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
gene1      39      34     115      17      19      13      46      48
gene2       2      93      20      17      16     287      83     134
gene3      43     588       3       2     128      17       4      20
gene4     271      59       2     122       5     101       2     181
gene5      14       1       8     364      10      21      15     280
gene6      52      29       9     258      56     678     138     576
      sample9 sample10 sample11 sample12 sample13 sample14 sample15
gene1       1       26        1        6       67        3       65
gene2      26      387        2        7      167        2      245
gene3       4       44        9      341      126       80        4
gene4      61       53      121       43      237      454       35
gene5      81        1       24        1        1       30       26
gene6     353        1       28        1      143        1      227
      sample16 sample17 sample18 sample19 sample20
gene1      232      285        4      700      116
gene2       53        6      412      512       12
gene3       69      105      172        2      114
gene4        1        8      185        9        1
gene5       25      980        1        1       14
gene6       10     1185       57       30       25

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 45.68189 -0.56434193 -0.1173612 -0.2519788    2
sample2 51.33442  0.41000606 -0.1617071 -1.4036705    1
sample3 56.87139 -1.06008148 -0.7716984  0.7171770    0
sample4 48.95940 -1.10558773  1.3713330  1.4466994    2
sample5 70.56267 -0.07950669 -0.1453455 -0.3814853    0
sample6 23.68780 -0.13524617 -2.0316586  0.8921058    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 5 columns
              baseMean              edf              stat
             <numeric>        <numeric>         <numeric>
gene1 57.9095664566279 1.00013573983602 0.443762157866825
gene2 86.9893358937095 1.00015413972742 0.696341277787806
gene3 96.3182231397205 1.00008808109268   3.3451279268653
gene4 96.5334722249479  1.0001212353405  1.72679365866303
gene5 57.5197708541638 1.00004614055739  14.2256946779633
gene6 114.710057881245 1.00005900632081  2.11630088134053
                    pvalue                padj
                 <numeric>           <numeric>
gene1    0.505368526554384   0.721955037934834
gene2    0.404116474333547   0.645664960260843
gene3   0.0674377082015472   0.263946328639829
gene4    0.188830494803121   0.496922354745055
gene5 0.000162189177037631 0.00444919490901165
gene6     0.14576016275905   0.448691678733603

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 6 columns
              baseMean               coef                SE
             <numeric>          <numeric>         <numeric>
gene1 57.9095664566279 -0.401793435753404 0.347949493681804
gene2 86.9893358937095  0.493214148596027 0.329380396443183
gene3 96.3182231397205   0.62203804067925  0.31008405330605
gene4 96.5334722249479  0.244933096755823 0.335373492008499
gene5 57.5197708541638 -0.923276768185908  0.30269803549055
gene6 114.710057881245 0.0101278789668535 0.326515510276744
                    stat              pvalue               padj
               <numeric>           <numeric>          <numeric>
gene1  -1.15474643029899    0.24819427997359  0.427921172368259
gene2   1.49739982683245   0.134289253323167  0.419653916634897
gene3   2.00603041029428  0.0448530035621007  0.196626801368276
gene4  0.730329327130052   0.465188906005355  0.684101332360817
gene5  -3.05015778080506 0.00228721173661384 0.0571802934153459
gene6 0.0310180639145424    0.97525513368319   0.97525513368319

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 6 columns
              baseMean               coef                SE
             <numeric>          <numeric>         <numeric>
gene1 57.9095664566279 -0.152465715341341 0.935714002037772
gene2 86.9893358937095   1.11573051233849 0.883856419286787
gene3 96.3182231397205  0.173412175199927 0.837043639096083
gene4 96.5334722249479   3.28186034225279 0.907408045493527
gene5 57.5197708541638   1.56117400974899  0.80186490354437
gene6 114.710057881245  0.345544858838881 0.874950313153578
                    stat               pvalue               padj
               <numeric>            <numeric>          <numeric>
gene1 -0.162940508541397    0.870565275804782   0.94219887524746
gene2   1.26234362051566    0.206825166950517  0.537753664004689
gene3  0.207172203574946    0.835875374708727   0.94219887524746
gene4   3.61674150736434 0.000298334964479384 0.0149167482239692
gene5   1.94692896876813   0.0515432510641139  0.293875987158664
gene6  0.394930836236215    0.692893931915481   0.94219887524746

Visualization

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

DataFrame with 6 rows and 5 columns
               baseMean              edf             stat
              <numeric>        <numeric>        <numeric>
gene5  57.5197708541638 1.00004614055739 14.2256946779633
gene25 57.7613282230133 1.00005291537191 14.0512063570746
gene18 64.1473384266057 1.00005356491994 12.1653257223555
gene8  82.8188382925501 1.00004202638168 9.24046180727509
gene29 79.9065394999251 1.00005026428115 8.86975210650455
gene45  69.256376139901 1.00015760899572  7.9436471763423
                     pvalue                padj
                  <numeric>           <numeric>
gene5  0.000162189177037631 0.00444919490901165
gene25 0.000177967796360466 0.00444919490901165
gene18 0.000486986742951584 0.00811644571585973
gene8   0.00236756958590585  0.0289967213068209
gene29  0.00289967213068209  0.0289967213068209
gene45  0.00482474647616751  0.0384570436076805

Session info

R version 3.6.0 (2019-04-26)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows Server 2012 R2 x64 (build 9600)

Matrix products: default

locale:
[1] LC_COLLATE=C                          
[2] LC_CTYPE=English_United States.1252   
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    

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

other attached packages:
 [1] ggplot2_3.1.1               NBAMSeq_1.0.0              
 [3] SummarizedExperiment_1.14.0 DelayedArray_0.10.0        
 [5] BiocParallel_1.18.0         matrixStats_0.54.0         
 [7] Biobase_2.44.0              GenomicRanges_1.36.0       
 [9] GenomeInfoDb_1.20.0         IRanges_2.18.0             
[11] S4Vectors_0.22.0            BiocGenerics_0.30.0        

loaded via a namespace (and not attached):
 [1] bit64_0.9-7            splines_3.6.0          Formula_1.2-3         
 [4] assertthat_0.2.1       latticeExtra_0.6-28    blob_1.1.1            
 [7] GenomeInfoDbData_1.2.1 yaml_2.2.0             pillar_1.3.1          
[10] RSQLite_2.1.1          backports_1.1.4        lattice_0.20-38       
[13] glue_1.3.1             digest_0.6.18          RColorBrewer_1.1-2    
[16] XVector_0.24.0         checkmate_1.9.1        colorspace_1.4-1      
[19] htmltools_0.3.6        Matrix_1.2-17          plyr_1.8.4            
[22] DESeq2_1.24.0          XML_3.98-1.19          pkgconfig_2.0.2       
[25] genefilter_1.66.0      zlibbioc_1.30.0        purrr_0.3.2           
[28] xtable_1.8-4           snow_0.4-3             scales_1.0.0          
[31] htmlTable_1.13.1       tibble_2.1.1           annotate_1.62.0       
[34] mgcv_1.8-28            withr_2.1.2            nnet_7.3-12           
[37] lazyeval_0.2.2         survival_2.44-1.1      magrittr_1.5          
[40] crayon_1.3.4           memoise_1.1.0          evaluate_0.13         
[43] nlme_3.1-139           foreign_0.8-71         tools_3.6.0           
[46] data.table_1.12.2      stringr_1.4.0          locfit_1.5-9.1        
[49] munsell_0.5.0          cluster_2.0.9          AnnotationDbi_1.46.0  
[52] compiler_3.6.0         rlang_0.3.4            grid_3.6.0            
[55] RCurl_1.95-4.12        rstudioapi_0.10        htmlwidgets_1.3       
[58] labeling_0.3           bitops_1.0-6           base64enc_0.1-3       
[61] rmarkdown_1.12         gtable_0.3.0           DBI_1.0.0             
[64] R6_2.4.0               gridExtra_2.3          knitr_1.22            
[67] dplyr_0.8.0.1          bit_1.1-14             Hmisc_4.2-0           
[70] stringi_1.4.3          Rcpp_1.0.1             geneplotter_1.62.0    
[73] rpart_4.1-15           acepack_1.4.1          tidyselect_0.2.5      
[76] xfun_0.6              

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). BioMed Central: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). Oxford University Press: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). Oxford University Press:2672–8.