1 Processed data

This is an interactive table of the covariate data.

2 Normalisation quality control metrics

2.1 Principal component analysis

The principal component analysis plot shown below was generated using the most varying 500 genes across all samples.

The expression values are obtained by the “vst” method, where the normalisation doesn’t take into account the experimental design.

In presence of strong biological signal, the samples should cluster with the biological condition. When samples are clustered according to other effects (for example patient, or technical batch), great care must be used when interpreting the results, as the other effects will considerably reduce the ability to extract meaningful biological information.

## Warning: Removed 5 rows containing missing values (geom_point).

## Warning: Removed 5 rows containing missing values (geom_point).
## pdf 
##   2

Download plot

2.2 Hierarchical clustering

The hierarchical clustering shown below was generated using the most varying 500 genes across all samples. The expression values are obtained by the “vst” method, where the normalisation doesn’t take into account the experimental design. The clustering is using euclidian distance for both the rows (genes) and columns (samples). In both cases, the distance between clusters is defined as the maximum of the distances between elements pairs from each cluster.

The hierarchical clustering can provide clues on which groups of genes could affect the clustering of samples.

Download plot

2.3 Sample similarity

The hierarchical clustering shown below was generated using all the full normalised dataset (17731 genes). The expression values are obtained by the “vst” method, where the normalisation doesn’t take into account the experimental design. The clustering is using euclidian distance for both the rows (genes) and columns (samples). In both cases, the distance between clusters is defined as the maximum of the distances between elements pairs from each cluster.

Download plot

2.4 Normalised expression densities

The expression values are obtained by the “vst” method, where the experimental design has been used for normalisation.

Download plot

2.5 DESeq2 normalisation

Download plot

2.6 Cox outliers

Download plot

3 Volcano plots for all contrasts

4 Contrasts

Contrasts generated by the pipeline.

4.1 B vs A (treatment vs control)

4.1.1 MA plot

A MA plot of the contrast B vs A (treatment vs control).

4.1.2 Results table

An interactive data table of the contrast results for B vs A (treatment vs control). Only results with adjusted p value smaller than 0.1 are included (total 629 results shown).

4.1.3 tmod enrichment analysis for B vs A (treatment vs control)

Table. Summary of the results for contrast B vs A (treatment vs control) shows number of significant gene sets at various significance levels and for AUC > 0.65.

## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character
## Warning in bind_rows_(x, .id): binding character and factor vector, coercing
## into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector, coercing
## into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector, coercing
## into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector, coercing
## into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector, coercing
## into character vector
DB 0.01 0.001 1e-04 1e-06
tmod 1 0 0 0
msigdb_reactome 11 5 4 3
msigdb_hallmark 3 2 2 2
msigdb_kegg 8 4 2 2
msigdb_go_bp 38 19 8 5

Table. Results of the tmod enrichment analysis for contrast B vs A (treatment vs control). Only significantly enriched gene sets are shown (FDR < 0.01, AUC > 0.65). AUC, area under curve; p.value, p-value from enrichment test; FDR, p-value corrected for multiple testing with Benjamini-Hochberg method.

4.1.3.0.1
4.1.3.0.1.1 tmod.pval
4.1.3.0.1.2 msigdb_reactome.pval
4.1.3.0.1.3 msigdb_hallmark.pval
4.1.3.0.1.4 msigdb_kegg.pval
4.1.3.0.1.5 msigdb_go_bp.pval

Fig. Upset plot.

4.1.3.0.2
4.1.3.0.2.1 tmod.pval


Too few results to generate upset plot.

4.1.3.0.2.2 msigdb_reactome.pval

4.1.3.0.2.3 msigdb_hallmark.pval

4.1.3.0.2.4 msigdb_kegg.pval

4.1.3.0.2.5 msigdb_go_bp.pval

4.1.4 cluster profiler results

4.1.4.1 Dot plot

Dot plot for cluster profiler results for contrast B vs A (treatment vs control).

MSigDb
1

2

Error in str_count(res$core_enrichment, “/”) + 1 : non-numeric argument to binary operator

GO
1

2

KEGG
1

4.1.4.2 Enrichment map

Enrichment map for cluster profiler results for contrast B vs A (treatment vs control).

MSigDb
1

2

Error in emapplot.enrichResult(x, showCategory = showCategory, color = color, : no enriched term found…

GO
1

2

KEGG
1

4.1.4.3 UpSet plot

UpSet plot for cluster profiler results for contrast B vs A (treatment vs control).

MSigDb
1

Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘upsetplot’ for signature ‘“gseaResult”’

2

Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘upsetplot’ for signature ‘“gseaResult”’

GO
1

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KEGG
1

5 Functional analysis

5.1 Gene set enrichment analysis with tmod

5.1.1 Overview

Table. Overview of the databases for which gene set enrichment using tmod was performed.

ID Name Description TaxonID N
tmod Co-expression gene sets (tmod) Gene sets derived from clustering expression profiles from human blood collected for various immune conditions. These gene sets are included in the tmod package by default. Check tmod documentation for further information. 9606 606
msigdb_reactome Reactome gene sets (MSigDB) Reactome gene sets the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/). 9606 1499
msigdb_hallmark Hallmark gene sets (MSigDB) Hallmark gene sets the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/). 9606 50
msigdb_kegg KEGG pathways (MSigDB) KEGG pathways from the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/). 9606 186
msigdb_go_bp GO Biological Process (MSigDB) GO Biological Process definitions from the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/). 9606 7350

5.2 tmod enrichment analysis results for database Co-expression gene sets (tmod).

5.2.1 Summary

Database ID: tmod.

Description: Gene sets derived from clustering expression profiles from human blood collected for various immune conditions. These gene sets are included in the tmod package by default. Check tmod documentation for further information..

Tab. Summary of the results. Numbers show the number of enrichments significant at a given threshold for the given contrast and test type.

Contrast 0.05 0.01 0.001 1e-05
B_vs_A_(treatment_vs_control)_ID0.pval 1 1 0 0 
## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

No panel plot produced because there was only 1 module to show.

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

5.2.2 Evidence plots

Figures below show the evidence plots for the top 1 gene sets. Each row corresponds to one gene set. Each column corresponds to one enrichment test (contrast + ordering). Each evidence plot shows the existing evidence for the enrichment of the given gene set in the given contrast. The curve shows the Receiver Operator Characteristic (ROC) curve for a given gene set. The rug below the figure represents the ordered list of genes. Genes belonging to a given gene set are highlighted. Colors indicate whether the genes are positively or negatively regulated (red or blue, respectively), while color brightness indicates whether genes are significantly regulated (at q < 0.05).

5.3 tmod enrichment analysis results for database Reactome gene sets (MSigDB).

5.3.1 Summary

Database ID: msigdb_reactome.

Description: Reactome gene sets the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/)..

Tab. Summary of the results. Numbers show the number of enrichments significant at a given threshold for the given contrast and test type.

Contrast 0.05 0.01 0.001 1e-05
B_vs_A_(treatment_vs_control)_ID0.pval 65 38 16 7 
## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

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5.3.2 Figure

Fig. Panel plot showing results for the database msigdb_reactome.

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

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## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

5.3.3 Evidence plots

Figures below show the evidence plots for the top 5 gene sets. Each row corresponds to one gene set. Each column corresponds to one enrichment test (contrast + ordering). Each evidence plot shows the existing evidence for the enrichment of the given gene set in the given contrast. The curve shows the Receiver Operator Characteristic (ROC) curve for a given gene set. The rug below the figure represents the ordered list of genes. Genes belonging to a given gene set are highlighted. Colors indicate whether the genes are positively or negatively regulated (red or blue, respectively), while color brightness indicates whether genes are significantly regulated (at q < 0.05).

5.4 tmod enrichment analysis results for database Hallmark gene sets (MSigDB).

5.4.1 Summary

Database ID: msigdb_hallmark.

Description: Hallmark gene sets the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/)..

Tab. Summary of the results. Numbers show the number of enrichments significant at a given threshold for the given contrast and test type.

Contrast 0.05 0.01 0.001 1e-05
B_vs_A_(treatment_vs_control)_ID0.pval 25 21 14 9

5.4.2 Figure

Fig. Panel plot showing results for the database msigdb_hallmark.

5.4.3 Evidence plots

Figures below show the evidence plots for the top 5 gene sets. Each row corresponds to one gene set. Each column corresponds to one enrichment test (contrast + ordering). Each evidence plot shows the existing evidence for the enrichment of the given gene set in the given contrast. The curve shows the Receiver Operator Characteristic (ROC) curve for a given gene set. The rug below the figure represents the ordered list of genes. Genes belonging to a given gene set are highlighted. Colors indicate whether the genes are positively or negatively regulated (red or blue, respectively), while color brightness indicates whether genes are significantly regulated (at q < 0.05).

5.5 tmod enrichment analysis results for database KEGG pathways (MSigDB).

5.5.1 Summary

Database ID: msigdb_kegg.

Description: KEGG pathways from the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/)..

Tab. Summary of the results. Numbers show the number of enrichments significant at a given threshold for the given contrast and test type.

Contrast 0.05 0.01 0.001 1e-05
B_vs_A_(treatment_vs_control)_ID0.pval 24 16 6 2

5.5.2 Figure

Fig. Panel plot showing results for the database msigdb_kegg.

5.5.3 Evidence plots

Figures below show the evidence plots for the top 5 gene sets. Each row corresponds to one gene set. Each column corresponds to one enrichment test (contrast + ordering). Each evidence plot shows the existing evidence for the enrichment of the given gene set in the given contrast. The curve shows the Receiver Operator Characteristic (ROC) curve for a given gene set. The rug below the figure represents the ordered list of genes. Genes belonging to a given gene set are highlighted. Colors indicate whether the genes are positively or negatively regulated (red or blue, respectively), while color brightness indicates whether genes are significantly regulated (at q < 0.05).

5.6 tmod enrichment analysis results for database GO Biological Process (MSigDB).

5.6.1 Summary

Database ID: msigdb_go_bp.

Description: GO Biological Process definitions from the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/)..

Tab. Summary of the results. Numbers show the number of enrichments significant at a given threshold for the given contrast and test type.

Contrast 0.05 0.01 0.001 1e-05
B_vs_A_(treatment_vs_control)_ID0.pval 664 337 157 62 
## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

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## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

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## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

5.6.2 Figure

Fig. Panel plot showing results for the database msigdb_go_bp.

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

5.6.3 Evidence plots

Figures below show the evidence plots for the top 5 gene sets. Each row corresponds to one gene set. Each column corresponds to one enrichment test (contrast + ordering). Each evidence plot shows the existing evidence for the enrichment of the given gene set in the given contrast. The curve shows the Receiver Operator Characteristic (ROC) curve for a given gene set. The rug below the figure represents the ordered list of genes. Genes belonging to a given gene set are highlighted. Colors indicate whether the genes are positively or negatively regulated (red or blue, respectively), while color brightness indicates whether genes are significantly regulated (at q < 0.05).

5.7 Cluster profiler summary overview by database

5.7.1 Overview

Table. Overview of the databases for which gene set enrichment using cluster_profiler was performed.

No figure produced because there were enrichment results.

No figure produced because there were enrichment results.

No figure produced because there were enrichment results.

No figure produced because there were enrichment results.

No figure produced because there were enrichment results.

6 Session Info

## R version 3.5.1 (2018-07-02)
## Platform: x86_64-conda_cos6-linux-gnu (64-bit)
## Running under: CentOS Linux 7 (Core)
## 
## Matrix products: default
## BLAS/LAPACK: /fast/work/users/ivanova_c/miniconda3/envs/sea_snap/lib/R/lib/libRlapack.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=de_DE.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] orthomapper_0.0.0.9000      enrichplot_1.2.0           
##  [3] tmod_0.46.2                 pander_0.6.3               
##  [5] forcats_0.5.0               stringr_1.4.0              
##  [7] readr_1.3.1                 tidyr_1.0.2                
##  [9] tidyverse_1.3.0             glue_1.4.1                 
## [11] scales_1.1.0                cowplot_1.0.0              
## [13] RColorBrewer_1.1-2          plotly_4.9.2.1             
## [15] ggplot2_3.3.2               purrr_0.3.4                
## [17] tibble_3.0.1                dplyr_0.8.5                
## [19] magrittr_1.5                DT_0.13                    
## [21] yaml_2.2.0                  DESeq2_1.22.1              
## [23] SummarizedExperiment_1.12.0 DelayedArray_0.8.0         
## [25] BiocParallel_1.16.6         matrixStats_0.55.0         
## [27] Biobase_2.42.0              GenomicRanges_1.34.0       
## [29] GenomeInfoDb_1.18.1         IRanges_2.16.0             
## [31] S4Vectors_0.20.1            BiocGenerics_0.28.0        
## 
## loaded via a namespace (and not attached):
##   [1] tagcloud_0.6           tidyselect_1.0.0       RSQLite_2.1.5         
##   [4] AnnotationDbi_1.44.0   htmlwidgets_1.5.1      grid_3.5.1            
##   [7] munsell_0.5.0          preprocessCore_1.44.0  withr_2.1.2           
##  [10] colorDF_0.1.4          colorspace_1.4-1       GOSemSim_2.8.0        
##  [13] knitr_1.27             rstudioapi_0.11        DOSE_3.8.0            
##  [16] labeling_0.3           urltools_1.7.3         GenomeInfoDbData_1.2.1
##  [19] polyclip_1.10-0        bit64_0.9-7            farver_2.0.3          
##  [22] pheatmap_1.0.12        vctrs_0.3.0            generics_0.0.2        
##  [25] xfun_0.12              R6_2.4.1               graphlayouts_0.5.0    
##  [28] locfit_1.5-9.1         bitops_1.0-6           fgsea_1.8.0           
##  [31] gridGraphics_0.4-1     assertthat_0.2.1       promises_1.1.0        
##  [34] ggraph_2.0.1           nnet_7.3-12            beeswarm_0.2.3        
##  [37] gtable_0.3.0           Cairo_1.5-10           affy_1.60.0           
##  [40] tidygraph_1.1.2        rlang_0.4.8            genefilter_1.64.0     
##  [43] splines_3.5.1          lazyeval_0.2.2         acepack_1.4.1         
##  [46] plotwidgets_0.4        hexbin_1.28.1          broom_0.5.6           
##  [49] europepmc_0.3          checkmate_1.9.4        BiocManager_1.30.10   
##  [52] reshape2_1.4.3         modelr_0.1.7           crosstalk_1.0.0       
##  [55] backports_1.1.5        httpuv_1.5.2           qvalue_2.14.1         
##  [58] Hmisc_4.3-0            tools_3.5.1            ggplotify_0.0.4       
##  [61] affyio_1.52.0          ellipsis_0.3.0         gplots_3.1.0          
##  [64] ggridges_0.5.2         Rcpp_1.0.5             plyr_1.8.5            
##  [67] base64enc_0.1-3        progress_1.2.2         zlibbioc_1.28.0       
##  [70] RCurl_1.98-1.1         prettyunits_1.1.1      rpart_4.1-15          
##  [73] viridis_0.5.1          haven_2.2.0            ggrepel_0.8.1         
##  [76] cluster_2.1.0          fs_1.4.1               data.table_1.11.6     
##  [79] DO.db_2.9              triebeard_0.3.0        reprex_0.3.0          
##  [82] hms_0.5.3              mime_0.8               evaluate_0.14         
##  [85] xtable_1.8-4           XML_3.99-0.3           readxl_1.3.1          
##  [88] gridExtra_2.3          compiler_3.5.1         KernSmooth_2.23-16    
##  [91] crayon_1.3.4           htmltools_0.4.0        later_1.0.0           
##  [94] Formula_1.2-3          geneplotter_1.60.0     lubridate_1.7.8       
##  [97] DBI_1.1.0              tweenr_1.0.1           dbplyr_1.4.3          
## [100] MASS_7.3-51.5          Matrix_1.2-18          cli_2.0.2             
## [103] vsn_3.50.0             igraph_1.2.4.2         pkgconfig_2.0.3       
## [106] rvcheck_0.1.7          foreign_0.8-75         xml2_1.2.2            
## [109] annotate_1.60.1        XVector_0.22.0         rvest_0.3.5           
## [112] digest_0.6.25          rmarkdown_2.1          cellranger_1.1.0      
## [115] fastmatch_1.1-0        htmlTable_1.13.3       shiny_1.4.0.2         
## [118] gtools_3.8.2           lifecycle_0.2.0        nlme_3.1-143          
## [121] jsonlite_1.6.1         viridisLite_0.3.0      limma_3.38.3          
## [124] fansi_0.4.1            pillar_1.4.3           lattice_0.20-38       
## [127] fastmap_1.0.1          httr_1.4.1             survival_3.1-8        
## [130] GO.db_3.7.0            UpSetR_1.4.0           bit_1.1-15.1          
## [133] ggforce_0.3.1          stringi_1.4.5          blob_1.2.0            
## [136] latticeExtra_0.6-28    caTools_1.17.1.4       memoise_1.1.0