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       3       5       2       2      61      13     212       6     107
gene2       1      10     106       1      22       1     335       2      36
gene3       7      98      18       1      30      64      41      35      17
gene4      41       6       1      60      16     143       1      11       4
gene5      10       3     319       1       1     312      43       3      22
gene6       1      81     449       4     362       4      19      74     532
      sample10 sample11 sample12 sample13 sample14 sample15 sample16 sample17
gene1      273        2       71       30       43       14        1        1
gene2        5        1      120        6       16        2        7      805
gene3       55      120        6       59        1       11       14        1
gene4      572       31        1      344       58       12       20      330
gene5       26      109        5        8       26       76      200      166
gene6      267       35       69       59      243      188       21      632
      sample18 sample19 sample20
gene1      155      289      799
gene2       64       95        1
gene3      308      313       10
gene4       27        7        4
gene5      139      340       12
gene6       11       95        4

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 36.45066 -0.02413811 1.2940361 0.57054527    0
sample2 58.61363  0.05496400 0.8194974 0.81123820    1
sample3 63.32145 -0.00449474 0.3444390 0.69527733    1
sample4 60.62626  0.26733796 0.1804601 0.04029040    2
sample5 44.44742  0.77958638 0.5657089 0.09589169    1
sample6 75.39852  0.54212324 0.8143508 0.97449953    0

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   90.0788   1.00143 0.000371608 0.9952494  0.995249   217.150   224.121
gene2   80.7395   1.00025 1.114631449 0.2910806  0.748239   192.597   199.568
gene3   42.4583   1.00008 0.016321577 0.8985639  0.916902   206.056   213.026
gene4   81.8683   1.00031 0.201885112 0.6537538  0.806844   216.570   223.540
gene5   80.2695   1.00006 5.572949511 0.0182452  0.210294   218.095   225.066
gene6  145.3781   1.00005 0.154988158 0.6938859  0.806844   239.810   246.781

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   90.0788 -0.124537  0.399229 -0.311943 0.75508363 0.8850563   217.150
gene2   80.7395 -1.069347  0.388345 -2.753602 0.00589435 0.0982391   192.597
gene3   42.4583 -0.772035  0.340774 -2.265536 0.02347981 0.1230501   206.056
gene4   81.8683 -0.440174  0.413360 -1.064868 0.28693568 0.5313624   216.570
gene5   80.2695 -0.705700  0.351417 -2.008156 0.04462673 0.1716413   218.095
gene6  145.3781 -0.740655  0.334364 -2.215113 0.02675232 0.1230501   239.810
            BIC
      <numeric>
gene1   224.121
gene2   199.568
gene3   213.026
gene4   223.540
gene5   225.066
gene6   246.781

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   90.0788  1.4089402  1.058595  1.3309531 0.1832044  0.436201   217.150
gene2   80.7395  0.3354875  1.035418  0.3240117 0.7459292  0.925890   192.597
gene3   42.4583 -0.2760074  0.900537 -0.3064920 0.7592301  0.925890   206.056
gene4   81.8683  0.0691645  1.093739  0.0632368 0.9495779  0.961397   216.570
gene5   80.2695 -2.0069057  0.932441 -2.1523139 0.0313726  0.235583   218.095
gene6  145.3781  0.8913475  0.887066  1.0048265 0.3149804  0.627228   239.810
            BIC
      <numeric>
gene1   224.121
gene2   199.568
gene3   213.026
gene4   223.540
gene5   225.066
gene6   246.781

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>
gene26  110.2313   1.00010  13.22154 0.00027713 0.0138565   205.076   212.046
gene34   55.0388   1.00023   9.13066 0.00252013 0.0630032   205.670   212.640
gene40  106.3222   1.00015   6.35095 0.01174566 0.1957609   228.787   235.757
gene5    80.2695   1.00006   5.57295 0.01824521 0.2102943   218.095   225.066
gene33  118.1699   1.00015   5.17247 0.02297736 0.2102943   240.353   247.323
gene35   54.7496   1.00019   5.00944 0.02523531 0.2102943   207.447   214.418

Session info

R version 4.2.1 (2022-06-23)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.5 LTS

Matrix products: default
BLAS:   /home/biocbuild/bbs-3.16-bioc/R/lib/libRblas.so
LAPACK: /home/biocbuild/bbs-3.16-bioc/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_GB              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] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] ggplot2_3.3.6               BiocParallel_1.32.0        
 [3] NBAMSeq_1.14.0              SummarizedExperiment_1.28.0
 [5] Biobase_2.58.0              GenomicRanges_1.50.0       
 [7] GenomeInfoDb_1.34.0         IRanges_2.32.0             
 [9] S4Vectors_0.36.0            BiocGenerics_0.44.0        
[11] MatrixGenerics_1.10.0       matrixStats_0.62.0         

loaded via a namespace (and not attached):
 [1] httr_1.4.4             sass_0.4.2             bit64_4.0.5           
 [4] jsonlite_1.8.3         splines_4.2.1          bslib_0.4.0           
 [7] assertthat_0.2.1       highr_0.9              blob_1.2.3            
[10] GenomeInfoDbData_1.2.9 yaml_2.3.6             pillar_1.8.1          
[13] RSQLite_2.2.18         lattice_0.20-45        glue_1.6.2            
[16] digest_0.6.30          RColorBrewer_1.1-3     XVector_0.38.0        
[19] colorspace_2.0-3       htmltools_0.5.3        Matrix_1.5-1          
[22] DESeq2_1.38.0          XML_3.99-0.12          pkgconfig_2.0.3       
[25] genefilter_1.80.0      zlibbioc_1.44.0        xtable_1.8-4          
[28] scales_1.2.1           tibble_3.1.8           annotate_1.76.0       
[31] mgcv_1.8-41            KEGGREST_1.38.0        farver_2.1.1          
[34] generics_0.1.3         withr_2.5.0            cachem_1.0.6          
[37] cli_3.4.1              survival_3.4-0         magrittr_2.0.3        
[40] crayon_1.5.2           memoise_2.0.1          evaluate_0.17         
[43] fansi_1.0.3            nlme_3.1-160           tools_4.2.1           
[46] lifecycle_1.0.3        stringr_1.4.1          locfit_1.5-9.6        
[49] munsell_0.5.0          DelayedArray_0.24.0    AnnotationDbi_1.60.0  
[52] Biostrings_2.66.0      compiler_4.2.1         jquerylib_0.1.4       
[55] rlang_1.0.6            grid_4.2.1             RCurl_1.98-1.9        
[58] labeling_0.4.2         bitops_1.0-7           rmarkdown_2.17        
[61] gtable_0.3.1           codetools_0.2-18       DBI_1.1.3             
[64] R6_2.5.1               knitr_1.40             dplyr_1.0.10          
[67] fastmap_1.1.0          bit_4.0.4              utf8_1.2.2            
[70] stringi_1.7.8          parallel_4.2.1         Rcpp_1.0.9            
[73] vctrs_0.5.0            geneplotter_1.76.0     png_0.1-7             
[76] tidyselect_1.2.0       xfun_0.34             

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.