Installation

To install and load NBAMSeq

Introduction

High-throughput sequencing experiments followed by differential expression analysis is a widely used approach to detect genomic biomarkers. A fundamental step in differential expression analysis is to model the association between gene counts and covariates of interest. NBAMSeq is a flexible statistical model based on the generalized additive model and allows for information sharing across genes in variance estimation. Specifically, we model the logarithm of mean gene counts as sums of smooth functions with the smoothing parameters and coefficients estimated simultaneously by a nested iteration. The variance is estimated by the Bayesian shrinkage approach to fully exploit the information across all genes.

The workflow of NBAMSeq contains three main steps:

Here we illustrate each of these steps respectively.

Data input

Users are expected to provide three parts of input, i.e. countData, colData, and design.

countData is a matrix of gene counts generated by RNASeq experiments.

      sample1 sample2 sample3 sample4 sample5 sample6 sample7 sample8 sample9
gene1     143     301       5      17      16      89      12     111     190
gene2       2      84      63      13       2       8      11       2     208
gene3     587      58       1       6      73      16     105     196      79
gene4       1      82      34       2      98       2      10     110     242
gene5     218      54      25       5       4       1      81      41      16
gene6      57     413      74      36      34       4      49      62     107
      sample10 sample11 sample12 sample13 sample14 sample15 sample16 sample17
gene1       75        4      321        2      488        1      526      166
gene2      321      149        1        1        7      221       53      451
gene3        3      107        2        6       13      292       48      184
gene4        2      200      303       11      116       39       24       10
gene5       54       56       43       12      235       23        1      269
gene6       20      826      445       11        1       92        6        3
      sample18 sample19 sample20
gene1       13       57       70
gene2      192        2       43
gene3      537      111       79
gene4       44      147        4
gene5       18        1        8
gene6       18      126        1

colData is a data frame which contains the covariates of samples. The sample order in colData should match the sample order in countData.

           pheno       var1       var2       var3 var4
sample1 24.84861 -0.4263403 -0.7057702 -0.7205817    1
sample2 53.86174 -0.3463108  1.2745028  0.6958805    1
sample3 42.57126 -0.3985668 -0.5206388 -0.7236395    0
sample4 24.29973 -2.5448176 -0.3661942 -0.4673764    1
sample5 41.38161 -0.5092604  0.5738038  1.3869155    0
sample6 43.10515  1.5722885  0.6024634  0.3095557    2

design is a formula which specifies how to model the samples. Compared with other packages performing DE analysis including DESeq2 (Love, Huber, and Anders 2014), edgeR (Robinson, McCarthy, and Smyth 2010), NBPSeq (Di et al. 2015) and BBSeq (Zhou, Xia, and Wright 2011), NBAMSeq supports the nonlinear model of covariates via mgcv (Wood and Wood 2015). To indicate the nonlinear covariate in the model, users are expected to use s(variable_name) in the design formula. In our example, if we would like to model pheno as a nonlinear covariate, the design formula should be:

Several notes should be made regarding the design formula:

We then construct the NBAMSeqDataSet using countData, colData, and design:

class: NBAMSeqDataSet 
dim: 50 20 
metadata(1): fitted
assays(1): counts
rownames(50): gene1 gene2 ... gene49 gene50
rowData names(0):
colnames(20): sample1 sample2 ... sample19 sample20
colData names(5): pheno var1 var2 var3 var4

Differential expression analysis

Differential expression analysis can be performed by NBAMSeq function:

Several other arguments in NBAMSeq function are available for users to customize the analysis.

Pulling out DE results

Results of DE analysis can be pulled out by results function. For continuous covariates, the name argument should be specified indicating the covariate of interest. For nonlinear continuous covariates, base mean, effective degrees of freedom (edf), test statistics, p-value, and adjusted p-value will be returned.

DataFrame with 6 rows and 7 columns
       baseMean       edf      stat    pvalue      padj       AIC       BIC
      <numeric> <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
gene1  144.4547   1.00004 1.6949915  0.192963  0.480680   236.796   243.766
gene2   51.1028   1.00015 6.3047353  0.012046  0.150575   210.884   217.854
gene3   84.8827   1.00006 0.0040092  0.949882  0.969268   231.887   238.857
gene4   69.2996   1.00009 0.0402864  0.841164  0.876213   214.825   221.795
gene5   51.3963   1.00007 0.7415260  0.389192  0.689670   207.379   214.349
gene6   96.6872   1.00009 0.1686986  0.681359  0.857476   229.689   236.659

For linear continuous covariates, base mean, estimated coefficient, standard error, test statistics, p-value, and adjusted p-value will be returned.

DataFrame with 6 rows and 8 columns
       baseMean       coef        SE       stat     pvalue      padj       AIC
      <numeric>  <numeric> <numeric>  <numeric>  <numeric> <numeric> <numeric>
gene1  144.4547  1.0226938  0.392244  2.6072906 0.00912619 0.0912619   236.796
gene2   51.1028 -0.5052109  0.404008 -1.2504960 0.21111843 0.6043199   210.884
gene3   84.8827  0.2987359  0.372324  0.8023538 0.42234835 0.6812070   231.887
gene4   69.2996  0.0127559  0.389064  0.0327862 0.97384506 0.9863074   214.825
gene5   51.3963  1.0922873  0.396006  2.7582623 0.00581096 0.0885365   207.379
gene6   96.6872  0.2786991  0.424016  0.6572839 0.51099839 0.7553220   229.689
            BIC
      <numeric>
gene1   243.766
gene2   217.854
gene3   238.857
gene4   221.795
gene5   214.349
gene6   236.659

For discrete covariates, the contrast argument should be specified. e.g.  contrast = c("var4", "2", "0") means comparing level 2 vs. level 0 in var4.

DataFrame with 6 rows and 8 columns
       baseMean      coef        SE      stat      pvalue       padj       AIC
      <numeric> <numeric> <numeric> <numeric>   <numeric>  <numeric> <numeric>
gene1  144.4547 -1.057721  0.929951 -1.137394 0.255373462 0.75074647   236.796
gene2   51.1028  0.714560  0.957753  0.746079 0.455619579 0.75074647   210.884
gene3   84.8827 -0.755398  0.884438 -0.854100 0.393049659 0.75074647   231.887
gene4   69.2996 -3.547979  0.940428 -3.772727 0.000161473 0.00807364   214.825
gene5   51.3963 -0.231601  0.933215 -0.248175 0.803998854 0.94071026   207.379
gene6   96.6872 -2.275806  1.010505 -2.252147 0.024312980 0.24312980   229.689
            BIC
      <numeric>
gene1   243.766
gene2   217.854
gene3   238.857
gene4   221.795
gene5   214.349
gene6   236.659

Visualization

We suggest two approaches to visualize the nonlinear associations. The first approach is to plot the smooth components of a fitted negative binomial additive model by plot.gam function in mgcv (Wood and Wood 2015). This can be done by calling makeplot function and passing in NBAMSeqDataSet object. Users are expected to provide the phenotype of interest in phenoname argument and gene of interest in genename argument.

In addition, to explore the nonlinear association of covariates, it is also instructive to look at log normalized counts vs. variable scatter plot. Below we show how to produce such plot.

DataFrame with 6 rows and 7 columns
        baseMean       edf      stat      pvalue      padj       AIC       BIC
       <numeric> <numeric> <numeric>   <numeric> <numeric> <numeric> <numeric>
gene23  169.7333   1.00062  13.60397 0.000227063 0.0113532   240.298   247.268
gene47  106.5887   1.00030   8.86047 0.002918731 0.0654872   222.258   229.228
gene19  188.8400   1.00006   8.31706 0.003929229 0.0654872   253.499   260.470
gene2    51.1028   1.00015   6.30474 0.012046021 0.1505753   210.884   217.854
gene22   89.9088   1.00012   5.59447 0.018029285 0.1802928   209.042   216.012
gene45   72.9956   1.00043   4.98812 0.025596210 0.1855279   218.886   225.856

Session info

R Under development (unstable) (2020-10-17 r79346)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.1 LTS

Matrix products: default
BLAS:   /home/biocbuild/bbs-3.13-bioc/R/lib/libRblas.so
LAPACK: /home/biocbuild/bbs-3.13-bioc/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C              
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] parallel  stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] ggplot2_3.3.2               BiocParallel_1.25.0        
 [3] NBAMSeq_1.7.1               SummarizedExperiment_1.21.0
 [5] Biobase_2.51.0              GenomicRanges_1.43.0       
 [7] GenomeInfoDb_1.27.0         IRanges_2.25.0             
 [9] S4Vectors_0.29.0            BiocGenerics_0.37.0        
[11] MatrixGenerics_1.3.0        matrixStats_0.57.0         

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.5             locfit_1.5-9.4         lattice_0.20-41       
 [4] digest_0.6.27          R6_2.5.0               RSQLite_2.2.1         
 [7] evaluate_0.14          httr_1.4.2             pillar_1.4.6          
[10] zlibbioc_1.37.0        rlang_0.4.8            annotate_1.69.0       
[13] blob_1.2.1             Matrix_1.2-18          rmarkdown_2.5         
[16] labeling_0.4.2         splines_4.1.0          geneplotter_1.69.0    
[19] stringr_1.4.0          RCurl_1.98-1.2         bit_4.0.4             
[22] munsell_0.5.0          DelayedArray_0.17.0    compiler_4.1.0        
[25] xfun_0.18              pkgconfig_2.0.3        mgcv_1.8-33           
[28] htmltools_0.5.0        tidyselect_1.1.0       tibble_3.0.4          
[31] GenomeInfoDbData_1.2.4 XML_3.99-0.5           withr_2.3.0           
[34] crayon_1.3.4           dplyr_1.0.2            bitops_1.0-6          
[37] grid_4.1.0             nlme_3.1-150           xtable_1.8-4          
[40] gtable_0.3.0           lifecycle_0.2.0        DBI_1.1.0             
[43] magrittr_1.5           scales_1.1.1           stringi_1.5.3         
[46] farver_2.0.3           XVector_0.31.0         genefilter_1.73.0     
[49] ellipsis_0.3.1         vctrs_0.3.4            generics_0.0.2        
[52] RColorBrewer_1.1-2     tools_4.1.0            bit64_4.0.5           
[55] glue_1.4.2             DESeq2_1.31.0          purrr_0.3.4           
[58] survival_3.2-7         yaml_2.2.1             AnnotationDbi_1.53.0  
[61] colorspace_1.4-1       memoise_1.1.0          knitr_1.30            

References

Di, Y, DW Schafer, JS Cumbie, and JH Chang. 2015. “NBPSeq: Negative Binomial Models for Rna-Sequencing Data.” R Package Version 0.3. 0, URL Http://CRAN. R-Project. Org/Package= NBPSeq.

Love, Michael I, Wolfgang Huber, and Simon Anders. 2014. “Moderated Estimation of Fold Change and Dispersion for Rna-Seq Data with Deseq2.” Genome Biology 15 (12): 550.

Robinson, Mark D, Davis J McCarthy, and Gordon K Smyth. 2010. “EdgeR: A Bioconductor Package for Differential Expression Analysis of Digital Gene Expression Data.” Bioinformatics 26 (1): 139–40.

Wood, Simon, and Maintainer Simon Wood. 2015. “Package ’Mgcv’.” R Package Version 1: 29.

Zhou, Yi-Hui, Kai Xia, and Fred A Wright. 2011. “A Powerful and Flexible Approach to the Analysis of Rna Sequence Count Data.” Bioinformatics 27 (19): 2672–8.