787 25 95 1.994788e-04 1.988432e-02 0.9174573
#> 49 -0.08170331 29 87 2.171999e-04 2.119715e-02 0.9174573
#> 50 0.08123632 19 29 2.360716e-04 2.253606e-02 0.9174573
#> 51 0.08101702 51 84 2.454547e-04 2.318024e-02 0.9174573
#> 52 0.08030748 16 93 2.782643e-04 2.532796e-02 0.9174573
#> 53 0.08006503 28 52 2.903870e-04 2.608271e-02 0.9174573
#> 54 -0.07941656 41 80 3.252833e-04 2.814824e-02 0.9174573
#> 55 0.07941410 54 89 3.254229e-04 2.815620e-02 0.9174573
#> 56 -0.07934653 28 80 3.292784e-04 2.837511e-02 0.9174573
#> 57 0.07916783 29 92 3.396802e-04 2.895702e-02 0.9174573
#> 58 -0.07866905 17 86 3.703635e-04 3.060293e-02 0.9174573
#> 59 0.07827749 16 29 3.962446e-04 3.191462e-02 0.9174573
#> 60 -0.07808262 73 89 4.097452e-04 3.257290e-02 0.9174573
#> 61 0.07766261 52 67 4.403165e-04 3.400207e-02 0.9174573
#> 62 0.07762917 25 87 4.428396e-04 3.411637e-02 0.9174573
#> 63 -0.07739378 9 93 4.609872e-04 3.492295e-02 0.9174573
#> 64 0.07738885 31 80 4.613747e-04 3.493988e-02 0.9174573
#> 65 -0.07718681 80 94 4.775136e-04 3.563444e-02 0.9174573
#> 66 0.07706275 27 58 4.876831e-04 3.606179e-02 0.8297811
#> 67 -0.07610709 16 83 5.730532e-04 4.085920e-02 0.8297811
#> 68 0.07550557 53 84 6.337143e-04 4.406472e-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 1.120812e-09 1.093997e-06 0.9999516
#> 6 0.12594697 21 72 1.103836e-08 8.978563e-06 0.9998400
#> 7 0.11956105 28 86 5.890924e-08 3.853590e-05 0.9998400
#> 8 -0.11723897 26 80 1.060526e-07 5.816172e-05 0.9998400
#> 9 -0.11711625 72 89 1.093655e-07 5.930499e-05 0.9972804
#> 10 0.10658013 20 21 1.366610e-06 5.925275e-04 0.9972804
#> 11 0.10589778 21 73 1.596859e-06 6.678429e-04 0.9972804
#> 12 0.10478689 20 91 2.053403e-06 8.024425e-04 0.9972804
#> 13 0.10420836 7 52 2.338382e-06 8.778605e-04 0.9944557
#> 14 0.10236077 87 95 3.525186e-06 1.224964e-03 0.9944557
#> 15 0.10113550 27 95 4.610444e-06 1.500047e-03 0.9920084
#> 16 0.09928954 21 51 6.868357e-06 2.046549e-03 0.9920084
#> 17 0.09791914 21 88 9.192373e-06 2.520616e-03 0.9920084
#> 18 0.09719685 18 95 1.070232e-05 2.790102e-03 0.9920084
#> 19 0.09621791 28 90 1.313007e-05 3.171817e-03 0.9920084
#> 20 0.09619099 12 80 1.320374e-05 3.182526e-03 0.9920084
#> 21 0.09576091 89 95 1.443542e-05 3.354777e-03 0.9891317
#> 22 0.09473210 7 51 1.784126e-05 3.864825e-03 0.9891317
#> 23 -0.09386896 53 58 2.127622e-05 4.313590e-03 0.9891317
#> 24 -0.09366615 29 83 2.217013e-05 4.421099e-03 0.9891317
#> 25 -0.09341148 21 89 2.334321e-05 4.556947e-03 0.9810727
#> 26 -0.09156391 49 93 3.380043e-05 5.955972e-03 0.9810727
#> 27 -0.09150710 80 90 3.418363e-05 6.002083e-03 0.9810727
#> 28 0.09101505 7 53 3.767966e-05 6.408102e-03 0.9810727
#> 29 0.09050688 21 84 4.164471e-05 6.838782e-03 0.9810727
#> 30 0.08965490 72 73 4.919365e-05 7.581866e-03 0.9810727
#> 31 -0.08934025 29 99 5.229604e-05 7.861416e-03 0.9810727
#> 32 -0.08906819 9 95 5.512708e-05 8.104759e-03 0.9810727
#> 33 0.08888345 2 49 5.713144e-05 8.270673e-03 0.9810727
#> 34 0.08850681 86 90 6.143363e-05 8.610161e-03 0.9810727
#> 35 0.08805868 17 53 6.695170e-05 9.015175e-03 0.9810727
#> 36 0.08790809 28 48 6.890884e-05 9.151291e-03 0.9810727
#> 37 0.08783471 33 58 6.988211e-05 9.217597e-03 0.9682377
#> 38 -0.08705796 7 49 8.101244e-05 1.021362e-02 0.9682377
#> 39 0.08645033 20 46 9.086547e-05 1.102466e-02 0.9682377
#> 40 0.08609950 48 86 9.705862e-05 1.150392e-02 0.9682377
#> 41 0.08598769 21 52 9.911458e-05 1.165816e-02 0.9682377
#> 42 0.08555275 32 95 1.075099e-04 1.226435e-02 0.9682377
#> 43 0.08548231 17 51 1.089311e-04 1.236337e-02 0.9424721
#> 44 0.08470370 80 83 1.258659e-04 1.382356e-02 0.9424721
#> 45 0.08442510 80 82 1.325062e-04 1.437068e-02 0.9174573
#> 46 0.08271606 80 93 1.810275e-04 1.845632e-02 0.9174573
#> 47 0.08235175 46 91 1.933329e-04 1.941579e-02 0.9174573
#> 48 0.08217787 25 95 1.994788e-04 1.988432e-02 0.9174573
#> 49 -0.08170331 29 87 2.171999e-04 2.119715e-02 0.9174573
#> 50 0.08123632 19 29 2.360716e-04 2.253606e-02 0.9174573
#> 51 0.08101702 51 84 2.454547e-04 2.318024e-02 0.9174573
#> 52 0.08030748 16 93 2.782643e-04 2.532796e-02 0.9174573
#> 53 0.08006503 28 52 2.903870e-04 2.608271e-02 0.9174573
#> 54 -0.07941656 41 80 3.252833e-04 2.814824e-02 0.9174573
#> 55 0.07941410 54 89 3.254229e-04 2.815620e-02 0.9174573
#> 56 -0.07934653 28 80 3.292784e-04 2.837511e-02 0.9174573
#> 57 0.07916783 29 92 3.396802e-04 2.895702e-02 0.9174573
#> 58 -0.07866905 17 86 3.703635e-04 3.060293e-02 0.9174573
#> 59 0.07827749 16 29 3.962446e-04 3.191462e-02 0.9174573
#> 60 -0.07808262 73 89 4.097452e-04 3.257290e-02 0.9174573
#> 61 0.07766261 52 67 4.403165e-04 3.400207e-02 0.9174573
#> 62 0.07762917 25 87 4.428396e-04 3.411637e-02 0.9174573
#> 63 -0.07739378 9 93 4.609872e-04 3.492295e-02 0.9174573
#> 64 0.07738885 31 80 4.613747e-04 3.493988e-02 0.9174573
#> 65 -0.07718681 80 94 4.775136e-04 3.563444e-02 0.9174573
# parameters of the mixture distribution used to compute p-values etc.
c <- fdrtool(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 2043.377 94.72264
## A random network with 20 nodes and 10 percent (=19) edges
true.pcor <- ggm.simulate.pcor(20, 0.1)
# convert to edge list
test.results <- 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 <- 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~G A~D B~L C~O D~M D~I F~G F~J
#> 0.31713 -0.50713 0.51959 0.77535 -0.28878 0.64012 0.23833 -0.85939
#> G~T H~O H~L I~L L~T L~M L~R M~R
#> -0.63903 0.32175 -0.38068 -0.00510 0.03385 0.30539 0.40322 -0.07393
#> N~P O~S Q~R
#> -0.99984 -0.31540 -0.65198
#>
#> $dir
#> A~G A~D B~L C~O D~M D~I F~G F~J G~T H~O H~L
#> "none" "none" "none" "none" "none" "none" "none" "none" "none" "none" "none"
#> I~L L~T L~M L~R M~R N~P O~S Q~R
#> "none" "none" "none" "none" "none" "none" "none" "none"
gr2 <- network.make.graph( test.results, nlab, drop.singles=TRUE)
gr2
#> A graphNEL graph with directed edges
#> Number of Nodes = 18
#> Number of Edges = 38
num.nodes(gr2)
#> [1] 18
edge.info(gr2)
#> $weight
#> A~G A~D B~L C~O D~M D~I F~G F~J
#> 0.31713 -0.50713 0.51959 0.77535 -0.28878 0.64012 0.23833 -0.85939
#> G~T H~O H~L I~L L~T L~M L~R M~R
#> -0.63903 0.32175 -0.38068 -0.00510 0.03385 0.30539 0.40322 -0.07393
#> N~P O~S Q~R
#> -0.99984 -0.31540 -0.65198
#>
#> $dir
#> A~G A~D B~L C~O D~M D~I F~G F~J G~T H~O H~L
#> "none" "none" "none" "none" "none" "none" "none" "none" "none" "none" "none"
#> I~L L~T L~M L~R M~R N~P O~S Q~R
#> "none" "none" "none" "none" "none" "none" "none" "none"
# plot network
plot(gr, "fdp")
#> Warning in arrows(tail_from[1], tail_from[2], tail_to[1], tail_to[2], col =
#> edgeColor, : zero-length arrow is of indeterminate angle and so skipped
#> Warning in arrows(head_from[1], head_from[2], head_to[1], head_to[2], col =
#> edgeColor, : zero-length arrow is of indeterminate angle and so skipped
plot(gr2, "fdp")
#> Warning in arrows(tail_from[1], tail_from[2], tail_to[1], tail_to[2], col =
#> edgeColor, : zero-length arrow is of indeterminate angle and so skipped
#> Warning in arrows(tail_from[1], tail_from[2], tail_to[1], tail_to[2], col =
#> edgeColor, : zero-length arrow is of indeterminate angle and so skipped
A side-by-side heatmaps
set.seed(123454321)
m <- matrix(runif(2500),50)
r <- cor(m)
g <- as.matrix(r>=0.7)+0
f1 <- Heatmap(r)
f2 <- Heatmap(g)
f <- f1+f2
draw(f)
df <- normalize(mtcars)
heatmaply(df,k_col=5,k_row=5,colors = colorRampPalette(brewer.pal(3, "RdBu"))(256))and a module analysis with WGCNA,
pwr <- c(1:10, seq(from=12, to=30, by=2))
sft <- pickSoftThreshold(dat, powerVector=pwr, verbose=5)
ADJ <- abs(cor(dat, method="pearson", use="pairwise.complete.obs"))^6
dissADJ <- 1-ADJ
dissTOM <- TOMdist(ADJ)
TOM <- TOMsimilarityFromExpr(dat)
Tree <- hclust(as.dist(1-TOM), method="average")
for(j in pwr)
{
pam_name <- paste0("pam",j)
assign(pam_name, pam(as.dist(dissADJ),j))
pamTOM_name <- paste0("pamTOM",j)
assign(pamTOM_name,pam(as.dist(dissTOM),j))
tc <- table(get(pam_name)$clustering,get(pamTOM_name)$clustering)
print(tc)
print(diag(tc))
}
colorStaticTOM <- as.character(cutreeStaticColor(Tree,cutHeight=.99,minSize=5))
colorDynamicTOM <- labels2colors(cutreeDynamic(Tree,method="tree",minClusterSize=5))
Colors <- data.frame(pamTOM6$clustering,colorStaticTOM,colorDynamicTOM)
plotDendroAndColors(Tree, Colors, dendroLabels=FALSE, hang=0.03, addGuide=TRUE, guideHang=0.05)
meg <- moduleEigengenes(dat, color=1:ncol(dat), softPower=6)4 Meta-data
hs <- available_projects()
#> 2023-08-23 11:24:55.205225 caching file sra.recount_project.MD.gz.
#> 2023-08-23 11:24:56.221966 caching file gtex.recount_project.MD.gz.
#> 2023-08-23 11:24:57.401763 caching file tcga.recount_project.MD.gz.
dim(subset(hs,file_source=="gtex"))
#> [1] 32 6
annotation_options("human")
#> [1] "gencode_v26" "gencode_v29" "fantom6_cat" "refseq" "ercc"
#> [6] "sirv"
blood_rse <- create_rse(subset(hs,project=="BLOOD"))
#> 2023-08-23 11:25:00.391702 downloading and reading the metadata.
#> 2023-08-23 11:25:01.355857 caching file gtex.gtex.BLOOD.MD.gz.
#> 2023-08-23 11:25:02.571987 caching file gtex.recount_project.BLOOD.MD.gz.
#> 2023-08-23 11:25:03.627338 caching file gtex.recount_qc.BLOOD.MD.gz.
#> 2023-08-23 11:25:04.694022 caching file gtex.recount_seq_qc.BLOOD.MD.gz.
#> 2023-08-23 11:25:05.525348 downloading and reading the feature information.
#> 2023-08-23 11:25:06.308037 caching file human.gene_sums.G026.gtf.gz.
#> 2023-08-23 11:25:07.049666 downloading and reading the counts: 1048 samples across 63856 features.
#> 2023-08-23 11:25:07.988771 caching file gtex.gene_sums.BLOOD.G026.gz.
#> 2023-08-23 11:25:13.244113 constructing the RangedSummarizedExperiment (rse) object.
metadata(blood_rse)
#> $time_created
#> [1] "2023-08-23 11:25:13 BST"
#>
#> $recount3_version
#> package ondiskversion loadedversion
#> recount3 recount3 1.9.1 1.9.1
#> path
#> recount3 /rds/project/jmmh2/rds-jmmh2-public_databases/software/R/recount3
#> loadedpath
#> recount3 /rds/project/jmmh2/rds-jmmh2-public_databases/software/R/recount3
#> attached is_base date source md5ok
#> recount3 TRUE FALSE 2023-01-11 Bioconductor NA
#> library
#> recount3 /rds/project/jmmh2/rds-jmmh2-public_databases/software/R
#>
#> $project
#> [1] "BLOOD"
#>
#> $project_home
#> [1] "data_sources/gtex"
#>
#> $type
#> [1] "gene"
#>
#> $organism
#> [1] "human"
#>
#> $annotation
#> [1] "gencode_v26"
#>
#> $recount3_url
#> [1] "http://duffel.rail.bio/recount3"
rowRanges(blood_rse)
#> GRanges object with 63856 ranges and 10 metadata columns:
#> seqnames ranges strand | source
#> <Rle> <IRanges> <Rle> | <factor>
#> ENSG00000278704.1 GL000009.2 56140-58376 - | ENSEMBL
#> ENSG00000277400.1 GL000194.1 53590-115018 - | ENSEMBL
#> ENSG00000274847.1 GL000194.1 53594-115055 - | ENSEMBL
#> ENSG00000277428.1 GL000195.1 37434-37534 - | ENSEMBL
#> ENSG00000276256.1 GL000195.1 42939-49164 - | ENSEMBL
#> ... ... ... ... . ...
#> ENSG00000124334.17_PAR_Y chrY 57184101-57197337 + | HAVANA
#> ENSG00000185203.12_PAR_Y chrY 57201143-57203357 - | HAVANA
#> ENSG00000270726.6_PAR_Y chrY 57190738-57208756 + | HAVANA
#> ENSG00000182484.15_PAR_Y chrY 57207346-57212230 + | HAVANA
#> ENSG00000227159.8_PAR_Y chrY 57212184-57214397 - | HAVANA
#> type bp_length phase gene_id
#> <factor> <numeric> <integer> <character>
#> ENSG00000278704.1 gene 2237 <NA> ENSG00000278704.1
#> ENSG00000277400.1 gene 2179 <NA> ENSG00000277400.1
#> ENSG00000274847.1 gene 1599 <NA> ENSG00000274847.1
#> ENSG00000277428.1 gene 101 <NA> ENSG00000277428.1
#> ENSG00000276256.1 gene 2195 <NA> ENSG00000276256.1
#> ... ... ... ... ...
#> ENSG00000124334.17_PAR_Y gene 2504 <NA> ENSG00000124334.17_P..
#> ENSG00000185203.12_PAR_Y gene 1054 <NA> ENSG00000185203.12_P..
#> ENSG00000270726.6_PAR_Y gene 773 <NA> ENSG00000270726.6_PA..
#> ENSG00000182484.15_PAR_Y gene 4618 <NA> ENSG00000182484.15_P..
#> ENSG00000227159.8_PAR_Y gene 1306 <NA> ENSG00000227159.8_PA..
#> gene_type gene_name level
#> <character> <character> <character>
#> ENSG00000278704.1 protein_coding BX004987.1 3
#> ENSG00000277400.1 protein_coding AC145212.2 3
#> ENSG00000274847.1 protein_coding AC145212.1 3
#> ENSG00000277428.1 misc_RNA Y_RNA 3
#> ENSG00000276256.1 protein_coding AC011043.1 3
#> ... ... ... ...
#> ENSG00000124334.17_PAR_Y protein_coding IL9R 2
#> ENSG00000185203.12_PAR_Y antisense WASIR1 2
#> ENSG00000270726.6_PAR_Y processed_transcript AJ271736.10 2
#> ENSG00000182484.15_PAR_Y transcribed_unproces.. WASH6P 2
#> ENSG00000227159.8_PAR_Y unprocessed_pseudogene DDX11L16 2
#> havana_gene tag
#> <character> <character>
#> ENSG00000278704.1 <NA> <NA>
#> ENSG00000277400.1 <NA> <NA>
#> ENSG00000274847.1 <NA> <NA>
#> ENSG00000277428.1 <NA> <NA>
#> ENSG00000276256.1 <NA> <NA>
#> ... ... ...
#> ENSG00000124334.17_PAR_Y OTTHUMG00000022720.1 PAR
#> ENSG00000185203.12_PAR_Y OTTHUMG00000022676.3 PAR
#> ENSG00000270726.6_PAR_Y OTTHUMG00000184987.2 PAR
#> ENSG00000182484.15_PAR_Y OTTHUMG00000022677.5 PAR
#> ENSG00000227159.8_PAR_Y OTTHUMG00000022678.1 PAR
#> -------
#> seqinfo: 374 sequences from an unspecified genome; no seqlengths
colnames(colData(blood_rse))[1:20]
#> [1] "rail_id" "external_id" "study" "gtex.run_acc"
#> [5] "gtex.subjid" "gtex.sex" "gtex.age" "gtex.dthhrdy"
#> [9] "gtex.sampid" "gtex.smatsscr" "gtex.smcenter" "gtex.smpthnts"
#> [13] "gtex.smrin" "gtex.smts" "gtex.smtsd" "gtex.smubrid"
#> [17] "gtex.smtsisch" "gtex.smtspax" "gtex.smnabtch" "gtex.smnabtcht"
expand_sra_attributes(blood_rse)
#> Warning: sra.sample_attributes was absent from the input rse. Will return the
#> original rse object.
#> class: RangedSummarizedExperiment
#> dim: 63856 1048
#> metadata(8): time_created recount3_version ... annotation recount3_url
#> assays(1): raw_counts
#> rownames(63856): ENSG00000278704.1 ENSG00000277400.1 ...
#> ENSG00000182484.15_PAR_Y ENSG00000227159.8_PAR_Y
#> rowData names(10): source type ... havana_gene tag
#> colnames(1048): GTEX-R55C-0005-SM-3GAE9.1 GTEX-QMR6-0005-SM-32PKY.1 ...
#> GTEX-1NSGN-0005-SM-DKPPX.1 GTEX-1QP29-0005-SM-DLZQX.1
#> colData names(198): rail_id external_id ... recount_seq_qc.errq
#> BigWigURL5 Pathway and enrichment analysis
reactome <- pathways("hsapiens", "reactome")
kegg <- pathways("hsapiens","kegg")
pharmgkb <- 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 <- enricher(gene=VEGF, TERM2GENE = kegg_t2g,
universe=,
pAdjustMethod = "BH",
pvalueCutoff = 0.1, qvalueCutoff = 0.05,
minGSSize = 10, maxGSSize = 500)6 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
listColumns(x, "protein", skip.keys=TRUE)
#> [1] "tx_id" "protein_id" "protein_sequence"
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"
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"
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"
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
organism(x)
#> [1] "Homo sapiens"
returnFilterColumns(x)
#> [1] TRUE
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
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"
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.
genes(x, columns=c("gene_name"), filter=list(SeqNameFilter("X"), 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
transcripts(x, columns=listColumns(x, "tx"), filter = 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 <- makeTxDbFromEnsembl(organism="Homo sapiens", release=98)
#> 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=)8 Bioconductor/CRAN packages
| Package | Description |
|---|---|
| Bioconductor | |
| AnnotationDbi | AnnotationDb objects and their progeny, methods etc. |
| Biobase | Base functions for Bioconductor |
| org.Hs.eg.db | Conversion of Entrez ID – gene symbols |
| EnsDb.Hsapiens.v86 | Exposes an annotation databases generated from Ensembl |
| ensembldb | Retrieve annotation data from an Ensembl based package |
| TxDb.Hsapiens.UCSC.hg38.knownGene | Annotation of the human genome |
| INSPEcT | Quantification of the intronic and exonic gene features and the post-transcriptional regulation analysis |
| graphite | GRAPH Interaction from pathway topological environment |
| clusterProfiler | Functional profiles for genes and gene clusters |
| DESSeq2 | Differential gene expression analysis based on the negative binomial distribution |
| edgeR | Empirical analysis of digital gene expression |
| WGCNA | Weighted correlation network analysis |
| ComplexHeatmap | Make complex heatmaps |
| recount3 | Interface to uniformly processed RNA-seq data |
| 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 |
| FlowSorted.DLPFC.450k | Illumina HumanMethylation data on sorted frontal cortex cell populations |
| Rgraphiz | Interfaces R with the AT&T graphviz library for plotting R graph objects from the graph package |
| sva | Surrogate Variable Analysis |
| doParallel | Foreach Parallel Adaptor for the ‘parallel’ Package |
| CRAN | |
| GeneNet | Modeling and Inferring Gene Networks |
| RColorBrewer | ColorBrewer Palettes |
| 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 |
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.) 67–9 (Springer New York, 2016). doi:10.1007/978-1-4939-3578-9_4.