Last updated: 2022-10-01

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Knit directory: funcFinemapping/

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There are no past versions. Publish this analysis with wflow_publish() to start tracking its development.


MMSplice/MTSplice predictions

MTSplice model \[ \Delta logit(\Psi_{e,t}) = logit(\Psi_{e,avg}^{ref} ) + \text{MMSplice}(S_{ref}-S_{alt}) + \text{TSplice}(S_{alt}, \text{tissue}) - logit(\Psi_{e,t}^{ref}) \] MMSplice model \[ \Delta logit(\Psi_{e,t}) = logit(\Psi_{e,avg}^{ref} ) + \text{MMSplice}(S_{ref}-S_{alt}) - logit(\Psi_{e,t}^{ref}) \]

Understand MTSplice outputs.

Three types of outputs

  • Tissue_ref: psi scores for ref allele in one tissue
  • Tissue_delta_psi: difference in psi scores between ref and alt
  • Tissue:logit psi difference between ref and alt

Distribution of the location of predicted SNPs

Warning in sprintf("%s%% predicted SNPs are within 5kb from the nearest exon", :
one argument not used by format '%s%% predicted SNPs are within 5kb from the
nearest exon'

Majority of MTSplice predicted SNPs are within 500 bp from the nearest exon.

MMSplice predictions on ASD de novo mutations

Replicate the results in fig 7a using the reported de novo mutations from ASD patients (Zhou et al.)

[1] "The number of mutations that satisfy the criteria is 3725, very close to 3884 mutations found in the paper."

Compare with Spidex results

data directory: /home/jinggu/cluster/data/features/raw/splicing/spidex

Around 3000 out of 3700 de novo variants with splicing altering effects were found in Spidex predictions. In our analysis, neither MMSplice or Spidex shows the differences in predicted values as comparable as the ones displayed on Fig 7a from the paper.


sessionInfo()
R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.3.13-el7-x86_64/lib/libopenblas_haswellp-r0.3.13.so

locale:
 [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C         LC_TIME=C           
 [4] LC_COLLATE=C         LC_MONETARY=C        LC_MESSAGES=C       
 [7] LC_PAPER=C           LC_NAME=C            LC_ADDRESS=C        
[10] LC_TELEPHONE=C       LC_MEASUREMENT=C     LC_IDENTIFICATION=C 

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

other attached packages:
[1] ggplot2_3.3.6     dplyr_1.0.9       data.table_1.14.2

loaded via a namespace (and not attached):
 [1] tidyselect_1.1.2  xfun_0.30         bslib_0.3.1       reshape2_1.4.4   
 [5] purrr_0.3.4       colorspace_2.0-3  vctrs_0.4.1       generics_0.1.2   
 [9] htmltools_0.5.2   yaml_2.3.5        utf8_1.2.2        rlang_1.0.2      
[13] R.oo_1.24.0       jquerylib_0.1.4   later_1.3.0       pillar_1.7.0     
[17] glue_1.6.2        withr_2.5.0       DBI_1.1.2         R.utils_2.11.0   
[21] plyr_1.8.7        lifecycle_1.0.1   stringr_1.4.0     munsell_0.5.0    
[25] gtable_0.3.0      workflowr_1.7.0   R.methodsS3_1.8.1 evaluate_0.15    
[29] labeling_0.4.2    knitr_1.39        fastmap_1.1.0     httpuv_1.6.5     
[33] fansi_1.0.3       highr_0.9         Rcpp_1.0.8.3      promises_1.2.0.1 
[37] scales_1.2.0      jsonlite_1.8.0    farver_2.1.0      fs_1.5.2         
[41] digest_0.6.29     stringi_1.7.6     rprojroot_2.0.3   grid_4.2.0       
[45] cli_3.3.0         tools_4.2.0       magrittr_2.0.3    sass_0.4.1       
[49] tibble_3.1.7      crayon_1.5.1      pkgconfig_2.0.3   ellipsis_0.3.2   
[53] assertthat_0.2.1  rmarkdown_2.14    rstudioapi_0.13   R6_2.5.1         
[57] git2r_0.30.1      compiler_4.2.0