Conc_1 in differnet dba.report result is slightly different
3
0
Entering edit mode
@shangguandong1996-21805
Last seen 2.1 years ago
China

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 • 1.6k views
ADD COMMENT •
1
Entering edit mode
Rory Stark ★ 5.2k
@rory-stark-5741
Last seen 8 weeks ago
Cambridge, UK

These anomalies have been resolved in DiffBind_3.0.12.
ADD COMMENT •
0
Entering edit mode
Rory Stark ★ 5.2k
@rory-stark-5741
Last seen 8 weeks ago
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)
ADD COMMENT •
0
Entering edit mode

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
ADD REPLY •
0
Entering edit mode

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.

ADD REPLY •
0
Entering edit mode

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
ADD REPLY •
0
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
ADD REPLY •
0
Entering edit mode
@shangguandong1996-21805
Last seen 2.1 years ago
China

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
ADD COMMENT •

Login before adding your answer.

Similar Posts
Loading Similar Posts
Traffic: 555 users visited in the last hour
Content Search
Users
Tags
Badges
Help About
FAQ
Access RSS
API
Stats

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.

Powered by the version 2.3.6