Last updated: 2021-08-10

Checks: 5 2

Knit directory: EMBL2016/analysis/

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Ignored files:
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Characterizations of NOTCH1 mutations

How many samples are annotated as NOTCH1 mutated?

[1] 24

How many samples have exonic mutations?

[1] 19

Samples with exonic NOTCH1 mutations

Samples with non-exonic NOTCH1 mutations

[1] "P0518" "P0478" "P0474" "P0734" "P0583"

Based on tumorbank, those cases have mutations in 5UTR region. Currently they are also included as NOTCH1 mutated cases for testing

Interactions between IGHV and NOTCH1 mutations


    Fisher's Exact Test for Count Data

data:  tt
p-value = 0.001372
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
  1.71984 18.26667
sample estimates:
odds ratio 
  5.077015

NOTCH1 mutated samples have higher occurrence in U-CLL. In the below analysis, I will only use U-CLL

Gene expression signatures of NOTCH1 mutations in U-CLL

Pre-processing

Sample size


 0  1 
76 18

Differential expression analysis

Table of significant associations (10% FDR)

Pathway enrichment analysis

Cancer hallmmarks

KEGG

Oncogenetic

Proteomic signatures of NOTCH1 mutations in U-CLL

Using the exploration dataset (batch 1 & 3, higher quality)

Load packages and datasets

library(proDA)
library(SummarizedExperiment)

#load datasets
load("../../CLLproteomics_batch13/data/patMeta_enc.RData")
load("../../CLLproteomics_batch13/data/proteomic_explore_enc.RData")

Preprocessing

Sample size


 0  1 
37  7

Differential expression

Table of proteins with raw p-values <0.05 (no results passed 10% FDR)

Pathway enrichment analysis

Cancer hallmmarks

[1] "No sets passed the criteria"

KEGG

Oncogenetic

[1] "No sets passed the criteria"

NULL

Using the independent dataset (batch 2, all are U-CLL samples, lower quality, but somehow has stronger NOTCH1 signatures)

Load packages and datasets

#load datasets
load("../../CLLproteomics_batch13/data/patMeta_enc.RData")
load("../../CLLproteomics_batch13/data/proteomic_independent_enc.RData")

Preprocessing

Sample isle


 0  1 
16  9

Differential expression

Table of proteins with 10% FDR

Genes with both changes on protein and RNA level (10% FDR)

Up-regulated in NOTCH1 mutants

character(0)

Down-regulated in NOTCH1 mutants

character(0)

No overlaps

Pathway enrichment analysis

Cancer hallmmarks

KEGG

Oncogenetic

[1] "No sets passed the criteria"

NULL

R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS  10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

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

other attached packages:
 [1] proDA_1.2.0                 forcats_0.5.1              
 [3] stringr_1.4.0               dplyr_1.0.7                
 [5] purrr_0.3.4                 readr_2.0.0                
 [7] tidyr_1.1.3                 tibble_3.1.3               
 [9] ggplot2_3.3.5               tidyverse_1.3.1            
[11] jyluMisc_0.1.5              DESeq2_1.28.1              
[13] SummarizedExperiment_1.18.2 DelayedArray_0.14.1        
[15] matrixStats_0.60.0          Biobase_2.48.0             
[17] GenomicRanges_1.40.0        GenomeInfoDb_1.24.2        
[19] IRanges_2.22.2              S4Vectors_0.26.1           
[21] BiocGenerics_0.34.0        

loaded via a namespace (and not attached):
  [1] readxl_1.3.1           backports_1.2.1        fastmatch_1.1-3       
  [4] drc_3.0-1              workflowr_1.6.2        igraph_1.2.6          
  [7] shinydashboard_0.7.1   splines_4.0.2          crosstalk_1.1.1       
 [10] BiocParallel_1.22.0    TH.data_1.0-10         digest_0.6.27         
 [13] htmltools_0.5.1.1      fansi_0.5.0            magrittr_2.0.1        
 [16] memoise_2.0.0          cluster_2.1.2          tzdb_0.1.2            
 [19] openxlsx_4.2.4         limma_3.44.3           annotate_1.66.0       
 [22] modelr_0.1.8           vroom_1.5.3            sandwich_3.0-1        
 [25] piano_2.4.0            colorspace_2.0-2       rvest_1.0.1           
 [28] blob_1.2.2             haven_2.4.1            xfun_0.24             
 [31] crayon_1.4.1           RCurl_1.98-1.3         jsonlite_1.7.2        
 [34] genefilter_1.70.0      survival_3.2-11        zoo_1.8-9             
 [37] glue_1.4.2             survminer_0.4.9        gtable_0.3.0          
 [40] zlibbioc_1.34.0        XVector_0.28.0         car_3.0-11            
 [43] abind_1.4-5            scales_1.1.1           mvtnorm_1.1-2         
 [46] DBI_1.1.1              relations_0.6-9        rstatix_0.7.0         
 [49] Rcpp_1.0.7             plotrix_3.8-1          xtable_1.8-4          
 [52] foreign_0.8-81         bit_4.0.4              km.ci_0.5-2           
 [55] DT_0.18                httr_1.4.2             htmlwidgets_1.5.3     
 [58] fgsea_1.14.0           gplots_3.1.1           RColorBrewer_1.1-2    
 [61] ellipsis_0.3.2         farver_2.1.0           pkgconfig_2.0.3       
 [64] XML_3.99-0.6           dbplyr_2.1.1           sass_0.4.0            
 [67] locfit_1.5-9.4         utf8_1.2.2             labeling_0.4.2        
 [70] tidyselect_1.1.1       rlang_0.4.11           later_1.2.0           
 [73] AnnotationDbi_1.50.3   munsell_0.5.0          cellranger_1.1.0      
 [76] tools_4.0.2            visNetwork_2.0.9       cachem_1.0.5          
 [79] cli_3.0.1              generics_0.1.0         RSQLite_2.2.7         
 [82] broom_0.7.9            evaluate_0.14          fastmap_1.1.0         
 [85] yaml_2.2.1             knitr_1.33             bit64_4.0.5           
 [88] fs_1.5.0               zip_2.2.0              survMisc_0.5.5        
 [91] caTools_1.18.2         mime_0.11              slam_0.1-48           
 [94] xml2_1.3.2             rstudioapi_0.13        BiocStyle_2.16.1      
 [97] compiler_4.0.2         curl_4.3.2             ggsignif_0.6.2        
[100] marray_1.66.0          reprex_2.0.0           geneplotter_1.66.0    
[103] bslib_0.2.5.1          stringi_1.7.3          highr_0.9             
[106] lattice_0.20-44        Matrix_1.3-4           KMsurv_0.1-5          
[109] shinyjs_2.0.0          vctrs_0.3.8            pillar_1.6.2          
[112] lifecycle_1.0.0        BiocManager_1.30.16    jquerylib_0.1.4       
[115] data.table_1.14.0      cowplot_1.1.1          bitops_1.0-7          
[118] httpuv_1.6.1           R6_2.5.0               promises_1.2.0.1      
[121] KernSmooth_2.23-20     gridExtra_2.3          rio_0.5.27            
[124] codetools_0.2-18       MASS_7.3-54            gtools_3.9.2          
[127] exactRankTests_0.8-32  assertthat_0.2.1       rprojroot_2.0.2       
[130] withr_2.4.2            multcomp_1.4-17        GenomeInfoDbData_1.2.3
[133] hms_1.1.0              grid_4.0.2             rmarkdown_2.9         
[136] carData_3.0-4          git2r_0.28.0           maxstat_0.7-25        
[139] ggpubr_0.4.0           sets_1.0-18            lubridate_1.7.10      
[142] shiny_1.6.0

Explore NOTCH1 mutational signature in CLL

Junyan Lu

10 August 2021

Characterizations of NOTCH1 mutations

Interactions between IGHV and NOTCH1 mutations

Gene expression signatures of NOTCH1 mutations in U-CLL

Pre-processing

Differential expression analysis

Pathway enrichment analysis

Cancer hallmmarks

KEGG

Oncogenetic

Proteomic signatures of NOTCH1 mutations in U-CLL

Using the exploration dataset (batch 1 & 3, higher quality)

Load packages and datasets

Preprocessing

Differential expression

Pathway enrichment analysis

Cancer hallmmarks

KEGG

Oncogenetic

Using the independent dataset (batch 2, all are U-CLL samples, lower quality, but somehow has stronger NOTCH1 signatures)

Load packages and datasets

Preprocessing

Differential expression

Genes with both changes on protein and RNA level (10% FDR)

Pathway enrichment analysis

Cancer hallmmarks

KEGG

Oncogenetic