Conc_1 in differnet dba.report result is slightly different
0
Entering edit mode
@shangguandong1996-21805
Last seen 26 days ago

Hi, Dr Stark

I found when I pool three group in a experiment, like groupA, B, C. The Conc_A in dba.report is slightly different between GroupB_VS_GroupA and GroupC_VS_Group_A.

Just my own data

library(tidyverse)
a <- read.csv("result/Diff_Peak_Anno/H3K27ac/SIM3d_H3K27ac_VS_CIM7d_H3K27ac.csv") %>% 
  select(feature_id, Conc_CIM7d_H3K27ac)
b <- read.csv("result/Diff_Peak_Anno/H3K27ac/SIM8d_H3K27ac_VS_CIM7d_H3K27ac.csv") %>% 
  select(feature_id, Conc_CIM7d_H3K27ac)

# sometimes, the diff will be bigger
> inner_join(a, b, by = "feature_id") %>% 
+   mutate(diff = Conc_CIM7d_H3K27ac.x - Conc_CIM7d_H3K27ac.y) %>% 
+   pull(diff) %>% 
+   table()
.
                  0 0.00999999999999979                0.01 
               3971               13053                  76 
 0.0100000000000002  0.0100000000000007  0.0100000000000016 
                 75                1083                1297

by the way, I find it is hard to open the link posted in the DiffBind manual.

http://https//www.cruk.cam.ac.uk/core-facilities/bioinformatics-core/software/diffbind

Best wishes

Guandong Shang

DiffBind • 260 views
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1
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Rory Stark ♦ 3.5k
@rory-stark-5741
Last seen 1 day ago
CRUK, Cambridge, UK

These anomalies have been resolved in DiffBind_3.0.12.

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Rory Stark ♦ 3.5k
@rory-stark-5741
Last seen 1 day ago
CRUK, Cambridge, UK

First, thanks for the pointing out the bad link, I've checked in a fix.

On your main issue, I'm not sure how you derived your .csv files. Could you show how you went from a report to the .csv? Did you use dba.report() with file= or some other method?

Here's a check I did to check for this issue:

data(tamoxifen_counts)
tamoxifen <- dba.contrast(tamoxifen,
                          contrast=c("Tissue","BT474","MCF7"))
tamoxifen <- dba.contrast(tamoxifen,
                          contrast=c("Tissue","BT474","T47D"))
tamoxifen <- dba.analyze(tamoxifen)

r1 <- sort(dba.report(tamoxifen, contrast=1, th=1, precision=0))
r2 <- sort(dba.report(tamoxifen, contrast=2, th=1, precision=0))

sum(r1$Conc_BT474 != r2$Conc_BT474)
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I use as_tibble to convert Grange objecet, and then use write_csv. The basic command is like below


# I have to admit this example is not reproducibility
# Once the data link is fixed, I will post the test data result here again.

dba_contrast <- dba.contrast(XX)
dba_diff <- dba.analyze(dba_contrast)
dba_report_all <- dba.report(dba_diff,th = 1)

# annotatePeak is the function of ChIPseeker package
peakAnno <- annotatePeak(dba_report_all,
                           TxDb = TxDb,
                           level = "gene",
                           tssRegion = c(-500, 500)
                           )


peakAnno_geneSymbol <- left_join(as_tibble(peakAnno@anno),
                                   gene_alias,
                                   by = c("geneId" = "name"))

peakAnno_geneSymbol %>% 
    readr::write_csv("XX.csv")

By the way, I found the N in Binding Affinity: treat vs. control (N FDR < 0.050)ยท of MA-plot is different from the N result by using filter in R or excel. I hope this phenomenon may be helpful.

For example, the N in MA-plot text may be 6809 FDR < 0.05 while in my result is 6801.

# my MA-plot function code
# Binding Affinity: YE vs. Col_0 (6809 FDR < 0.050)
dba.plotMA(dba_diff,fold = 2,cex.main=0.8)

# neither >= nor > is same as text in MA-plot
read_csv("result/Diff_Peak_Anno/THB_ChIP/YE_VS_Col_0.csv") %>% 
  filter(abs(Fold) >= 2, FDR < 0.05) %>% 
  nrow()

[1] 6801


read_csv("result/Diff_Peak_Anno/THB_ChIP/YE_VS_Col_0.csv") %>% 
  filter(abs(Fold) > 2, FDR < 0.05) %>% 
  nrow()

[1] 6796
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dba.plotMA() uses (abs(Fold) >= fold) & (FDR <= th).

For the concentration issue, if you could send me a link where I can access your dba_diffobject, I can see if I can reproduce it. I'll also need to see the output from calling sessionInfo() so I know which software versions and platform you are running.

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I am sorry I did not post link and seesionInfo

Here is the link, I help it will be helpful

https://github.com/shangguandong1996/tmpDataLink/blob/main/dba_diff.rda

And the session

> sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS:   /opt/sysoft/R-3.6.1/lib64/R/lib/libRblas.so
LAPACK: /opt/sysoft/R-3.6.1/lib64/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [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    
[7] datasets  methods   base     

other attached packages:
 [1] org.At.tair.db_3.10.0                    
 [2] clusterProfiler_3.14.0                   
 [3] ChIPseeker_1.22.1                        
 [4] TxDb.Athaliana.BioMart.plantsmart28_3.2.2
 [5] GenomicFeatures_1.38.0                   
 [6] AnnotationDbi_1.48.0                     
 [7] dplyr_1.0.2                              
 [8] DiffBind_2.14.0                          
 [9] SummarizedExperiment_1.16.0              
[10] DelayedArray_0.12.0                      
[11] BiocParallel_1.19.6                      
[12] matrixStats_0.55.0                       
[13] Biobase_2.46.0                           
[14] GenomicRanges_1.38.0                     
[15] GenomeInfoDb_1.22.0                      
[16] IRanges_2.20.0                           
[17] S4Vectors_0.24.0                         
[18] BiocGenerics_0.32.0                      

loaded via a namespace (and not attached):
  [1] tidyselect_1.1.0                       
  [2] htmlwidgets_1.5.1                      
  [3] RSQLite_2.1.5                          
  [4] grid_3.6.1                             
  [5] munsell_0.5.0                          
  [6] base64url_1.4                          
  [7] systemPipeR_1.20.0                     
  [8] withr_2.1.2                            
  [9] colorspace_1.4-1                       
 [10] GOSemSim_2.12.0                        
 [11] Category_2.52.0                        
 [12] knitr_1.26                             
 [13] rstudioapi_0.10                        
 [14] DOSE_3.12.0                            
 [15] urltools_1.7.3                         
 [16] GenomeInfoDbData_1.2.2                 
 [17] hwriter_1.3.2                          
 [18] polyclip_1.10-0                        
 [19] bit64_0.9-7                            
 [20] farver_2.0.1                           
 [21] pheatmap_1.0.12                        
 [22] batchtools_0.9.11                      
 [23] vctrs_0.3.2                            
 [24] generics_0.0.2                         
 [25] xfun_0.19                              
 [26] BiocFileCache_1.10.0                   
 [27] R6_2.4.1                               
 [28] graphlayouts_0.5.0                     
 [29] locfit_1.5-9.1                         
 [30] bitops_1.0-6                           
 [31] fgsea_1.12.0                           
 [32] gridGraphics_0.4-1                     
 [33] assertthat_0.2.1                       
 [34] scales_1.1.0                           
 [35] ggraph_2.0.2.9000                      
 [36] nnet_7.3-12                            
 [37] enrichplot_1.6.0                       
 [38] gtable_0.3.0                           
 [39] tidygraph_1.1.2                        
 [40] rlang_0.4.7                            
 [41] genefilter_1.68.0                      
 [42] splines_3.6.1                          
 [43] rtracklayer_1.46.0                     
 [44] acepack_1.4.1                          
 [45] europepmc_0.3                          
 [46] brew_1.0-6                             
 [47] checkmate_1.9.4                        
 [48] BiocManager_1.30.9                     
 [49] yaml_2.2.0                             
 [50] reshape2_1.4.3                         
 [51] backports_1.1.5                        
 [52] qvalue_2.18.0                          
 [53] Hmisc_4.3-0                            
 [54] RBGL_1.62.0                            
 [55] tools_3.6.1                            
 [56] ggplotify_0.0.4                        
 [57] ggplot2_3.3.2                          
 [58] gplots_3.0.1.1                         
 [59] RColorBrewer_1.1-2                     
 [60] ggridges_0.5.1                         
 [61] Rcpp_1.0.3                             
 [62] plyr_1.8.5                             
 [63] base64enc_0.1-3                        
 [64] progress_1.2.2                         
 [65] zlibbioc_1.32.0                        
 [66] purrr_0.3.3                            
 [67] RCurl_1.95-4.12                        
 [68] prettyunits_1.0.2                      
 [69] rpart_4.1-15                           
 [70] openssl_1.4.1                          
 [71] viridis_0.5.1                          
 [72] cowplot_1.0.0                          
 [73] ggrepel_0.8.1                          
 [74] cluster_2.1.0                          
 [75] magrittr_1.5                           
 [76] data.table_1.12.6                      
 [77] DO.db_2.9                              
 [78] triebeard_0.3.0                        
 [79] packrat_0.5.0                          
 [80] amap_0.8-18                            
 [81] hms_0.5.2                              
 [82] xtable_1.8-4                           
 [83] XML_3.98-1.20                          
 [84] jpeg_0.1-8.1                           
 [85] BuenColors_0.5.5                       
 [86] gridExtra_2.3                          
 [87] compiler_3.6.1                         
 [88] biomaRt_2.42.0                         
 [89] tibble_2.1.3                           
 [90] KernSmooth_2.23-16                     
 [91] crayon_1.3.4                           
 [92] htmltools_0.4.0                        
 [93] GOstats_2.52.0                         
 [94] Formula_1.2-3                          
 [95] geneplotter_1.64.0                     
 [96] tidyr_1.0.0                            
 [97] DBI_1.1.0                              
 [98] tweenr_1.0.1                           
 [99] dbplyr_1.4.2                           
[100] MASS_7.3-51.5                          
[101] rappdirs_0.3.1                         
[102] boot_1.3-24                            
[103] ShortRead_1.44.0                       
[104] Matrix_1.2-18                          
[105] readr_1.3.1                            
[106] gdata_2.18.0                           
[107] igraph_1.2.4.2                         
[108] pkgconfig_2.0.3                        
[109] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[110] rvcheck_0.1.6                          
[111] GenomicAlignments_1.22.0               
[112] foreign_0.8-73                         
[113] xml2_1.2.2                             
[114] annotate_1.64.0                        
[115] XVector_0.26.0                         
[116] AnnotationForge_1.28.0                 
[117] stringr_1.4.0                          
[118] VariantAnnotation_1.32.0               
[119] digest_0.6.23                          
[120] graph_1.64.0                           
[121] Biostrings_2.54.0                      
[122] fastmatch_1.1-0                        
[123] htmlTable_1.13.3                       
[124] edgeR_3.28.0                           
[125] GSEABase_1.48.0                        
[126] curl_4.3                               
[127] Rsamtools_2.2.3                        
[128] gtools_3.8.1                           
[129] rjson_0.2.20                           
[130] lifecycle_0.2.0                        
[131] jsonlite_1.6                           
[132] viridisLite_0.3.0                      
[133] askpass_1.1                            
[134] limma_3.42.0                           
[135] BSgenome_1.54.0                        
[136] pillar_1.4.3                           
[137] lattice_0.20-38                        
[138] httr_1.4.1                             
[139] plotrix_3.7-7                          
[140] survival_3.1-8                         
[141] GO.db_3.10.0                           
[142] glue_1.4.1                             
[143] png_0.1-7                              
[144] bit_1.1-14                             
[145] Rgraphviz_2.30.0                       
[146] ggforce_0.3.1                          
[147] stringi_1.4.3                          
[148] blob_1.2.0                             
[149] DESeq2_1.26.0                          
[150] latticeExtra_0.6-29                    
[151] caTools_1.17.1.3                       
[152] memoise_1.1.0
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Entering edit mode

Hi, Dr Stark

I just find you fixed the link, and I want to use the test data to reproduce my issue

library(DiffBind)
library(BiocParallel)

library(dplyr)

tamoxifen <- dba(sampleSheet="tamoxifen.csv")
tamoxifen <- dba.count(tamoxifen, summits=250)

compare_function <- function(group1, group2){
  dba_contrast <- dba.contrast(tamoxifen, 
                               group1 = tamoxifen$masks[[group1]],
                               group2 = tamoxifen$masks[[group2]],
                               name1 = group1,
                               name2 = group2)

  dba_analyze <- dba.analyze(dba_contrast)

  dba_report_all <- dba.report(dba_analyze, 
                               th = 1, 
                               precision = 0)
  dba_report_all$feature_id <- paste0("peak", names(dba_report_all))

  return(dba_report_all)
}
compare_1 <- compare_function(group1 = "BT474", 
                              group2 = "MCF7")
compare_2 <- compare_function(group1 = "BT474", 
                              group2 = "T47D")

> subset(compare_1, feature_id == "peak1006")$Conc_BT474
[1] 3.397591
> subset(compare_2, feature_id == "peak1006")$Conc_BT474
[1] 3.604595
compare_1 %>% 
  as_tibble() %>% 
  select(feature_id, Conc_BT474) %>% 
  rename(compare_1 = Conc_BT474) -> data_1

compare_2 %>% 
  as_tibble() %>% 
  select(feature_id, Conc_BT474) %>% 
  rename(compare_2 = Conc_BT474) -> data_2
> inner_join(data_1, data_2) %>% 
+   mutate(compare = compare_2 - compare_1) %>% 
+   pull(compare) %>% 
+   summary()
Joining, by = "feature_id"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  0.207   0.207   0.207   0.207   0.207   0.207
> sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS:   /opt/sysoft/R-3.6.1/lib64/R/lib/libRblas.so
LAPACK: /opt/sysoft/R-3.6.1/lib64/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [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    
[7] datasets  methods   base     

other attached packages:
 [1] dplyr_1.0.2                 DiffBind_2.14.0            
 [3] SummarizedExperiment_1.16.0 DelayedArray_0.12.0        
 [5] BiocParallel_1.19.6         matrixStats_0.55.0         
 [7] Biobase_2.46.0              GenomicRanges_1.38.0       
 [9] GenomeInfoDb_1.22.0         IRanges_2.20.0             
[11] S4Vectors_0.24.0            BiocGenerics_0.32.0        

loaded via a namespace (and not attached):
  [1] amap_0.8-18              colorspace_1.4-1        
  [3] rjson_0.2.20             hwriter_1.3.2           
  [5] ellipsis_0.3.0           htmlTable_1.13.3        
  [7] XVector_0.26.0           base64enc_0.1-3         
  [9] rstudioapi_0.10          ggrepel_0.8.1           
 [11] bit64_0.9-7              fansi_0.4.0             
 [13] AnnotationDbi_1.48.0     splines_3.6.1           
 [15] geneplotter_1.64.0       knitr_1.26              
 [17] Formula_1.2-3            Rsamtools_2.2.3         
 [19] packrat_0.5.0            annotate_1.64.0         
 [21] cluster_2.1.0            GO.db_3.10.0            
 [23] dbplyr_1.4.2             png_0.1-7               
 [25] pheatmap_1.0.12          graph_1.64.0            
 [27] compiler_3.6.1           httr_1.4.1              
 [29] GOstats_2.52.0           backports_1.1.5         
 [31] assertthat_0.2.1         Matrix_1.2-18           
 [33] cli_2.0.0                limma_3.42.0            
 [35] htmltools_0.4.0          acepack_1.4.1           
 [37] prettyunits_1.0.2        tools_3.6.1             
 [39] gtable_0.3.0             glue_1.4.1              
 [41] GenomeInfoDbData_1.2.2   Category_2.52.0         
 [43] systemPipeR_1.20.0       batchtools_0.9.11       
 [45] rappdirs_0.3.1           ShortRead_1.44.0        
 [47] Rcpp_1.0.3               vctrs_0.3.2             
 [49] Biostrings_2.54.0        gdata_2.18.0            
 [51] rtracklayer_1.46.0       xfun_0.19               
 [53] stringr_1.4.0            lifecycle_0.2.0         
 [55] gtools_3.8.1             XML_3.98-1.20           
 [57] edgeR_3.28.0             zlibbioc_1.32.0         
 [59] scales_1.1.0             BSgenome_1.54.0         
 [61] VariantAnnotation_1.32.0 hms_0.5.2               
 [63] RBGL_1.62.0              RColorBrewer_1.1-2      
 [65] yaml_2.2.0               curl_4.3                
 [67] gridExtra_2.3            memoise_1.1.0           
 [69] ggplot2_3.3.2            rpart_4.1-15            
 [71] biomaRt_2.42.0           latticeExtra_0.6-29     
 [73] stringi_1.4.3            RSQLite_2.1.5           
 [75] genefilter_1.68.0        checkmate_1.9.4         
 [77] GenomicFeatures_1.38.0   caTools_1.17.1.3        
 [79] rlang_0.4.7              pkgconfig_2.0.3         
 [81] bitops_1.0-6             lattice_0.20-38         
 [83] purrr_0.3.3              htmlwidgets_1.5.1       
 [85] GenomicAlignments_1.22.0 bit_1.1-14              
 [87] tidyselect_1.1.0         GSEABase_1.48.0         
 [89] AnnotationForge_1.28.0   magrittr_1.5            
 [91] DESeq2_1.26.0            R6_2.4.1                
 [93] gplots_3.0.1.1           generics_0.0.2          
 [95] Hmisc_4.3-0              base64url_1.4           
 [97] DBI_1.1.0                foreign_0.8-73          
 [99] pillar_1.4.3             withr_2.1.2             
[101] nnet_7.3-12              survival_3.1-8          
[103] RCurl_1.95-4.12          tibble_2.1.3            
[105] crayon_1.3.4             utf8_1.1.4              
[107] KernSmooth_2.23-16       BiocFileCache_1.10.0    
[109] jpeg_0.1-8.1             progress_1.2.2          
[111] locfit_1.5-9.1           grid_3.6.1              
[113] data.table_1.12.6        blob_1.2.0              
[115] Rgraphviz_2.30.0         digest_0.6.23           
[117] xtable_1.8-4             brew_1.0-6              
[119] openssl_1.4.1            munsell_0.5.0           
[121] askpass_1.1
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Entering edit mode
@shangguandong1996-21805
Last seen 26 days ago

For a newer DiffBind version

library(DiffBind)
library(BiocParallel)

library(dplyr)

tamoxifen <- dba(sampleSheet="tamoxifen.csv")
tamoxifen <- dba.count(tamoxifen, summits=250)

compare_function <- function(group1, group2){
  dba_contrast <- dba.contrast(tamoxifen, 
                               group1 = tamoxifen$masks[[group1]],
                               group2 = tamoxifen$masks[[group2]],
                               name1 = group1,
                               name2 = group2)

  dba_analyze <- dba.analyze(dba_contrast)

  dba_report_all <- dba.report(dba_analyze, 
                               th = 1, 
                               precision = 0)
  dba_report_all$feature_id <- paste0("peak", names(dba_report_all))

  return(dba_report_all)
}

compare_1 <- compare_function(group1 = "BT474", 
                              group2 = "MCF7")
compare_2 <- compare_function(group1 = "BT474", 
                              group2 = "T47D")
> subset(compare_1, feature_id == "peak1006")$Conc_BT474
[1] 1.983722
> subset(compare_2, feature_id == "peak1006")$Conc_BT474
[1] 2.190726
> inner_join(data_1, data_2) %>% 
+   mutate(compare = compare_2 - compare_1) %>% 
+   pull(compare) %>% 
+   summary()
Joining, by = "feature_id"
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.2070  0.2070  0.2063  0.2070  0.2070
> sessionInfo()
R version 4.0.2 (2020-06-22)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19041)

Matrix products: default

locale:
[1] LC_COLLATE=Chinese (Simplified)_China.936  LC_CTYPE=Chinese (Simplified)_China.936   
[3] LC_MONETARY=Chinese (Simplified)_China.936 LC_NUMERIC=C                              
[5] LC_TIME=Chinese (Simplified)_China.936    

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

other attached packages:
 [1] dplyr_1.0.2                 BiocParallel_1.24.1         DiffBind_3.0.10            
 [4] SummarizedExperiment_1.20.0 Biobase_2.50.0              MatrixGenerics_1.2.0       
 [7] matrixStats_0.57.0          GenomicRanges_1.42.0        GenomeInfoDb_1.26.2        
[10] IRanges_2.24.1              S4Vectors_0.28.1            BiocGenerics_0.36.0        

loaded via a namespace (and not attached):
  [1] backports_1.2.0          GOstats_2.56.0           BiocFileCache_1.14.0    
  [4] plyr_1.8.6               GSEABase_1.52.1          splines_4.0.2           
  [7] ggplot2_3.3.3            amap_0.8-18              digest_0.6.27           
 [10] invgamma_1.1             GO.db_3.12.1             fansi_0.4.1             
 [13] SQUAREM_2021.1           magrittr_2.0.1           checkmate_2.0.0         
 [16] memoise_1.1.0            BSgenome_1.58.0          base64url_1.4           
 [19] limma_3.46.0             Biostrings_2.58.0        annotate_1.68.0         
 [22] systemPipeR_1.24.2       askpass_1.1              bdsmatrix_1.3-4         
 [25] prettyunits_1.1.1        jpeg_0.1-8.1             colorspace_2.0-0        
 [28] blob_1.2.1               rappdirs_0.3.1           apeglm_1.12.0           
  [31] ggrepel_0.9.0            crayon_1.3.4             RCurl_1.98-1.2          
 [34] jsonlite_1.7.2           graph_1.68.0             genefilter_1.72.0       
 [37] brew_1.0-6               survival_3.2-7           VariantAnnotation_1.36.0
 [40] glue_1.4.2               gtable_0.3.0             zlibbioc_1.36.0         
 [43] XVector_0.30.0           DelayedArray_0.16.0      V8_3.4.0                
 [46] Rgraphviz_2.34.0         scales_1.1.1             pheatmap_1.0.12         
 [49] mvtnorm_1.1-1            DBI_1.1.0                edgeR_3.32.0            
[52] Rcpp_1.0.5               xtable_1.8-4             progress_1.2.2          
 [55] emdbook_1.3.12           bit_4.0.4                rsvg_2.1                
 [58] AnnotationForge_1.32.0   truncnorm_1.0-8          httr_1.4.2              
 [61] gplots_3.1.1             RColorBrewer_1.1-2       ellipsis_0.3.1          
 [64] pkgconfig_2.0.3          XML_3.99-0.5             dbplyr_2.0.0            
 [67] locfit_1.5-9.4           tidyselect_1.1.0         rlang_0.4.10            
 [70] AnnotationDbi_1.52.0     munsell_0.5.0            tools_4.0.2             
 [73] cli_2.2.0                generics_0.1.0           RSQLite_2.2.2           
 [76] stringr_1.4.0            yaml_2.2.1               bit64_4.0.5             
 [79] caTools_1.18.0           purrr_0.3.4              RBGL_1.66.0             
 [82] xml2_1.3.2               biomaRt_2.46.0           compiler_4.0.2          
 [85] rstudioapi_0.13          curl_4.3                 png_0.1-7               
 [88] geneplotter_1.68.0       tibble_3.0.4             stringi_1.5.3           
 [91] GenomicFeatures_1.42.1   lattice_0.20-41          Matrix_1.2-18           
 [94] vctrs_0.3.6              pillar_1.4.7             lifecycle_0.2.0         
 [97] data.table_1.13.6        bitops_1.0-6             irlba_2.3.3             
[100] rtracklayer_1.49.5       R6_2.5.0                 latticeExtra_0.6-29     
[103] hwriter_1.3.2            ShortRead_1.48.0         KernSmooth_2.23-18      
[106] MASS_7.3-53              gtools_3.8.2             assertthat_0.2.1        
[109] DESeq2_1.30.0            openssl_1.4.3            Category_2.56.0         
[112] rjson_0.2.20             withr_2.3.0              GenomicAlignments_1.26.0
[115] batchtools_0.9.15        Rsamtools_2.6.0          GenomeInfoDbData_1.2.4  
[118] hms_0.5.3                grid_4.0.2               DOT_0.1                 
[121] coda_0.19-4              GreyListChIP_1.22.0      ashr_2.2-47             
[124] mixsqp_0.3-43            bbmle_1.0.23.1           numDeriv_2016.8-1.1
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