+
+
+
# bambu: reference-guided transcript discovery and quantification for long read RNA-Seq data
-The code is still under development.
+
+[](https://github.com/GoekeLab/bambu)
+[](https://github.com/GoekeLab/bambu/graphs/contributors)
+[](#installation)
+[](https://www.gnu.org/licenses/gpl-3.0)
+
+
+***bambu*** is a R package for multi-sample transcript discovery and quantification using long read RNA-Seq data. You can use ***bambu*** after read alignment to obtain expression estimates for known and novel transcripts and genes. The output from ***bambu*** can directly be used for visualisation and downstream analysis such as differential gene expression or transcript usage.
+
+
+---
+
+### Content
+
+ - [Installation](#installation)
+ - [General usage](#general-usage)
+ - [Use precalculated annotation objects](#use-precalculated-annotation-objects)
+ - [Advanced options](#advanced-options)
+ - [Complementary functions](#complementary-functions)
+ - [Release History](#release-history)
+ - [Contributors](#contributors)
+
+
+### Installation
+
+You can install ***bambu*** from github:
+
+```rscript
+if (!requireNamespace("devtools", quietly = TRUE))
+ install.packages("devtools")
+devtools::install_github("GoekeLab/bambu")
+```
+---
+
+### General Usage
+
+The default mode to run ***bambu** is using a set of aligned reads (bam files), reference genome annotations (gtf file, TxDb object, or bambuAnnotation object), and reference genome sequence (fasta file or BSgenome). ***bambu*** will return a summarizedExperiment object with the genomic coordinates for annotated and new transcripts and transcript expression estimates:
+
+ ```rscript
+library(bambu)
+
+test.bam <- system.file("extdata", "SGNex_A549_directRNA_replicate5_run1_chr9_1_1000000.bam", package = "bambu")
+
+
+se <- bambu(reads = test.bam, annotations = "TxDb.Hsapiens.UCSC.hg38.knownGene", genomeSequence = "BSgenome.Hsapiens.NCBI.GRCh38")
+
+```
+
+
+We highly recommend to use the same annotations that were used for genome alignment. If you have a gtf file and fasta file you can run ***bambu*** with the following options:
+
+```rscript
+test.bam <- system.file("extdata", "SGNex_A549_directRNA_replicate5_run1_chr9_1_1000000.bam", package = "bambu")
+
+fa.file <- system.file("extdata", "Homo_sapiens.GRCh38.dna_sm.primary_assembly_chr9_1_1000000.fa", package = "bambu")
+
+gtf.file <- system.file("extdata", "Homo_sapiens.GRCh38.91_chr9_1_1000000.gtf", package = "bambu")
+
+bambuAnnotations <- prepareAnnotationsFromGTF(gtf.file)
+
+se <- bambu(reads = test.bam, annotations = bambuAnnotations, genomeSequence = fa.file)
+
+```
+
+
+**Quantification of annotated transcripts and genes only (no transcript/gene discovery)**
+
+```rscript
+bambu(reads = test.bam, annotations = txdb, genomeSequence = fa.file, extendAnnotations = FALSE)
+```
+
+**Large sample number/ limited memory**
+For larger sample numbers we recommend to write the processed data to a file:
+
+```rscript
+bambu(reads = test.bam, readClass.outputDir = "./bambu/", annotations = bambuAnnotations, genomeSequence = fa.file)
+```
+---
+
+
+### Use precalculated annotation objects
+
+You can also use precalculated annotations.
+
+If you plan to run ***bambu*** more frequently, we recommend to save the bambuAnnotations object.
+
+The bambuAnnotation object can be calculated from a *.gtf* file:
+
+```rscript
+annotations <- prepareAnnotationFromGTF(gtf.file)
+```
+
+From *TxDb* object
+
+```rscript
+annotations <- prepareAnnotations(txdb)
+```
+
+---
+
+### Advanced Options
+
+**More stringent filtering thresholds imposed on potential novel transcripts**
+
+- Keep novel transcripts with min 5 read count in at least 1 sample:
+
+```rscript
+bambu(reads, annotations, genomeSequence, isoreParameters = list(min.readCount = 5))
+```
+
+- Keep novel transcripts with min 5 samples having at least 2 counts:
+
+```rscript
+bambu(reads, annotations, genomeSequence, isoreParameters = list(min.sampleNumber = 5))
+```
+
+- Filter out transcripts with relative abundance within gene lower than 10%:
+
+```rscript
+bambu(reads, annotations, genomeSequence, isoreParameters = list(min.readFractionByGene = 0.1))
+```
+
+**Quantification without bias correction**
+
+ The default estimation automatically does bias correction for expression estimates. However, you can choose to perform the quantification without bias correction.
+
+```rscript
+bambu(reads, annotations, genomeSequence, emParameters = list(bias = FALSE))
+```
+
+**Parallel computation**
+ ***bambu*** allows parallel computation.
+
+```rscript
+bambu(reads, annotations, genomeSequence, ncore = 8)
+```
+
+See [manual](docs/bambu_0.1.0.pdf) for details to customize other conditions.
+
+---
+
+### Complementary functions
+
+**Transcript expression to gene expression**
+
+```rscript
+transcriptToGeneExpression(se)
+```
+
+**Visualization**
+
+ You can visualize the novel genes/transcripts using ***plot.bambu*** function
+
+```rscript
+plot.bambu(se, type = "annotation", gene_id)
+
+plot.bambu(se, type = "annotation", transcript_id)
+```
+
+- ***plot.bambu*** can also be used to visualize the clustering of input samples on gene/transcript expressions
+
+```rscript
+plot.bambu(se, type = "heatmap") # heatmap
+
+plot.bambu(se, type = "pca") # PCA visualization
+```
+
+- ***plot.bambu*** can also be used to visualize the clustering of input samples on gene/transcript expressions with grouping variable
+
+```rscript
+plot.bambu(se, type = "heatmap", group.var) # heatmap
+
+plot.bambu(se, type = "pca", group.var) # PCA visualization
+```
+
+**Write bambu outputs to files**
+
+- ***writeBambuOutput*** will generate three files, including a *.gtf* file for the extended annotations, and two *.txt* files for the expression counts at transcript and gene levels.
+
+```rscript
+writeBambuOutput(se, path = "./bambu/")
+```
+---
+
+### Release History
+
+**bambu version 0.1.0**
+
+Release date: 29th May 2020
+
+### Contributors
+
+This package is developed and maintained by [Ying Chen](https://github.com/cying111), [Yuk Kei Wan](https://github.com/yuukiiwa), and [Jonathan Goeke](https://github.com/jonathangoeke) at the Genome Institute of Singapore. If you want to contribute, please leave an issue. Thank you.
+
+
diff --git a/data-raw/DATASET.R b/data-raw/DATASET.R
index 4b168235..8fa19830 100755
--- a/data-raw/DATASET.R
+++ b/data-raw/DATASET.R
@@ -30,11 +30,11 @@ data5 <- data.frame(tx_id = c(1:6,2,3,1,2,5,1,2,3,5,1:5),
estOutput_woBC <- lapply(1:5, function(s){
- est <- bambu.quantDT(dt = get(paste0("data",s)), algo.control=list(bias_correction = FALSE,ncore = 1))
+ est <- bambu.quantDT(readClassDt = get(paste0("data",s)), emParameters=list(bias = FALSE))
})
estOutput_wBC <- lapply(1:5, function(s){
- est <- bambu.quantDT(dt = get(paste0("data",s)), algo.control=list(ncore = 1))
+ est <- bambu.quantDT(readClassDt = get(paste0("data",s)))
})
diff --git a/docs/404.html b/docs/404.html
new file mode 100644
index 00000000..282f3cbb
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+++ b/docs/404.html
@@ -0,0 +1,148 @@
+
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+
+
+
+
+
+
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Bambu can be used for transcript discovery and quantification from long read RNA-Seq data. Here, we present an example usage of bambu on Nanopore long read RNA-Sequencing from 2 human cancer cell lines
+To demonstrate the usage of Bambu, we used the data in NanoporeRNASeq, which contains single chromosome RNA-Seq data from two common cell lines K562 and MCF7. Each of these cell line has three replicates, with 1 direct RNA sequencing data and 2 cDNA sequencing data.
+ +data("sample_info") +sample_info +##> sample_id Platform cellLine protocol +##> 1: SGNex_K562_directcDNA_replicate1_run2 MinION K562 directcDNA +##> 2: SGNex_K562_directcDNA_replicate4_run2 GridION K562 directcDNA +##> 3: SGNex_K562_directRNA_replicate6_run1 GridION K562 directRNA +##> 4: SGNex_MCF7_directcDNA_replicate1_run2 MinION MCF7 directcDNA +##> 5: SGNex_MCF7_directcDNA_replicate3_run3 GridION MCF7 directcDNA +##> 6: SGNex_MCF7_directRNA_replicate4_run1 GridION MCF7 directRNA +##> bioRep cancer_type +##> 1: replicate1 Leukocyte +##> 2: replicate4 Leukocyte +##> 3: replicate6 Leukocyte +##> 4: replicate1 Breast +##> 5: replicate3 Breast +##> 6: replicate4 Breast
Name of bamfiles can be loaded as follows
+data("bamFileNames") +bamFileNames +##> [1] "SGNex_K562_directcDNA_replicate1_run2_genome_chr22_1_25409234.bam" +##> [2] "SGNex_K562_directcDNA_replicate4_run2_genome_chr22_1_25409234.bam" +##> [3] "SGNex_K562_directRNA_replicate6_run1_genome_chr22_1_25409234.bam" +##> [4] "SGNex_MCF7_directcDNA_replicate1_run2_genome_chr22_1_25409234.bam" +##> [5] "SGNex_MCF7_directcDNA_replicate3_run3_genome_chr22_1_25409234.bam" +##> [6] "SGNex_MCF7_directRNA_replicate4_run1_genome_chr22_1_25409234.bam"
We then loaded bam.files.
+bam.file <- system.file("extdata",bamFileNames, package = "NanoporeRNASeq") +data("annotationGrangesList_chr22_1_25409234")
We applied bambu to perform EM on extended annotations
+seExtended <- bambu(reads = bam.file, annotations = annotationGrangesList_chr22_1_25409234, genomeSequence = "BSgenome.Hsapiens.NCBI.GRCh38", extendAnnotations = TRUE, verbose = FALSE, ncore = 6) +##> + | + | | 0% + | + | | 0% + | + |======================================================================| 100% +##> +##> + | + | | 0% + | + |======================================================================| 100% +##> +##> + | + |============ | 17% + | + | | 0% + | + |======================================================================| 100% +##> +##> + | + | | 0% + | + |======================================================================| 100% +##> +##> + | + |======================= | 33% + | + | | 0% + | + |======================================================================| 100% +##> +##> + | + | | 0% + | + |======================================================================| 100% +##> +##> + | + |=================================== | 50% + | + | | 0% + | + |======================================================================| 100% +##> +##> + | + | | 0% + | + |======================================================================| 100% +##> +##> + | + |=============================================== | 67% + | + | | 0% + | + |======================================================================| 100% +##> +##> + | + | | 0% + | + |======================================================================| 100% +##> +##> + | + |========================================================== | 83% + | + | | 0% + | + |======================================================================| 100% +##> +##> + | + | | 0% + | + |======================================================================| 100% +##> +##> + | + |======================================================================| 100% +##> +##> + | + | | 0% + | + |============ | 17% + | + |======================= | 33% + | + |=================================== | 50% + | + |=============================================== | 67% + | + |========================================================== | 83% + | + |======================================================================| 100% +seExtended +##> class: RangedSummarizedExperiment +##> dim: 1923 6 +##> metadata(0): +##> assays(2): counts CPM +##> rownames(1923): tx.1 tx.2 ... ENST00000484509 ENST00000468442 +##> rowData names(4): TXNAME GENEID eqClass newTxClass +##> colnames(6): +##> SGNex_K562_directcDNA_replicate1_run2_genome_chr22_1_25409234 +##> SGNex_K562_directcDNA_replicate4_run2_genome_chr22_1_25409234 ... +##> SGNex_MCF7_directcDNA_replicate3_run3_genome_chr22_1_25409234 +##> SGNex_MCF7_directRNA_replicate4_run1_genome_chr22_1_25409234 +##> colData names(1): name
bambu allows quantification without isoform discovery
+se <- bambu(reads = bam.file, annotations = annotationGrangesList_chr22_1_25409234, genomeSequence = "BSgenome.Hsapiens.NCBI.GRCh38", extendAnnotations = FALSE, verbose = FALSE, ncore = 6) +##> + | + | | 0% + | + | | 0% + | + |======================================================================| 100% +##> +##> + | + | | 0% + | + |======================================================================| 100% +##> +##> + | + |============ | 17% + | + | | 0% + | + |======================================================================| 100% +##> +##> + | + | | 0% + | + |======================================================================| 100% +##> +##> + | + |======================= | 33% + | + | | 0% + | + |======================================================================| 100% +##> +##> + | + | | 0% + | + |======================================================================| 100% +##> +##> + | + |=================================== | 50% + | + | | 0% + | + |======================================================================| 100% +##> +##> + | + | | 0% + | + |======================================================================| 100% +##> +##> + | + |=============================================== | 67% + | + | | 0% + | + |======================================================================| 100% +##> +##> + | + | | 0% + | + |======================================================================| 100% +##> +##> + | + |========================================================== | 83% + | + | | 0% + | + |======================================================================| 100% +##> +##> + | + | | 0% + | + |======================================================================| 100% +##> +##> + | + |======================================================================| 100% +##> +##> + | + | | 0% + | + |============ | 17% + | + |======================= | 33% + | + |=================================== | 50% + | + |=============================================== | 67% + | + |========================================================== | 83% + | + |======================================================================| 100% +se +##> class: RangedSummarizedExperiment +##> dim: 1500 6 +##> metadata(0): +##> assays(2): counts CPM +##> rownames(1500): ENST00000624155 ENST00000422332 ... ENST00000484509 +##> ENST00000468442 +##> rowData names(3): TXNAME GENEID eqClass +##> colnames(6): +##> SGNex_K562_directcDNA_replicate1_run2_genome_chr22_1_25409234 +##> SGNex_K562_directcDNA_replicate4_run2_genome_chr22_1_25409234 ... +##> SGNex_MCF7_directcDNA_replicate3_run3_genome_chr22_1_25409234 +##> SGNex_MCF7_directRNA_replicate4_run1_genome_chr22_1_25409234 +##> colData names(1): name
We can check the estimated transcript expression using heatmap:
+colData(seExtended)$groupVar <- unlist(lapply(colnames(seExtended),function(x) unlist(strsplit(x,"_"))[2])) +colnames(seExtended) <- gsub("_genome_chr22_1_25409234","",colnames(seExtended)) +colData(seExtended)$name <- gsub("_genome_chr22_1_25409234","",colData(seExtended)$name) +plot.bambu(seExtended, group.variable = "groupVar", type = "heatmap")
or with PCA plot
+colData(seExtended)$groupVar <- unlist(lapply(colnames(seExtended),function(x) unlist(strsplit(x,"_"))[2])) +plot.bambu(seExtended, group.variable = "groupVar", type = "pca")
Single gene examples can also be checked using plot functions from bambu
+plot.bambu(seExtended, type = "annotation", gene_id = unique(rowData(seExtended)$GENEID)[10])
##> [[1]]
+##> TableGrob (3 x 1) "arrange": 3 grobs
+##> z cells name grob
+##> 1 1 (2-2,1-1) arrange gtable[layout]
+##> 2 2 (3-3,1-1) arrange gtable[layout]
+##> 3 3 (1-1,1-1) arrange text[GRID.text.210]Gene expression can be obtained from transcript expression using this function:
+seGene <- transcriptToGeneExpression(seExtended) +seGene +##> class: RangedSummarizedExperiment +##> dim: 887 6 +##> metadata(0): +##> assays(2): counts CPM +##> rownames(887): ENSG00000015475 ENSG00000040608 ... gene.98 gene.99 +##> rowData names(2): GENEID newGeneClass +##> colnames(6): SGNex_K562_directcDNA_replicate1_run2 +##> SGNex_K562_directcDNA_replicate4_run2 ... +##> SGNex_MCF7_directcDNA_replicate3_run3 +##> SGNex_MCF7_directRNA_replicate4_run1 +##> colData names(2): name groupVar
Gene expression heatmap
+colData(seGene)$groupVar <- unlist(lapply(colnames(seGene),function(x) unlist(strsplit(x,"_"))[2])) +plot.bambu(seGene, group.variable = "groupVar", type = "heatmap")
Gene expression PCA plot
+colData(seGene)$groupVar <- unlist(lapply(colnames(seGene),function(x) unlist(strsplit(x,"_"))[2])) +plot.bambu(seGene, group.variable = "groupVar", type = "pca")
We used DESeq2 to find the differentially expressed genes:
+library(DESeq2) +dds <- DESeqDataSetFromMatrix(apply(assays(seGene)$counts,c(1,2),round),#tmp_wide[,-1], + colData = colData(seExtended), + design = ~ groupVar) +system.time(dds.deseq <- DESeq(dds)) +##> user system elapsed +##> 1.996 0.028 2.025 + +deGeneRes <- DESeq2::results(dds.deseq, independentFiltering=FALSE)
head(deGeneRes[order(deGeneRes$padj),]) +##> log2 fold change (MLE): groupVar MCF7 vs K562 +##> Wald test p-value: groupVar MCF7 vs K562 +##> DataFrame with 6 rows and 6 columns +##> baseMean log2FoldChange lfcSE stat +##> <numeric> <numeric> <numeric> <numeric> +##> ENSG00000185686 513.5554 -7.22327 0.498140 -14.50048 +##> ENSG00000197077 26.2357 9.09835 1.326114 6.86091 +##> ENSG00000283633 88.7407 -9.03039 1.431724 -6.30736 +##> ENSG00000099977 232.6720 1.85141 0.306629 6.03795 +##> ENSG00000169635 44.4275 -3.44047 0.579080 -5.94127 +##> ENSG00000100181 38.3698 -5.07047 0.892940 -5.67840 +##> pvalue +##> <numeric> +##> ENSG00000185686 0.0000000000000000000000000000000000000000000000120303 +##> ENSG00000197077 0.0000000000068425448545783570607904382313656081217312 +##> ENSG00000283633 0.0000000002838407468976333203669654163178986266968806 +##> ENSG00000099977 0.0000000015607995974142258612474572408387353539271913 +##> ENSG00000169635 0.0000000028282083250919303521398367383355917925591427 +##> ENSG00000100181 0.0000000135958879211409468852046023923732520621854292 +##> padj +##> <numeric> +##> ENSG00000185686 0.00000000000000000000000000000000000000000000714599 +##> ENSG00000197077 0.00000000203223582180977191457661517551974705853013 +##> ENSG00000283633 0.00000005620046788573139474432216163360398475390411 +##> ENSG00000099977 0.00000023177874021601254939083656177872816428475744 +##> ENSG00000169635 0.00000033599114902092130879429198592189820260500710 +##> ENSG00000100181 0.00000134599290419295367876952908281484866392929689
summary(deGeneRes) +##> +##> out of 594 with nonzero total read count +##> adjusted p-value < 0.1 +##> LFC > 0 (up) : 16, 2.7% +##> LFC < 0 (down) : 20, 3.4% +##> outliers [1] : 0, 0% +##> low counts [2] : 0, 0% +##> (mean count < 0) +##> [1] see 'cooksCutoff' argument of ?results +##> [2] see 'independentFiltering' argument of ?results
Plotting shrinked lFC results
+resLFC <- lfcShrink(dds.deseq, coef="groupVar_MCF7_vs_K562", type="apeglm") +plotMA(resLFC, ylim = c(-3,3))
We used DEXSeq to detect alternative used isoforms.
+library(DRIMSeq) +count.data <- as.data.frame(rowData(seExtended)) +count.data$gene_id <- count.data$GENEID +count.data$feature_id <- count.data$TXNAME +count.data$GENEID <- count.data$TXNAME <- NULL + +count.data <- cbind(count.data, assays(seExtended)$counts) + +sample.info <- as.data.frame(colData(seExtended)) +sample.info$sample_id <- sample.info$name +sample.info$name <- NULL +d <- dmDSdata(counts=count.data, samples=sample.info) + +n_samp_gene <- 1 +n_samp_feature <- 1 +min_count_gene <- 1 +min_count_feature <- 1 +dFilter <- dmFilter(d, + min_samps_feature_expr = n_samp_feature, + min_samps_feature_prop = n_samp_feature, + min_samps_gene_expr = n_samp_gene, + min_feature_expr = min_count_feature, + min_gene_expr = min_count_gene, + min_feature_prop=0.1) +table(table(counts(dFilter)$gene_id)) ## number of isoforms +##> +##> 2 3 4 5 6 7 8 10 +##> 23 15 21 12 8 6 4 1
library(DEXSeq) +formulaFullModel <- as.formula("~sample + exon + groupVar:exon") + + +dxd <- DEXSeqDataSet(countData=round(as.matrix(counts(dFilter)[,-c(1:2)])), + sampleData=DRIMSeq::samples(dFilter), + design=formulaFullModel, + featureID = counts(dFilter)$feature_id, + groupID=counts(dFilter)$gene_id) + + +system.time({ + dxd <- estimateSizeFactors(dxd) + print('Size factor estimated') + dxd <- estimateDispersions(dxd, formula = formulaFullModel) + print('Dispersion estimated') + #dxd <- estimateExonFoldChanges( dxd ) + dxd <- testForDEU(dxd, fullModel = formulaFullModel) + print('DEU tested') + dxd <- estimateExonFoldChanges(dxd, fitExpToVar="groupVar") + print('Exon fold changes estimated') +}) +##> [1] "Size factor estimated" +##> [1] "Dispersion estimated" +##> [1] "DEU tested" +##> [1] "Exon fold changes estimated" +##> user system elapsed +##> 7.860 0.040 7.903
dxr <- DEXSeqResults(dxd, independentFiltering=FALSE) +head(dxr) +##> +##> LRT p-value: full vs reduced +##> +##> DataFrame with 6 rows and 12 columns +##> groupID featureID exonBaseMean dispersion stat +##> <character> <character> <numeric> <numeric> <numeric> +##> gene.2:tx.2 gene.2 tx.2 2.714822 0.427670 3.150551 +##> gene.2:tx.3 gene.2 tx.3 0.654029 0.791030 0.140914 +##> gene.2:tx.4 gene.2 tx.4 2.567833 0.315359 1.003001 +##> gene.2:tx.66 gene.2 tx.66 2.107202 0.332461 0.422016 +##> gene.2:tx.67 gene.2 tx.67 1.243100 1.748445 1.683733 +##> gene.2:tx.122 gene.2 tx.122 1.865452 0.467004 1.043483 +##> pvalue padj K562 MCF7 log2fold_MCF7_K562 +##> <numeric> <numeric> <numeric> <numeric> <numeric> +##> gene.2:tx.2 0.0759013 0.99485 2.14944 0.0448427 -11.620740 +##> gene.2:tx.3 0.7073738 1.00000 1.15465 0.0448419 -9.506951 +##> gene.2:tx.4 0.3165853 1.00000 2.03209 1.1710398 -1.860342 +##> gene.2:tx.66 0.5159328 1.00000 1.58048 1.8911532 0.622989 +##> gene.2:tx.67 0.1944292 1.00000 1.05916 1.7073365 1.554900 +##> gene.2:tx.122 0.3070128 1.00000 1.44728 1.8878239 0.910422 +##> genomicData countData +##> <GRangesList> <matrix> +##> gene.2:tx.2 18:13:3:... +##> gene.2:tx.3 2:3:2:... +##> gene.2:tx.4 10:15:3:... +##> gene.2:tx.66 7:5:3:... +##> gene.2:tx.67 0:6:0:... +##> gene.2:tx.122 2:9:1:...
library(stageR) +strp <- function(x) substr(x,1,15) +qval <- perGeneQValue(dxr) +dxr.g <- data.frame(gene=names(qval),qval) + +columns <- c("featureID","groupID","pvalue") +dxr_pval <- as.data.frame(dxr[,columns]) +head(dxr_pval) +##> featureID groupID pvalue +##> gene.2:tx.2 tx.2 gene.2 0.07590133 +##> gene.2:tx.3 tx.3 gene.2 0.70737384 +##> gene.2:tx.4 tx.4 gene.2 0.31658535 +##> gene.2:tx.66 tx.66 gene.2 0.51593283 +##> gene.2:tx.67 tx.67 gene.2 0.19442922 +##> gene.2:tx.122 tx.122 gene.2 0.30701285 + +pConfirmation <- matrix(dxr_pval$pvalue,ncol=1) +dimnames(pConfirmation) <- list(strp(dxr_pval$featureID),"transcript") +pScreen <- qval +names(pScreen) <- strp(names(pScreen)) +tx2gene <- as.data.frame(dxr_pval[,c("featureID", "groupID")]) +for (i in 1:2) tx2gene[,i] <- strp(tx2gene[,i]) + + +stageRObj <- stageRTx(pScreen=pScreen, pConfirmation=pConfirmation, + pScreenAdjusted=TRUE, tx2gene=tx2gene) +stageRObj <- stageWiseAdjustment(stageRObj, method="dtu", alpha=0.5) +suppressWarnings({ + dex.padj <- getAdjustedPValues(stageRObj, order=FALSE, + onlySignificantGenes=TRUE) +})
dxrDT <- data.table(as.data.frame(dxr)) +setnames(dxrDT, old = c('groupID','featureID'), new = c('geneID','txID')) +dex.padj <- data.table(dex.padj) + +dxrDT <- dex.padj[dxrDT, on = c('geneID','txID')] +head(dxrDT) +##> geneID txID gene transcript exonBaseMean dispersion stat pvalue +##> 1: gene.2 tx.2 NA NA 2.7148218 0.4276702 3.1505509 0.07590133 +##> 2: gene.2 tx.3 NA NA 0.6540286 0.7910296 0.1409142 0.70737384 +##> 3: gene.2 tx.4 NA NA 2.5678325 0.3153589 1.0030014 0.31658535 +##> 4: gene.2 tx.66 NA NA 2.1072015 0.3324607 0.4220160 0.51593283 +##> 5: gene.2 tx.67 NA NA 1.2431000 1.7484453 1.6837326 0.19442922 +##> 6: gene.2 tx.122 NA NA 1.8654524 0.4670041 1.0434828 0.30701285 +##> padj K562 MCF7 log2fold_MCF7_K562 genomicData +##> 1: 0.9948496 2.149443 0.04484265 -11.6207395 <GRanges> +##> 2: 1.0000000 1.154654 0.04484192 -9.5069512 <GRanges> +##> 3: 1.0000000 2.032089 1.17103980 -1.8603417 <GRanges> +##> 4: 1.0000000 1.580476 1.89115320 0.6229887 <GRanges> +##> 5: 1.0000000 1.059156 1.70733655 1.5549004 <GRanges> +##> 6: 1.0000000 1.447282 1.88782389 0.9104218 <GRanges> +##> countData.SGNex_K562_directcDNA_replicate1_run2 +##> 1: 18 +##> 2: 2 +##> 3: 10 +##> 4: 7 +##> 5: 0 +##> 6: 2 +##> countData.SGNex_K562_directcDNA_replicate4_run2 +##> 1: 13 +##> 2: 3 +##> 3: 15 +##> 4: 5 +##> 5: 6 +##> 6: 9 +##> countData.SGNex_K562_directRNA_replicate6_run1 +##> 1: 3 +##> 2: 2 +##> 3: 3 +##> 4: 3 +##> 5: 0 +##> 6: 1 +##> countData.SGNex_MCF7_directcDNA_replicate1_run2 +##> 1: 0 +##> 2: 0 +##> 3: 0 +##> 4: 1 +##> 5: 1 +##> 6: 1 +##> countData.SGNex_MCF7_directcDNA_replicate3_run3 +##> 1: 0 +##> 2: 0 +##> 3: 1 +##> 4: 2 +##> 5: 0 +##> 6: 2 +##> countData.SGNex_MCF7_directRNA_replicate4_run1 +##> 1: 0 +##> 2: 0 +##> 3: 0 +##> 4: 0 +##> 5: 1 +##> 6: 0
dxrDT[,sigLFC2:=(padj < 0.5&(abs(log2fold_MCF7_K562)>=2))] +ggplot(dxrDT, aes(y = log2fold_MCF7_K562, x = exonBaseMean, color = as.factor(padj<0.5)))+ + geom_point(size = 0.5)+ + scale_x_log10()+ + scale_color_manual(values = c('grey','indianred'), name = "Significant")+ + xlab("Mean of normalized counts")+ + ylab("Log2 Fold change")+ + theme_minimal()
dxrDT[padj<0.5,.(geneID, txID, log2fold_MCF7_K562,K562,MCF7)] +##> geneID txID log2fold_MCF7_K562 K562 MCF7 +##> 1: ENSG00000183597 tx.35 -2.925745 4.2504114 2.284457 +##> 2: ENSG00000184436 tx.44 -4.132398 3.3428343 1.103626 +##> 3: ENSG00000272779 tx.105 4.276779 0.6241729 2.320295 +##> 4: ENSG00000100030 ENST00000215832 1.112208 5.7031758 6.535581 +##> 5: ENSG00000100030 ENST00000398822 -1.438071 4.0021766 3.004256 +##> 6: ENSG00000169635 ENST00000407464 2.362562 1.9298346 3.404064 +##> 7: ENSG00000169635 ENST00000443632 -1.904184 4.0971165 2.785404 +##> 8: ENSG00000215012 ENST00000407472 -1.753270 3.6246114 2.462383
Bambu can be used for transcript discovery and quantification from long read RNA-Seq data. Here, we present an example usage of bambu on Nanopore long read RNA-Sequencing from 2 human cancer cell lines
+To demonstrate the usage of Bambu, we used the data in NanoporeRNASeq, which contains single chromosome RNA-Seq data from two common cell lines K562 and MCF7. Each of these cell line has three replicates, with 1 direct RNA sequencing data and 2 cDNA sequencing data.
+library(NanoporeRNASeq)
+library(bambu)data("sample_info")
+sample_info
+##> sample_id Platform cellLine protocol
+##> 1: SGNex_K562_directcDNA_replicate1_run2 MinION K562 directcDNA
+##> 2: SGNex_K562_directcDNA_replicate4_run2 GridION K562 directcDNA
+##> 3: SGNex_K562_directRNA_replicate6_run1 GridION K562 directRNA
+##> 4: SGNex_MCF7_directcDNA_replicate1_run2 MinION MCF7 directcDNA
+##> 5: SGNex_MCF7_directcDNA_replicate3_run3 GridION MCF7 directcDNA
+##> 6: SGNex_MCF7_directRNA_replicate4_run1 GridION MCF7 directRNA
+##> bioRep cancer_type
+##> 1: replicate1 Leukocyte
+##> 2: replicate4 Leukocyte
+##> 3: replicate6 Leukocyte
+##> 4: replicate1 Breast
+##> 5: replicate3 Breast
+##> 6: replicate4 BreastName of bamfiles can be loaded as follows
+data("bamFileNames")
+bamFileNames
+##> [1] "SGNex_K562_directcDNA_replicate1_run2_genome_chr22_1_25409234.bam"
+##> [2] "SGNex_K562_directcDNA_replicate4_run2_genome_chr22_1_25409234.bam"
+##> [3] "SGNex_K562_directRNA_replicate6_run1_genome_chr22_1_25409234.bam"
+##> [4] "SGNex_MCF7_directcDNA_replicate1_run2_genome_chr22_1_25409234.bam"
+##> [5] "SGNex_MCF7_directcDNA_replicate3_run3_genome_chr22_1_25409234.bam"
+##> [6] "SGNex_MCF7_directRNA_replicate4_run1_genome_chr22_1_25409234.bam"We then loaded bam.files.
+bam.file <- system.file("extdata",bamFileNames, package = "NanoporeRNASeq")
+data("annotationGrangesList_chr22_1_25409234")We applied bambu to perform EM on extended annotations
+seExtended <- bambu(reads = bam.file, annotations = annotationGrangesList_chr22_1_25409234, genomeSequence = "BSgenome.Hsapiens.NCBI.GRCh38", extendAnnotations = TRUE, verbose = FALSE, ncore = 6)
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+seExtended
+##> class: RangedSummarizedExperiment
+##> dim: 1923 6
+##> metadata(0):
+##> assays(2): counts CPM
+##> rownames(1923): tx.1 tx.2 ... ENST00000484509 ENST00000468442
+##> rowData names(4): TXNAME GENEID eqClass newTxClass
+##> colnames(6):
+##> SGNex_K562_directcDNA_replicate1_run2_genome_chr22_1_25409234
+##> SGNex_K562_directcDNA_replicate4_run2_genome_chr22_1_25409234 ...
+##> SGNex_MCF7_directcDNA_replicate3_run3_genome_chr22_1_25409234
+##> SGNex_MCF7_directRNA_replicate4_run1_genome_chr22_1_25409234
+##> colData names(1): namebambu allows quantification without isoform discovery
+se <- bambu(reads = bam.file, annotations = annotationGrangesList_chr22_1_25409234, genomeSequence = "BSgenome.Hsapiens.NCBI.GRCh38", extendAnnotations = FALSE, verbose = FALSE, ncore = 6)
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+se
+##> class: RangedSummarizedExperiment
+##> dim: 1500 6
+##> metadata(0):
+##> assays(2): counts CPM
+##> rownames(1500): ENST00000624155 ENST00000422332 ... ENST00000484509
+##> ENST00000468442
+##> rowData names(3): TXNAME GENEID eqClass
+##> colnames(6):
+##> SGNex_K562_directcDNA_replicate1_run2_genome_chr22_1_25409234
+##> SGNex_K562_directcDNA_replicate4_run2_genome_chr22_1_25409234 ...
+##> SGNex_MCF7_directcDNA_replicate3_run3_genome_chr22_1_25409234
+##> SGNex_MCF7_directRNA_replicate4_run1_genome_chr22_1_25409234
+##> colData names(1): nameWe can check the estimated transcript expression using heatmap:
+colData(seExtended)$groupVar <- unlist(lapply(colnames(seExtended),function(x) unlist(strsplit(x,"_"))[2]))
+colnames(seExtended) <- gsub("_genome_chr22_1_25409234","",colnames(seExtended))
+colData(seExtended)$name <- gsub("_genome_chr22_1_25409234","",colData(seExtended)$name)
+plot.bambu(seExtended, group.variable = "groupVar", type = "heatmap")
or with PCA plot
+colData(seExtended)$groupVar <- unlist(lapply(colnames(seExtended),function(x) unlist(strsplit(x,"_"))[2]))
+plot.bambu(seExtended, group.variable = "groupVar", type = "pca")
Single gene examples can also be checked using plot functions from bambu
+plot.bambu(seExtended, type = "annotation", gene_id = unique(rowData(seExtended)$GENEID)[10])
##> [[1]]
+##> TableGrob (3 x 1) "arrange": 3 grobs
+##> z cells name grob
+##> 1 1 (2-2,1-1) arrange gtable[layout]
+##> 2 2 (3-3,1-1) arrange gtable[layout]
+##> 3 3 (1-1,1-1) arrange text[GRID.text.210]Gene expression can be obtained from transcript expression using this function:
+seGene <- transcriptToGeneExpression(seExtended)
+seGene
+##> class: RangedSummarizedExperiment
+##> dim: 887 6
+##> metadata(0):
+##> assays(2): counts CPM
+##> rownames(887): ENSG00000015475 ENSG00000040608 ... gene.98 gene.99
+##> rowData names(2): GENEID newGeneClass
+##> colnames(6): SGNex_K562_directcDNA_replicate1_run2
+##> SGNex_K562_directcDNA_replicate4_run2 ...
+##> SGNex_MCF7_directcDNA_replicate3_run3
+##> SGNex_MCF7_directRNA_replicate4_run1
+##> colData names(2): name groupVarGene expression heatmap
+colData(seGene)$groupVar <- unlist(lapply(colnames(seGene),function(x) unlist(strsplit(x,"_"))[2]))
+plot.bambu(seGene, group.variable = "groupVar", type = "heatmap")
Gene expression PCA plot
+colData(seGene)$groupVar <- unlist(lapply(colnames(seGene),function(x) unlist(strsplit(x,"_"))[2]))
+plot.bambu(seGene, group.variable = "groupVar", type = "pca")
We used DESeq2 to find the differentially expressed genes:
+library(DESeq2)
+dds <- DESeqDataSetFromMatrix(apply(assays(seGene)$counts,c(1,2),round),#tmp_wide[,-1],
+ colData = colData(seExtended),
+ design = ~ groupVar)
+system.time(dds.deseq <- DESeq(dds))
+##> user system elapsed
+##> 2.020 0.068 2.089
+
+deGeneRes <- DESeq2::results(dds.deseq, independentFiltering=FALSE)head(deGeneRes[order(deGeneRes$padj),])
+##> log2 fold change (MLE): groupVar MCF7 vs K562
+##> Wald test p-value: groupVar MCF7 vs K562
+##> DataFrame with 6 rows and 6 columns
+##> baseMean log2FoldChange lfcSE stat
+##> <numeric> <numeric> <numeric> <numeric>
+##> ENSG00000185686 513.5554 -7.22327 0.498140 -14.50048
+##> ENSG00000197077 26.2357 9.09835 1.326114 6.86091
+##> ENSG00000283633 88.7407 -9.03039 1.431724 -6.30736
+##> ENSG00000099977 232.6720 1.85141 0.306629 6.03795
+##> ENSG00000169635 44.4275 -3.44047 0.579080 -5.94127
+##> ENSG00000100181 38.3698 -5.07047 0.892940 -5.67840
+##> pvalue
+##> <numeric>
+##> ENSG00000185686 0.0000000000000000000000000000000000000000000000120303
+##> ENSG00000197077 0.0000000000068425448545783570607904382313656081217312
+##> ENSG00000283633 0.0000000002838407468976333203669654163178986266968806
+##> ENSG00000099977 0.0000000015607995974142258612474572408387353539271913
+##> ENSG00000169635 0.0000000028282083250919303521398367383355917925591427
+##> ENSG00000100181 0.0000000135958879211409468852046023923732520621854292
+##> padj
+##> <numeric>
+##> ENSG00000185686 0.00000000000000000000000000000000000000000000714599
+##> ENSG00000197077 0.00000000203223582180977191457661517551974705853013
+##> ENSG00000283633 0.00000005620046788573139474432216163360398475390411
+##> ENSG00000099977 0.00000023177874021601254939083656177872816428475744
+##> ENSG00000169635 0.00000033599114902092130879429198592189820260500710
+##> ENSG00000100181 0.00000134599290419295367876952908281484866392929689summary(deGeneRes)
+##>
+##> out of 594 with nonzero total read count
+##> adjusted p-value < 0.1
+##> LFC > 0 (up) : 16, 2.7%
+##> LFC < 0 (down) : 20, 3.4%
+##> outliers [1] : 0, 0%
+##> low counts [2] : 0, 0%
+##> (mean count < 0)
+##> [1] see 'cooksCutoff' argument of ?results
+##> [2] see 'independentFiltering' argument of ?resultsplotMA(deGeneRes, ylim = c(-3,3))
Plotting shrinked lFC results
+resLFC <- lfcShrink(dds.deseq, coef="groupVar_MCF7_vs_K562", type="apeglm")
+plotMA(resLFC, ylim = c(-3,3))
We used DEXSeq to detect alternative used isoforms.
+library(DRIMSeq)
+count.data <- as.data.frame(rowData(seExtended))
+count.data$gene_id <- count.data$GENEID
+count.data$feature_id <- count.data$TXNAME
+count.data$GENEID <- count.data$TXNAME <- NULL
+
+count.data <- cbind(count.data, assays(seExtended)$counts)
+
+sample.info <- as.data.frame(colData(seExtended))
+sample.info$sample_id <- sample.info$name
+sample.info$name <- NULL
+d <- dmDSdata(counts=count.data, samples=sample.info)
+
+n_samp_gene <- 1
+n_samp_feature <- 1
+min_count_gene <- 1
+min_count_feature <- 1
+dFilter <- dmFilter(d,
+ min_samps_feature_expr = n_samp_feature,
+ min_samps_feature_prop = n_samp_feature,
+ min_samps_gene_expr = n_samp_gene,
+ min_feature_expr = min_count_feature,
+ min_gene_expr = min_count_gene,
+ min_feature_prop=0.1)
+table(table(counts(dFilter)$gene_id)) ## number of isoforms
+##>
+##> 2 3 4 5 6 7 8 10
+##> 23 15 21 12 8 6 4 1library(DEXSeq)
+formulaFullModel <- as.formula("~sample + exon + groupVar:exon")
+
+
+dxd <- DEXSeqDataSet(countData=round(as.matrix(counts(dFilter)[,-c(1:2)])),
+ sampleData=DRIMSeq::samples(dFilter),
+ design=formulaFullModel,
+ featureID = counts(dFilter)$feature_id,
+ groupID=counts(dFilter)$gene_id)
+
+
+system.time({
+ dxd <- estimateSizeFactors(dxd)
+ print('Size factor estimated')
+ dxd <- estimateDispersions(dxd, formula = formulaFullModel)
+ print('Dispersion estimated')
+ #dxd <- estimateExonFoldChanges( dxd )
+ dxd <- testForDEU(dxd, fullModel = formulaFullModel)
+ print('DEU tested')
+ dxd <- estimateExonFoldChanges(dxd, fitExpToVar="groupVar")
+ print('Exon fold changes estimated')
+})
+##> [1] "Size factor estimated"
+##> [1] "Dispersion estimated"
+##> [1] "DEU tested"
+##> [1] "Exon fold changes estimated"
+##> user system elapsed
+##> 7.816 0.024 7.844dxr <- DEXSeqResults(dxd, independentFiltering=FALSE)
+head(dxr)
+##>
+##> LRT p-value: full vs reduced
+##>
+##> DataFrame with 6 rows and 12 columns
+##> groupID featureID exonBaseMean dispersion stat
+##> <character> <character> <numeric> <numeric> <numeric>
+##> gene.2:tx.2 gene.2 tx.2 2.714822 0.427670 3.150551
+##> gene.2:tx.3 gene.2 tx.3 0.654029 0.791030 0.140914
+##> gene.2:tx.4 gene.2 tx.4 2.567833 0.315359 1.003001
+##> gene.2:tx.66 gene.2 tx.66 2.107202 0.332461 0.422016
+##> gene.2:tx.67 gene.2 tx.67 1.243100 1.748445 1.683733
+##> gene.2:tx.122 gene.2 tx.122 1.865452 0.467004 1.043483
+##> pvalue padj K562 MCF7 log2fold_MCF7_K562
+##> <numeric> <numeric> <numeric> <numeric> <numeric>
+##> gene.2:tx.2 0.0759013 0.99485 2.14944 0.0448427 -11.620740
+##> gene.2:tx.3 0.7073738 1.00000 1.15465 0.0448419 -9.506951
+##> gene.2:tx.4 0.3165853 1.00000 2.03209 1.1710398 -1.860342
+##> gene.2:tx.66 0.5159328 1.00000 1.58048 1.8911532 0.622989
+##> gene.2:tx.67 0.1944292 1.00000 1.05916 1.7073365 1.554900
+##> gene.2:tx.122 0.3070128 1.00000 1.44728 1.8878239 0.910422
+##> genomicData countData
+##> <GRangesList> <matrix>
+##> gene.2:tx.2 18:13:3:...
+##> gene.2:tx.3 2:3:2:...
+##> gene.2:tx.4 10:15:3:...
+##> gene.2:tx.66 7:5:3:...
+##> gene.2:tx.67 0:6:0:...
+##> gene.2:tx.122 2:9:1:...library(stageR)
+strp <- function(x) substr(x,1,15)
+qval <- perGeneQValue(dxr)
+dxr.g <- data.frame(gene=names(qval),qval)
+
+columns <- c("featureID","groupID","pvalue")
+dxr_pval <- as.data.frame(dxr[,columns])
+head(dxr_pval)
+##> featureID groupID pvalue
+##> gene.2:tx.2 tx.2 gene.2 0.07590133
+##> gene.2:tx.3 tx.3 gene.2 0.70737384
+##> gene.2:tx.4 tx.4 gene.2 0.31658535
+##> gene.2:tx.66 tx.66 gene.2 0.51593283
+##> gene.2:tx.67 tx.67 gene.2 0.19442922
+##> gene.2:tx.122 tx.122 gene.2 0.30701285
+
+pConfirmation <- matrix(dxr_pval$pvalue,ncol=1)
+dimnames(pConfirmation) <- list(strp(dxr_pval$featureID),"transcript")
+pScreen <- qval
+names(pScreen) <- strp(names(pScreen))
+tx2gene <- as.data.frame(dxr_pval[,c("featureID", "groupID")])
+for (i in 1:2) tx2gene[,i] <- strp(tx2gene[,i])
+
+
+stageRObj <- stageRTx(pScreen=pScreen, pConfirmation=pConfirmation,
+ pScreenAdjusted=TRUE, tx2gene=tx2gene)
+stageRObj <- stageWiseAdjustment(stageRObj, method="dtu", alpha=0.5)
+suppressWarnings({
+ dex.padj <- getAdjustedPValues(stageRObj, order=FALSE,
+ onlySignificantGenes=TRUE)
+})dxrDT <- data.table(as.data.frame(dxr))
+setnames(dxrDT, old = c('groupID','featureID'), new = c('geneID','txID'))
+dex.padj <- data.table(dex.padj)
+
+dxrDT <- dex.padj[dxrDT, on = c('geneID','txID')]
+head(dxrDT)
+##> geneID txID gene transcript exonBaseMean dispersion stat pvalue
+##> 1: gene.2 tx.2 NA NA 2.7148218 0.4276702 3.1505509 0.07590133
+##> 2: gene.2 tx.3 NA NA 0.6540286 0.7910296 0.1409142 0.70737384
+##> 3: gene.2 tx.4 NA NA 2.5678325 0.3153589 1.0030014 0.31658535
+##> 4: gene.2 tx.66 NA NA 2.1072015 0.3324607 0.4220160 0.51593283
+##> 5: gene.2 tx.67 NA NA 1.2431000 1.7484453 1.6837326 0.19442922
+##> 6: gene.2 tx.122 NA NA 1.8654524 0.4670041 1.0434828 0.30701285
+##> padj K562 MCF7 log2fold_MCF7_K562 genomicData
+##> 1: 0.9948496 2.149443 0.04484265 -11.6207395 <GRanges>
+##> 2: 1.0000000 1.154654 0.04484192 -9.5069512 <GRanges>
+##> 3: 1.0000000 2.032089 1.17103980 -1.8603417 <GRanges>
+##> 4: 1.0000000 1.580476 1.89115320 0.6229887 <GRanges>
+##> 5: 1.0000000 1.059156 1.70733655 1.5549004 <GRanges>
+##> 6: 1.0000000 1.447282 1.88782389 0.9104218 <GRanges>
+##> countData.SGNex_K562_directcDNA_replicate1_run2
+##> 1: 18
+##> 2: 2
+##> 3: 10
+##> 4: 7
+##> 5: 0
+##> 6: 2
+##> countData.SGNex_K562_directcDNA_replicate4_run2
+##> 1: 13
+##> 2: 3
+##> 3: 15
+##> 4: 5
+##> 5: 6
+##> 6: 9
+##> countData.SGNex_K562_directRNA_replicate6_run1
+##> 1: 3
+##> 2: 2
+##> 3: 3
+##> 4: 3
+##> 5: 0
+##> 6: 1
+##> countData.SGNex_MCF7_directcDNA_replicate1_run2
+##> 1: 0
+##> 2: 0
+##> 3: 0
+##> 4: 1
+##> 5: 1
+##> 6: 1
+##> countData.SGNex_MCF7_directcDNA_replicate3_run3
+##> 1: 0
+##> 2: 0
+##> 3: 1
+##> 4: 2
+##> 5: 0
+##> 6: 2
+##> countData.SGNex_MCF7_directRNA_replicate4_run1
+##> 1: 0
+##> 2: 0
+##> 3: 0
+##> 4: 0
+##> 5: 1
+##> 6: 0dxrDT[,sigLFC2:=(padj < 0.5&(abs(log2fold_MCF7_K562)>=2))]
+ggplot(dxrDT, aes(y = log2fold_MCF7_K562, x = exonBaseMean, color = as.factor(padj<0.5)))+
+ geom_point(size = 0.5)+
+ scale_x_log10()+
+ scale_color_manual(values = c('grey','indianred'), name = "Significant")+
+ xlab("Mean of normalized counts")+
+ ylab("Log2 Fold change")+
+ theme_minimal()
dxrDT[padj<0.5,.(geneID, txID, log2fold_MCF7_K562,K562,MCF7)]
+##> geneID txID log2fold_MCF7_K562 K562 MCF7
+##> 1: ENSG00000183597 tx.35 -2.925745 4.2504114 2.284457
+##> 2: ENSG00000184436 tx.44 -4.132398 3.3428343 1.103626
+##> 3: ENSG00000272779 tx.105 4.276779 0.6241729 2.320295
+##> 4: ENSG00000100030 ENST00000215832 1.112208 5.7031758 6.535581
+##> 5: ENSG00000100030 ENST00000398822 -1.438071 4.0021766 3.004256
+##> 6: ENSG00000169635 ENST00000407464 2.362562 1.9298346 3.404064
+##> 7: ENSG00000169635 ENST00000443632 -1.904184 4.0971165 2.785404
+##> 8: ENSG00000215012 ENST00000407472 -1.753270 3.6246114 2.462383bambu is a R package for multi-sample transcript discovery and quantification using long read RNA-Seq data. You can use bambu after read alignment to obtain expression estimates for known and novel transcripts and genes. The output from bambu can directly be used for visualisation and downstream analysis such as differential gene expression or transcript usage.
+
You can install bambu from github:
+
if (!requireNamespace("devtools", quietly = TRUE))
+ install.packages("devtools")
+devtools::install_github("GoekeLab/bambu")The default mode to run bambu is using a set of aligned reads (bam files), reference genome annotations (gtf file, TxDb object, or bambuAnnotation object), and reference genome sequence (fasta file or BSgenome). bambu* will return a summarizedExperiment object with the genomic coordinates for annotated and new transcripts and transcript expression estimates:
+```rscript library(bambu)
+test.bam <- system.file(“extdata”, “SGNex_A549_directRNA_replicate5_run1_chr9_1_1000000.bam”, package = “bambu”)
+se <- bambu(reads = test.bam, annotations = “TxDb.Hsapiens.UCSC.hg38.knownGene”, genomeSequence = “BSgenome.Hsapiens.NCBI.GRCh38”)
+```
+We highly recommend to use the same annotations that were used for genome alignment. If you have a gtf file and fasta file you can run bambu with the following options:
+test.bam <- system.file("extdata", "SGNex_A549_directRNA_replicate5_run1_chr9_1_1000000.bam", package = "bambu")
+
+fa.file <- system.file("extdata", "Homo_sapiens.GRCh38.dna_sm.primary_assembly_chr9_1_1000000.fa", package = "bambu")
+
+gtf.file <- system.file("extdata", "Homo_sapiens.GRCh38.91_chr9_1_1000000.gtf", package = "bambu")
+
+bambuAnnotations <- prepareAnnotationsFromGTF(gtf.file)
+
+se <- bambu(reads = test.bam, annotations = bambuAnnotations, genomeSequence = fa.file)
+Quantification of annotated transcripts and genes only (no transcript/gene discovery)
+bambu(reads = test.bam, annotations = txdb, genomeSequence = fa.file, extendAnnotations = FALSE)Large sample number/ limited memory
+For larger sample numbers we recommend to write the processed data to a file:
bambu(reads = test.bam, readClass.outputDir = "./bambu/", annotations = bambuAnnotations, genomeSequence = fa.file)You can also use precalculated annotations.
+If you plan to run bambu more frequently, we recommend to save the bambuAnnotations object.
+The bambuAnnotation object can be calculated from a gtf file:
+annotations <- prepareAnnotationFromGTF(gtf.file)From TxDb object
+annotations <- prepareAnnotations(txdb)More stringent filtering thresholds imposed on potential novel transcripts
+++Keep novel transcripts with min 5 read count in at least 1 sample:
+
bambu(reads, annotations, genomeSequence, isoreParameters = list(min.readCount = 5))++Keep novel transcripts with min 5 samples having at least 2 counts:
+
bambu(reads, annotations, genomeSequence, isoreParameters = list(min.sampleNumber = 5))++Filter out transcripts with relative abundance within gene lower than 10%:
+
bambu(reads, annotations, genomeSequence, isoreParameters = list(min.readFractionByGene = 0.1))Quantification without bias correction
+++The default estimation automatically does bias correction for expression estimates. However, you can choose to perform the quantification without bias correction.
+
bambu(reads, annotations, genomeSequence, emParameters(bias = FALSE))Parallel computation
+++bambu allows parallel computation.
+
bambu(reads, annotations, genomeSequence, ncore = 8)See manual for details to customize other conditions.
+Transcript expression to gene expression
+transcriptToGeneExpression(se)Visualization
+++You can visualize the novel genes/transcripts using plot.bambu function
+
plot.bambu(se, type = "annotation", gene_id)
+
+plot.bambu(se, type = "annotation", transcript_id)++plot.bambu can also be used to visualize the clustering of input samples on gene/transcript expressions
+
plot.bambu(se, type = "heatmap") # heatmap
+
+plot.bambu(se, type = "pca") # PCA visualization++plot.bambu can also be used to visualize the clustering of input samples on gene/transcript expressions with grouping variable
+
plot.bambu(se, type = "heatmap", group.var) # heatmap
+
+plot.bambu(se, type = "pca", group.var) # PCA visualizationWrite bambu outputs to files
+++writeBambuOutput will generate three files, including a .gtf file for the extended annotations, and two .txt files for the expression counts at transcript and gene levels.
+
writeBambuOutput(se, path = "./bambu/")This package is developed and maintained byYing Chen, Yuk Kei Wan, Jonathan Goeke and at Genome Institute of Singapore. If you want to contribute, please leave an issue. Thank you.
+Outputs a GTF dataframe for the nanorna-bam nextflow pipeline
+SEtoGTF(se)+ +
| se | +a summarizedExperiment object from |
+
|---|
gtf a GTF dataframe
+ +This function takes bam file of genomic alignments and performs isoform recontruction and gene and transcript expression quantification. +It also allows saving of read class files of alignments, extending provided annotations, and quantification based on extended annotations. +When multiple samples are provided, extended annotations will be combined across samples to allow comparison.
+bambu( + reads = NULL, + readClass.file = NULL, + readClass.outputDir = NULL, + annotations = NULL, + genomeSequence = NULL, + stranded = FALSE, + ncore = 1, + yieldSize = NULL, + isoreParameters = NULL, + emParameters = NULL, + extendAnnotations = TRUE, + verbose = FALSE +)+ +
| reads | +A string or a vector of strings specifying the paths of bam files for genomic alignments, or a |
+
|---|---|
| readClass.file | +A string or a vector of strings specifying the read class files that are saved during previous run of |
+
| readClass.outputDir | +A string variable specifying the path to where read class files will be saved. |
+
| annotations | +A |
+
| genomeSequence | +A fasta file or a BSGenome object. |
+
| ncore | +specifying number of cores used when parallel processing is used, defaults to 1. |
+
| yieldSize | +see |
+
| isoreParameters | +A list of controlling parameters for isoform reconstruction process:
|
+
| emParameters | +A list of controlling parameters for quantification algorithm estimation process:
|
+
| extendAnnotations | +A logical variable indicating whether annotations are to be extended for quantification. |
+
| verbose | +A logical variable indicating whether processing messages will be printed. |
+
A list of two SummarizedExperiment object for transcript expression and gene expression.
+Main function
+ +# S3 method for bambu +plot( + se, + group.variable = NULL, + type = c("annotation", "pca", "heatmap"), + gene_id = NULL, + transcript_id = NULL +)+ +
| se | +An summarized experiment object obtained from bambu or |
+
|---|
Function to prepare tables and genomic ranges for transript reconstruction using a txdb object
+prepareAnnotations(txdb)+ +
| txdb | +a |
+
|---|
A GrangesList object
+if (FALSE) { + library(TxDb.Hsapiens.UCSC.hg38.knownGene) + txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene + prepareAnnotations(txdb) + }
GRangesListR/annotationFunctions.R
+ prepareAnnotationsFromGTF.RdPrepare annotation granges object from GTF file
+prepareAnnotationsFromGTF(file)+ +
| file | +a GTF file |
+
|---|
Outputs GRangesList object from reading a GTF file
+readFromGTF(file)+ +
| file | +a |
+
|---|
grlist a GRangesList object, with two columns
TXNAME specifying prefix for new gene Ids (genePrefix.number), defaults to empty
GENEID indicating whether filter to remove read classes which are a subset of known transcripts(), defaults to TRUE
R/transcriptToGeneExpression.R
+ transcriptToGeneExpression.RdReduce transcript expression to gene expression
+transcriptToGeneExpression(se)+ +
| se | +a summarizedExperiment object from |
+
|---|
R/readWrite.R
+ writeBambuOutput.RdOutputs a GTF file, transcript-count file, and gene-count file from bambu
+writeBambuOutput(se, path)+ +
| se | +a summarizedExperiment object from |
+
|---|---|
| path | +the destination of the output files (gtf, transcript counts, and gene counts) |
+
The function will generate three files, a .gtf file for the annotations,
+two .txt files for transcript and gene counts respectively.
Write annotation GRangesList into a GTF file
+writeToGTF(annotation, file, geneIDs = NULL)+ +
| annotation | +a |
+
|---|---|
| file | +the output gtf file name |
+
| geneIDs | +an optional dataframe of geneIDs (column 2) with the corresponding transcriptIDs (column 1) |
+
gtf a GTF dataframe
+ +