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
## 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.