App
Manual
Example Data
Sample Annotation

R Session Information


                    
R Package References

                    






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15. Revolution Analytics, Weston S. Foreach: Provides foreach looping
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16. Zeileis A, Croissant Y. Formula: Extended
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31. Sarkar D. Lattice:
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45. Stacklies W, Redestig H, Wright K. pcaMethods: A collection
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46. Peterson BG, Carl P. PerformanceAnalytics:
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47. Kolde R. Pheatmap: Pretty
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48. Sievert C, Parmer C, Hocking T, Chamberlain S, Ram K, Corvellec M,
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49. Sachs MC. plotROC:
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50. Gatto L, Rainer J. ProtGenerics:
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51. Revelle W. Psych: Procedures
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52. Henry L, Wickham H. Purrr: Functional
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53. Neuwirth E. RColorBrewer:
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54. Eddelbuettel D, Francois R, Allaire J, Ushey K, Kou Q, Russell N, et
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55. Wickham H, Hester J, Bryan J. Readr: Read rectangular
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56. Benoit K, Obeng A. Readtext: Import and
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57. Wickham H. Reshape: Flexibly
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58. Sing T, Sander O, Beerenwinkel N, Lengauer T. ROCR: Visualizing the performance
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59. Kassambara A. Rstatix: Pipe-friendly
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60. Pagès H, Lawrence M, Aboyoun P. S4Vectors: Foundation
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61. Zeileis A, Lumley T. Sandwich: Robust
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62. Chang W, Cheng J, Allaire J, Sievert C, Schloerke B, Xie Y, et al.
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63. Meyer F, Perrier V. Shinybusy: Busy indicators
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64. Sali A, Attali D. Shinycssloaders: Add
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65. Chang W, Borges Ribeiro B. Shinydashboard: Create
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66. Attali D. Shinydisconnect: Show
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67. Attali D. Shinyjs: Easily
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68. Wickham H. Stringr: Simple,
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69. Therneau TM. Survival: Survival
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70. Müller K, Wickham H. Tibble: Simple data
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71. Wickham H, Girlich M. Tidyr: Tidy messy
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72. Wickham H. Tidyverse: Easily
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74. Urbanek S, Ts’o T. Uuid: Tools
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75. Garnier S. Viridis:
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76. Garnier S. viridisLite:
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77. Ooms J. Writexl: Export data
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78. Dragulescu A, Arendt C. Xlsx: Read, write, format
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79. Ryan JA, Ulrich JM. Xts: eXtensible time
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83. Gu Z, Gu L, Eils R, Schlesner M, Brors B. Circlize implements and
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84. Gu Z, Eils R, Schlesner M. Complex heatmaps reveal patterns and
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85. Wei T, Simko V. R
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86. Zeileis A, Croissant Y. Extended model formulas in
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87. Wickham H. ggplot2: Elegant
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88. Friedman J, Hastie T, Tibshirani R. Regularization paths for
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                        Penetrance fold change determines the number of times (frequence) a feature has higher intensity in the study group than in the reference group.
                        

                        

                          

                          

                            

                          

                          

                            

                          

                          

                          

                            

                          

                        

                        

                        

                          

                            

                              

                                

                              

                            

                          

                        

                        

                          
                        

                      

                    

                  

                

              

            

            

              

              Data tables from each stage of the data processing pipeline, with samples as columns and antigens as rows. Each table can be downloaded as a tab-delimited text file using the 'txt' download button.
              

                
                

                  

                    Sample annotation table updated with the per sample quality control metrics
                    

                  

                  

                    Data table with the mean net intensity values, prior to removing sample by quality control, log transformation and imputation. This is essentially the raw intensity data imported into the app, post background subtraction
                    

                  

                  

                    The CVs calculated during spot averaging
                    

                  

                  

                    The log2 transformed net intensity data after failed samples were removed and missing values were imputed
                    

                  

                  

                    Cyclicloess normalized log2 transformed data
                    

                  

                  

                    The retained antibody signals based on the normalized data. This is the data that should be used downstream to test for biomarkers.