amica is freely available at https://bioapps.maxperutzlabs.ac.at/app/amica

Check out our wiki and user manual for extensive online documentation.

amica

amica is an interactive and user-friendly web-based platform that accepts proteomic input files from different sources and provides automatically generated quality control, set comparisons, differential expression, biological network and over-representation analysis on the basis of minimal user input.

Functionality

• Faciliting interactive analyses and visualizations with just a couple of clicks

Input

• DDA: MaxQuant's proteinGroups.txt, FragPipe's combined_protein.tsv
• DIA: Spectronaut's PG report, DIA-NN's PG matrix
• TMT: FragPipe's [abundance/ratio]protein[normalization].tsv
• or any custom, tab-separated file.
• Processed data can be downloaded in a developed amica format which can also be used as input
• Experimental design mapping samples to conditions
• Contrast matrix file for group comparisons in case of MaxQuant, FragPipe or custom upload
• Specification file for mapping relevant columns in case of custom file upload

Outputs

• Analyzed data downloadable as amica format
• Almost all plots prduced by plotly (hover over plot and download plot as svg or png with the camera icon)
• All plots have customizable plot parameters (width, height, file format, etc.)
• Downloadable data tables

Analysis options

• Remove decoys and proteins only identified by site (MaxQuant)
• Filter on minimum peptide count and spectral count values
• Filter on minimum valid values per group
• Select intensities to:
• (Re-)normalize intensities (VSN, Quantile, Median centering)
• Imputate missing values from normal distribution or replace them by constant value (useful for pilots)

QC-plots

For different intensities (Raw intensities, LFQ intensities, imputed intensities)

• PCA
• Box plots
• Density plots
• Correlation plots (Pearson correlation)
• Bar plots (identified proteins, % contaminants, most abundant proteins) per sample
• Scatter plots
• Automated QC report

Differential abundance analysis

• Primary filter options (log2FC thresholds, multiple-testing correction, select enriched or reduced proteins)
• Analyze single - or multiple selected group comparisons
• Volcano - and MA - plots
• Set comparisons (UpSet plots and Euler diagrams)
• Customizable output data table (can be further filtered)
• Heatmap
• Dot plot
• Fold change plot
• Profile plot
• Protein-protein interaction (PPI) network
• Over-Representation Analysis (ORA)
• Automated Diff. abundance report

Compare multiple amica files

• Upload a second amica file from another experiment/analysis to combine datasets
• Download combined dataset
• Correlate intensities from combined dataset (scatter - and correlation plots)
• Differential abundance analysis for combined amica dataset

Dependencies

All dependencies can be installed by executing the install_dependencies.R script.

Session info

> sessionInfo()
R version 4.1.1 (2021-08-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 21.04

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0

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

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

other attached packages:
 [1] eulerr_6.1.1       colourpicker_1.1.0 RColorBrewer_1.1-2 dplyr_1.0.7        data.table_1.14.0  Rmisc_1.5         
 [7] plyr_1.8.6         lattice_0.20-45    pheatmap_1.0.12    colourvalues_0.3.7 UpSetR_1.4.0       visNetwork_2.0.9  
[13] igraph_1.2.6       reshape2_1.4.4     bslib_0.2.5.1      gprofiler2_0.2.0   DEqMS_1.10.0       limma_3.48.1      
[19] DT_0.18            heatmaply_1.2.1    viridis_0.6.1      viridisLite_0.4.0  plotly_4.9.4.1     ggfortify_0.4.12  
[25] ggplot2_3.3.5      shinyBS_0.61       shinyjs_2.0.0      shiny_1.6.0       

loaded via a namespace (and not attached):
 [1] httr_1.4.2        sass_0.4.0        tidyr_1.1.3       jsonlite_1.7.2    foreach_1.5.1     assertthat_0.2.1 
 [7] yaml_2.2.1        pillar_1.6.1      glue_1.4.2        digest_0.6.27     promises_1.2.0.1  colorspace_2.0-2 
[13] htmltools_0.5.1.1 httpuv_1.6.1      pkgconfig_2.0.3   purrr_0.3.4       xtable_1.8-4      scales_1.1.1     
[19] webshot_0.5.2     later_1.2.0       tibble_3.1.2      generics_0.1.0    ellipsis_0.3.2    cachem_1.0.5     
[25] withr_2.4.2       lazyeval_0.2.2    magrittr_2.0.1    crayon_1.4.1      mime_0.11         fs_1.5.0         
[31] fansi_0.5.0       registry_0.5-1    lifecycle_1.0.0   stringr_1.4.0     munsell_0.5.0     compiler_4.1.1   
[37] jquerylib_0.1.4   rlang_0.4.11      grid_4.1.1        iterators_1.0.13  htmlwidgets_1.5.3 crosstalk_1.1.1  
[43] miniUI_0.1.1.1    gtable_0.3.0      codetools_0.2-18  DBI_1.1.1         TSP_1.1-10        R6_2.5.0         
[49] seriation_1.3.0   gridExtra_2.3     fastmap_1.1.0     utf8_1.2.1        dendextend_1.15.1 stringi_1.7.3    
[55] Rcpp_1.0.7        vctrs_0.3.8       tidyselect_1.1.1 

Local installation

• Using git and Rstudio

## Clone the repository
git clone https://github.com/tbaccata/amica.git

## Move to the folder
cd amica

## execute install_dependencies.R


## Inside R console or R studio
> library("shiny")

> runApp()

• Using Docker

Have docker installed and running (www.docker.com/get-started)

## Clone the repository
git clone https://github.com/tbaccata/amica.git

## Move to the folder
cd amica

## Build amica, the -t flag is the name of the docker image
docker build -t amica .

## Start amica from terminal

docker run -p 3838:3838 amica

## Open local interface

https://localhost:3838/amica

Deploy amica with ShinyProxy

When deploying a Shiny application with ShinyProxy, the application is simply bundled as an R package and installed into a Docker image. Every time a user runs an application, a container spins up and serves the application.

Detailed documentation is provided here (https://www.shinyproxy.io/documentation/).

A minimum working example based on documentation (https://www.shinyproxy.io/documentation/deployment):

## install docker image for amica (follow the above instructions)
git clone https://github.com/tbaccata/amica.git
cd amica
docker build -t amica .
 
## download latest version and install it (for debian based systems)
wget https://www.shinyproxy.io/downloads/shinyproxy_2.5.0_amd64.deb
sudo dpkg -i shinyproxy_2.5.0_amd64.deb

## enable system process
sudo systemctl enable shinyproxy

## Add amica into specs part of the server /etc/shinyproxy/application.yml:
## In this file you can also specify the port for shinyproxy.

specs:
  - id: amica
    display-name: amica Shiny App
    description: Analysis and visualization tool for quantitative MS
    container-cmd: ["R", "-e", "shiny::runApp('/root/amica')"]
    container-image: amica
    access-groups: [scientists, mathematicians]

Used libraries and ressources

• (Differential expression analysis) limma: Ritchie, Matthew E., et al. "limma powers differential expression analyses for RNA-sequencing and microarray studies." Nucleic acids research 43.7 (2015): e47-e47.
• (Differential expression analysis) DEqMS: Zhu, Yafeng, et al. "DEqMS: a method for accurate variance estimation in differential protein expression analysis." Molecular & Cellular Proteomics 19.6 (2020): 1047-1057.
• (ORA) gprofiler2: Raudvere, Uku, et al. "g: Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update)." Nucleic acids research 47.W1 (2019): W191-W198.
• (PPI Networks) IntAct: Orchard, Sandra, et al. "The MIntAct project—IntAct as a common curation platform for 11 molecular interaction databases." Nucleic acids research 42.D1 (2014): D358-D363.
• (Subcell. localization) Human CellMap: Go, Christopher D., et al. "A proximity-dependent biotinylation map of a human cell." Nature (2021): 1-5.
• (Heatmaply) heatmaply: Galili, Tal, et al. "heatmaply: an R package for creating interactive cluster heatmaps for online publishing." Bioinformatics 34.9 (2018): 1600-1602.