README.md
In MASHUOA/HiTMaP: A collection of tools for imaging MS data processing
HiT-MaP
- Package installation
- Installation of docker image
- Installation code for R console
installation
- Codes for Linux OS building
enviornment
- Codes for Mac OS building enviornment
(optional)
- Example data and source code
- Proteomics identification on maldi-imaging
dataset
- Project folder and result
structure
- Identification result visulasation and
interpretation
- Scoring system for protein and
peptide
- Identification summary and cluster
imaging
- Details of parameter setting
- Modification
- Amino acid substitution
- Digestion site and enzyme
- Imaging-MS data preprocessing
- Example workflow command
- Peptide calibrant
- Bovine lens
- Mouse brain
- Cite this project
- Session information
- References
– An R package of High-resolution Informatics Toolbox for Maldi-imaging
Proteomics
Find our published research article on Nature Communications:
https://doi.org/10.1038/s41467-021-23461-w
https://doi.org/10.1038/s41467-021-23461-w
zenodo
Maintainer: George Guo george.guo@auckland.ac.nz
About us:
Mass Spectrometry Hub \| University of
Auckland
Cancer research theme \| Garvan Institute of Medical
Research
MSRC Schey lab \| Vanderbilt
University
Package installation
This is a tutorial for the use of HiTMaP (An R package of
High-resolution Informatics Toolbox for Maldi-imaging Proteomics).
User’s may run HiTMaP using Docker, or through R console, however Docker
is recommended to avoid issues with package dependency.
Installation of docker image
HiTMaP has been encapsulated into a docker image. After a proper
installation and configuration of Docker engine (Docker
documentation),
user’s can download the latest version of HiT-MaP by using the bash code
as below.
docker pull mashuoa/hitmap
Tags of available docker images:
-
mashuoa/hitmap:latest contains the stable build release (built
from the Dockerfile at MASHUOA/hitmap_docker with the effort from
John Reeves j.reeves@garvan.org.au).
-
mashuoa/hitmap:natcomms contains the original version when this
project been accepted (minor changes applied to enhance the
multi-files cluster image rendering).
-
mashuoa/hitmap:gui_latest contains the developing graphical user
interface of HiTMaP. Please map the 3838 port to the container and
access the GUI via http://localhost:3838/. We are happy to hear
your voice regarding the High-RES IMS pre-processing, segmentation
and annotation as well as their corresponding GUI configurations.
-
We are able to supply a singularity template to the users who want
to deploy the HiTMaP on an HPC server. This scripts also are
available at the MASHUOA/hitmap/dockerfiles.
Setting up and running the docker container:
# For windows user's, run the image with a local user\Documents\expdata folder mapped to the docker container:
docker run --name hitmap -v %userprofile%\Documents\expdata:/root/expdata -a stdin -a stdout -i -t mashuoa/hitmap /bin/bash
docker run --name hitmap_r411 -v %userprofile%\Documents\expdata:/root/expdata -a stdin -a stdout -i -t mashuoa/hitmap:base_R411 /bin/bash
# For linux or mac user's, run the image with a local user/expdata folder mapped to the docker container:
docker run --name hitmap -v ~/expdata:/root/expdata -a stdin -a stdout -i -t mashuoa/hitmap /bin/bash
#Run the R console
R
Revoke Docker terminal:
#use ctrl+d to exit the docker container shell
#Restart the container and connect to the shell
docker restart hitmap
docker container exec -it hitmap /bin/bash
Stop/remove docker container (warning: if no local disk is mapped to
“~/expdata”, please backup your existing result files from the container
before you remove it):
docker stop hitmap
docker rm hitmap
If you are using docker GUI, pull the docker image using the codes above
and follow the image as below to setup the container. if you are using
mashuoa/hitmap:shiny_server, please also map local host:3838 to the
container (Ports -> local hosts -> 3838).
Docker GUI setting
Installation code for R console installation
The code below is used for an experienced R user to build a local
R/HiTMaP running environment. Major dependencies to note:
- R base
- java running library (for linux, additional configuration is needed:
R CMD javareconf)
- orca for plotly (https://github.com/plotly/orca/releases/tag/v1.3.1)
- magick++ (for Linux, additional configuration is needed to expand the
pixel limitation)
#install the git package
install.packages("remotes")
install.packages("devtools")
#library(devtools)
library(remotes)
Sys.setenv("R_REMOTES_NO_ERRORS_FROM_WARNINGS" = "true")
remotes::install_github("MASHUOA/HiTMaP",force=T)
remotes::install_github("kuwisdelu/Cardinal",force=T)
3
no
#Update all dependencies
BiocManager::install(ask = F)
yes
library(HiTMaP)
For windows users, Rtools
(https://cran.r-project.org/bin/windows/Rtools/)
is required.
Codes for Linux OS building enviornment
Run the codes as below to enable the required components in Linux
console.
sudo apt-get install tcl-dev tk-dev
sudo apt-get install r-cran-ncdf4
sudo apt-get install libz-dev
sudo apt install libxml2-dev
sudo apt install libssl-dev
sudo apt install libcurl4-openssl-dev
sudo apt-get install libnss-winbind winbind
sudo apt install dirmngr gnupg apt-transport-https ca-certificates software-properties-common
sudo add-apt-repository ppa:webupd8team/y-ppa-manager
sudo apt-get update
sudo apt-get install y-ppa-manager
sudo y-ppa-manager
sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys
sudo add-apt-repository 'deb https://cloud.r-project.org/bin/linux/ubuntu focal-cran40/'
sudo apt-cache policy r-base
sudo apt-get purge r-base
sudo apt-get install r-base-core="4.0.2-1.2004.0"
sudo apt-get install libmagick++-dev
sudo apt-get install libfftw3-dev
sudo apt-get install r-base-dev texlive-full
sudo apt-get install libudunits2-dev
sudo apt-get install libgdal-dev
Codes for Mac OS building enviornment (optional)
The following code is for a local GUI purpose. Hitmap now has been built
on the shiny server system. You can skip this step in the later version.
You may need to update the Xcode. Go to your Mac OS terminal and input:
xcode-select --install
You’ll then receive: xcode-select: note: install requested for command
line developer tools You will be prompted at this point in a window to
update Xcode Command Line tools.
You may also need to install the X11.app and tcl/tk support for Mac
system:
-
X11.app: https://www.xquartz.org/
-
Use the following link to download and install the correct tcltk
package for your OS version.
https://cran.r-project.org/bin/macosx/tools/
Example data and source code
The HitMaP comes with a series of maldi-imaging datasets acquired by
FT-ICR mass spectromety. With the following code, you can download these
raw data set into a local folder.
You can download the example data manually through this link:
“https://github.com/MASHUOA/HiTMaP/releases/download/1.0.1/Data.tar.gz”
Or download the files in a R console:
if(!require(piggyback)) install.packages("piggyback")
library(piggyback)
#made sure that this folder has enough space
wd="~/expdata/"
dir.create(wd)
setwd(wd)
pb_download("HiTMaP-master.zip", repo = "MASHUOA/HiTMaP", dest = ".",show_progress = F)
pb_download("Data.tar.gz", repo = "MASHUOA/HiTMaP", dest = ".")
untar('Data.tar.gz',exdir =".", tar="tar")
#unlink('Data.tar.gz')
list.dirs()
The example data contains three folders for three individual IMS
datasets, which each contain a configuration file, and the fasta
database, respectively: “./Bovinlens_Trypsin_FT”
“./MouseBrain_Trypsin_FT” “./Peptide_calibrants_FT”
An Tiny version of data set is also available by using the code below:
if(!require(piggyback)) install.packages("piggyback")
library(piggyback)
#made sure that this folder has enough space
wd="~/expdata/"
dir.create(wd)
setwd(wd)
pb_download("Data_tiny.tar.gz", repo = "MASHUOA/HiTMaP", dest = ".")
untar('Data_tiny.tar.gz',exdir =".", tar="tar")
#unlink('Data.tar.gz')
list.dirs()
The tiny version dataset was generated from the Bovinlens and MouseBrain
original data:
-
m/z range: 700 - 1400
-
pixel range:
x \<= 20%, y >= 80% (Bovinlens)
x \<= 30%, y \<= 20% (MouseBrain)
Proteomics identification on maldi-imaging dataset
To perform false-discovery rate controlled peptide and protein
annotation, run the following script below in your R session:
#create candidate list
library(HiTMaP)
#set project folder that contains imzML, .ibd and fasta files
#wd=paste0(file.path(path.package(package="HiTMaP")),"/data/")
#set a series of imzML files to be processed
datafile=c("Bovinlens_Trypsin_FT/Bovin_lens.imzML")
wd="~/expdata/"
imaging_identification(
#==============Choose the imzml raw data file(s) to process make sure the fasta file in the same folder
datafile=paste0(wd,datafile),
threshold=0.005,
ppm=5,
FDR_cutoff = 0.05,
#==============specify the digestion enzyme specificity
Digestion_site="trypsin",
#==============specify the range of missed Cleavages
missedCleavages=0:1,
#==============Set the target fasta file
Fastadatabase="uniprot-bovin.fasta",
#==============Set the possible adducts and fixed modifications
adducts=c("M+H"),
Modifications=list(fixed=NULL,fixmod_position=NULL,variable=NULL,varmod_position=NULL),
#==============The decoy mode: could be one of the "adducts", "elements" or "isotope"
Decoy_mode = "isotope",
use_previous_candidates=F,
output_candidatelist=T,
#==============The pre-processing param
preprocess=list(force_preprocess=TRUE,
use_preprocessRDS=TRUE,
smoothSignal=list(method="Disable"),
reduceBaseline=list(method="Disable"),
peakPick=list(method="adaptive"),
peakAlign=list(tolerance=5, units="ppm"),
normalize=list(method=c("Disable","rms","tic","reference")[1],mz=1)),
#==============Set the parameters for image segmentation
spectra_segments_per_file=4,
Segmentation="spatialKMeans",
Smooth_range=1,
Virtual_segmentation=FALSE,
Virtual_segmentation_rankfile=NULL,
#==============Set the Score method for hi-resolution isotopic pattern matching
score_method="SQRTP",
peptide_ID_filter=2,
#==============Summarise the protein and peptide features across the project the result can be found at the summary folder
Protein_feature_summary=TRUE,
Peptide_feature_summary=TRUE,
Region_feature_summary=TRUE,
#==============The parameters for Cluster imaging. Specify the annotations of interest, the program will perform a case-insensitive search on the result file, extract the protein(s) of interest and plot them in the cluster imaging mode
plot_cluster_image_grid=FALSE,
ClusterID_colname="Protein",
componentID_colname="Peptide",
Protein_desc_of_interest=c("Crystallin","Actin"),
Rotate_IMG=NULL,
)
Project folder and result structure
In the above function, you have performed proteomics analysis on the
sample data file. It is a tryptic Bovin lens MALDI-imaging file which is
acquired on an FT-ICR MS. The function will take the selected data
files’ root directory as the project folder. In this example, the
project folder will be:
library(HiTMaP)
wd=paste0("D:\\GITHUB LFS\\HiTMaP-Data\\inst","/data/Bovinlens_Trypsin_FT/")
datafile=c("Bovin_lens")
After the whole identification process, you will get two sub-folders
within the project folder:
list.dirs(wd, recursive=FALSE)
## [1] "D:\\GITHUB LFS\\HiTMaP-Data\\inst/data/Bovinlens_Trypsin_FT//Bovin_lens ID"
## [2] "D:\\GITHUB LFS\\HiTMaP-Data\\inst/data/Bovinlens_Trypsin_FT//Summary folder"
-
The one which has an identical name to an input data file contains
the identification result of that input:
- the protein and peptides list of each segmentation region
- the PMF matching plot of each segmentation
- the image that indicates segmentations’ boundary (applies to
either K-mean segmentation (powered by Cardinal) or manually
defined segmentation)
- folders of each region contains the detailed identification
process, FDR plots and isotopic pattern matching plots
-
“Summary folder” contains:
- the identification summary of protein and peptides across all the
data
- the candidate list of all possible proteins and peptides (if
use_previous_candidates is set as TRUE)
- the Cluster imaging files of the protein of interest
- the database stats result for resolution-based candidates binning
(optional)
Identification result visulasation and interpretation
To plot the MALDI-image peptide and protein images, use the following
functions:
To check the segmentation result over the sample, you need to navigate
to each data file ID folder and find the “spatialKMeans_image_plot.png”
(if you are using the spatial K-means method for segmentation.)
library(magick)
p<-image_read(paste0(wd,datafile," ID/spatialKMeans_image_plot.png"))
print(p)
## # A tibble: 1 x 7
## format width height colorspace matte filesize density
## <chr> <int> <int> <chr> <lgl> <int> <chr>
## 1 PNG 1024 2640 sRGB FALSE 30726 72x72
The pixels in image data now has been categorized into four regions
according to the initial setting of segmentation
(spectra_segments_per_file=5). The rainbow shaped bovine lens
segmentation image (on the left panel) shows a unique statistical
classification based on the mz features of each region (on the right
panel).
The mouse brain example segmentation result (spatialKmeans n=9) shown as
below:
For further investigation of the segmentation process, you may find a
PCA images set in the “Datafile ID” folder. THe PCA images are good
summary of features and potential region of interests within a data
file. The combination of these PCs of interest will guide you to the
insightful tissue structure profile.
The identification will take place on the mean spectra of each
region. To check the peptide mass fingerprint (PMF) matching quality,
you could locate the PMF spectrum matching plot of each individual
region.
library(magick)
p_pmf<-image_read(paste0(wd,datafile," ID/Bovin_lens 3PMF spectrum match.png"))
print(p_pmf)
## # A tibble: 1 x 7
## format width height colorspace matte filesize density
## <chr> <int> <int> <chr> <lgl> <int> <chr>
## 1 PNG 1980 1080 sRGB FALSE 17664 72x72
A list of the peptides and proteins annotated within each region has
also been created for manual exploration of the results.
peptide_pmf_result<-read.csv(paste0(wd,datafile," ID/Peptide_segment_PMF_RESULT_3.csv"))
head(peptide_pmf_result)
## Protein mz Protein_coverage isdecoy Peptide Modification pepmz
## 1 48 1300.664 0.06875544 0 HLEQFATEGLR NA 1299.657
## 2 48 1300.661 0.06875544 0 QYFLDLALSCK NA 1299.653
## 3 48 1324.643 0.06875544 0 GSKCILYCFYK NA 1323.636
## 4 53 1328.747 0.05542725 0 FKNINPFPVPR NA 1327.740
## 5 53 1449.712 0.05542725 0 AVQNFTEYNVHK NA 1448.705
## 6 53 1605.813 0.05542725 0 AVQNFTEYNVHKR NA 1604.806
## formula adduct charge start end pro_end mz_align Score Rank
## 1 C57H90N17O18 M+H 1 580 590 1149 1300.666 2.4633527 4
## 2 C60H94N13O17S1 M+H 1 744 754 1149 1300.666 2.0216690 10
## 3 C62H94N13O15S2 M+H 1 840 850 1149 1324.647 -0.2644896 32
## 4 C64H98N17O14 M+H 1 207 217 433 1328.747 1.0865820 7
## 5 C65H97N18O20 M+H 1 92 103 433 1449.714 0.7060553 10
## 6 C71H109N22O21 M+H 1 92 104 433 1605.806 2.7178547 11
## moleculeNames Region Delta_ppm Intensity peptide_count
## 1 HLEQFATEGLR 3 0.9026772 4672324.6 3
## 2 QYFLDLALSCK 3 1.4117311 4672324.6 3
## 3 GSKCILYCFYK 3 1.5164261 145191.4 3
## 4 FKNINPFPVPR 3 0.9094769 191636.4 3
## 5 AVQNFTEYNVHK 3 2.8830137 1275214.1 3
## 6 AVQNFTEYNVHKR 3 1.6464326 558610.4 3
## desc.x
## 1 sp|Q29449|AT8A1_BOVIN Probable phospholipid-transporting ATPase IA OS=Bos taurus OX=9913 GN=ATP8A1 PE=1 SV=2
## 2 sp|Q29449|AT8A1_BOVIN Probable phospholipid-transporting ATPase IA OS=Bos taurus OX=9913 GN=ATP8A1 PE=1 SV=2
## 3 sp|Q29449|AT8A1_BOVIN Probable phospholipid-transporting ATPase IA OS=Bos taurus OX=9913 GN=ATP8A1 PE=1 SV=2
## 4 sp|Q3SX05|ECSIT_BOVIN Evolutionarily conserved signaling intermediate in Toll pathway, mitochondrial OS=Bos taurus OX=9913 GN=ECSIT PE=2 SV=1
## 5 sp|Q3SX05|ECSIT_BOVIN Evolutionarily conserved signaling intermediate in Toll pathway, mitochondrial OS=Bos taurus OX=9913 GN=ECSIT PE=2 SV=1
## 6 sp|Q3SX05|ECSIT_BOVIN Evolutionarily conserved signaling intermediate in Toll pathway, mitochondrial OS=Bos taurus OX=9913 GN=ECSIT PE=2 SV=1
## desc.y
## 1 sp|Q29449|AT8A1_BOVIN Probable phospholipid-transporting ATPase IA OS=Bos taurus OX=9913 GN=ATP8A1 PE=1 SV=2
## 2 sp|Q29449|AT8A1_BOVIN Probable phospholipid-transporting ATPase IA OS=Bos taurus OX=9913 GN=ATP8A1 PE=1 SV=2
## 3 sp|Q29449|AT8A1_BOVIN Probable phospholipid-transporting ATPase IA OS=Bos taurus OX=9913 GN=ATP8A1 PE=1 SV=2
## 4 sp|Q3SX05|ECSIT_BOVIN Evolutionarily conserved signaling intermediate in Toll pathway, mitochondrial OS=Bos taurus OX=9913 GN=ECSIT PE=2 SV=1
## 5 sp|Q3SX05|ECSIT_BOVIN Evolutionarily conserved signaling intermediate in Toll pathway, mitochondrial OS=Bos taurus OX=9913 GN=ECSIT PE=2 SV=1
## 6 sp|Q3SX05|ECSIT_BOVIN Evolutionarily conserved signaling intermediate in Toll pathway, mitochondrial OS=Bos taurus OX=9913 GN=ECSIT PE=2 SV=1
protein_pmf_result<-read.csv(paste0(wd,datafile," ID/Protein_segment_PMF_RESULT_3.csv"))
head(protein_pmf_result)
## Protein Proscore isdecoy Intensity Score peptide_count Protein_coverage
## 1 10134 0.13943597 0 2873903.1 1.9269417 3 0.06715328
## 2 10204 0.13654123 0 380571.3 0.7940642 3 0.18468468
## 3 10370 0.20365140 0 1877250.1 2.0776861 4 0.09364548
## 4 10659 0.11239668 0 327352.4 0.7448240 3 0.16400000
## 5 10888 0.07975644 0 532832.0 1.2420183 3 0.06720978
## 6 11270 0.10687770 0 2944154.2 1.3292158 3 0.07449857
## Intensity_norm
## 1 1.0775539
## 2 0.9310593
## 3 1.0466962
## 4 0.9201443
## 5 0.9554442
## 6 1.0793038
## desc
## 1 tr|G3N2M8|G3N2M8_BOVIN Sterile alpha motif domain containing 15 OS=Bos taurus OX=9913 GN=SAMD15 PE=4 SV=2
## 2 tr|A0A3Q1LYB6|A0A3Q1LYB6_BOVIN Uncharacterized protein OS=Bos taurus OX=9913 PE=4 SV=1
## 3 tr|E1B9U7|E1B9U7_BOVIN Polypeptide N-acetylgalactosaminyltransferase OS=Bos taurus OX=9913 GN=GALNT17 PE=3 SV=3
## 4 tr|A0A3Q1M1B1|A0A3Q1M1B1_BOVIN Phosphatidylinositol transfer protein beta isoform OS=Bos taurus OX=9913 GN=PITPNB PE=4 SV=1
## 5 tr|F1MMD4|F1MMD4_BOVIN Matrix metallopeptidase 11 OS=Bos taurus OX=9913 GN=MMP11 PE=3 SV=2
## 6 tr|F6RR01|F6RR01_BOVIN Ribosome production factor 1 homolog OS=Bos taurus OX=9913 GN=RPF1 PE=4 SV=1
Scoring system for protein and peptide
Score in peptide result table shows the isotopic pattern matching
score of the peptide (Pepscore). In Protein result table, it shows the
protein score (Proscore). The ‘Pepscore’ consist of two parts:
Intensity_Score and Mass_error_Score:
-
Intensity_Score indicates how well a putative isotopic pattern can be
matched to the observed spectrum.The default scoring method is SQRTP.
It combines the ‘square root mean’ differences between observed and
theoretical peaks and observed proportion of the isotopic peaks above
a certain relative intensity threshold.
-
Mass_error_Score indicates the summary of mass error (in ppm) for
every detected isotopic peak. In order to integrate the
Mass_error_Score in to scoring system, the mean ppm error has been
normalized by ppm tolerance, and supplied to the probability normal
distributions (pnorm function for R). The resulting value (quantiles
of the given probability density) is deducted by 0.5 and converted
into an absolute value.
-%5Clog(%5Csqrt%7B%5Cfrac%7B%5Csum_%7Bx%20%3D%201%7D%5E%7Bn%7D%20(Theoritical%5C_intensity_x-Observed%5C_intensity_x)%5E2%7D%7B%5Csum_%7Bx%20%3D%201%7D%5E%7Bn%7D%20(Theoritical%5C_intensity_x)%5E2(Observed%5C_intensity_x)%5E2%7D%7D%24)
-0.5)%7C%24)
Proscore in the protein result table shows the overall estimation of
the protein identification Accuracy.
)%7D%7Bmean(log(Intensity))%7D*Protein%5C_coverage*Normalized%5C_intensity%5C_factor%24)
A Peptide_region_file.csv has also been created to summarise all the
IDs in this data file:
Identification_summary_table<-read.csv(paste0(wd,datafile," ID/Peptide_region_file.csv"))
head(Identification_summary_table)
## Protein mz Protein_coverage isdecoy Peptide Modification
## 1 24 1143.5793 0.06119704 0 GFPGQDGLAGPK NA
## 2 24 1684.8878 0.06119704 0 DGANGIPGPIGPPGPRGR NA
## 3 24 742.3478 0.06119704 0 GDSGPPGR NA
## 4 24 1693.8214 0.06119704 0 LLSTEGSQNITYHCK NA
## 5 24 1881.9276 0.06119704 0 GQPGVMGFPGPKGANGEPGK NA
## 6 48 1216.7008 0.03481288 0 ASTSVQNRLLK NA
## pepmz formula adduct charge start end pro_end mz_align
## 1 1142.5720 C51H79N14O16 M+H 1 516 527 1487 1143.5828
## 2 1683.8805 C72H118N25O22 M+H 1 1175 1192 1487 1684.8830
## 3 741.3406 C29H48N11O12 M+H 1 933 940 1487 742.3504
## 4 1692.8141 C72H117N20O25S1 M+H 1 1380 1394 1487 1693.8197
## 5 1880.9203 C82H129N24O25S1 M+H 1 597 616 1487 1881.9268
## 6 1215.6935 C51H94N17O17 M+H 1 614 624 1149 1216.7047
## Score Rank moleculeNames Region Delta_ppm Intensity peptide_count
## 1 1.4443497 2 GFPGQDGLAGPK 2 1.3471596 250698.3 5
## 2 1.9337304 2 DGANGIPGPIGPPGPRGR 2 1.5937657 2696717.3 5
## 3 1.2698949 1 GDSGPPGR 2 0.1407633 190469.7 5
## 4 1.3660521 3 LLSTEGSQNITYHCK 2 2.2329023 368927.9 5
## 5 0.5868561 17 GQPGVMGFPGPKGANGEPGK 2 3.0817671 974427.3 5
## 6 1.9039495 1 ASTSVQNRLLK 2 1.8837090 2036000.7 1
## desc.x
## 1 sp|P02459|CO2A1_BOVIN Collagen alpha-1(II) chain OS=Bos taurus OX=9913 GN=COL2A1 PE=1 SV=4
## 2 sp|P02459|CO2A1_BOVIN Collagen alpha-1(II) chain OS=Bos taurus OX=9913 GN=COL2A1 PE=1 SV=4
## 3 sp|P02459|CO2A1_BOVIN Collagen alpha-1(II) chain OS=Bos taurus OX=9913 GN=COL2A1 PE=1 SV=4
## 4 sp|P02459|CO2A1_BOVIN Collagen alpha-1(II) chain OS=Bos taurus OX=9913 GN=COL2A1 PE=1 SV=4
## 5 sp|P02459|CO2A1_BOVIN Collagen alpha-1(II) chain OS=Bos taurus OX=9913 GN=COL2A1 PE=1 SV=4
## 6 sp|Q29449|AT8A1_BOVIN Probable phospholipid-transporting ATPase IA OS=Bos taurus OX=9913 GN=ATP8A1 PE=1 SV=2
## desc.y
## 1 sp|P02459|CO2A1_BOVIN Collagen alpha-1(II) chain OS=Bos taurus OX=9913 GN=COL2A1 PE=1 SV=4
## 2 sp|P02459|CO2A1_BOVIN Collagen alpha-1(II) chain OS=Bos taurus OX=9913 GN=COL2A1 PE=1 SV=4
## 3 sp|P02459|CO2A1_BOVIN Collagen alpha-1(II) chain OS=Bos taurus OX=9913 GN=COL2A1 PE=1 SV=4
## 4 sp|P02459|CO2A1_BOVIN Collagen alpha-1(II) chain OS=Bos taurus OX=9913 GN=COL2A1 PE=1 SV=4
## 5 sp|P02459|CO2A1_BOVIN Collagen alpha-1(II) chain OS=Bos taurus OX=9913 GN=COL2A1 PE=1 SV=4
## 6 sp|Q29449|AT8A1_BOVIN Probable phospholipid-transporting ATPase IA OS=Bos taurus OX=9913 GN=ATP8A1 PE=1 SV=2
The details of protein/peptide identification process has been save to
the folder named by the segmentation:
list.dirs(paste0(wd,datafile," ID/"), recursive=FALSE)
## [1] "D:\\GITHUB LFS\\HiTMaP-Data\\inst/data/Bovinlens_Trypsin_FT/Bovin_lens ID//1"
## [2] "D:\\GITHUB LFS\\HiTMaP-Data\\inst/data/Bovinlens_Trypsin_FT/Bovin_lens ID//2"
## [3] "D:\\GITHUB LFS\\HiTMaP-Data\\inst/data/Bovinlens_Trypsin_FT/Bovin_lens ID//3"
## [4] "D:\\GITHUB LFS\\HiTMaP-Data\\inst/data/Bovinlens_Trypsin_FT/Bovin_lens ID//4"
In the identification details folder, you will find a series of FDR file
and plots to demonstrate the FDR model and score cutoff threshold:
dir(paste0(wd,datafile," ID/1/"), recursive=FALSE)
## [1] "FDR.CSV"
## [2] "FDR.png"
## [3] "Matching_Score_vs_mz_target-decoy.png"
## [4] "Peptide_1st_ID.csv"
## [5] "Peptide_1st_ID_score_rank_SQRTP.csv"
## [6] "Peptide_2nd_ID_score_rankSQRTP_Rank_above_3.csv"
## [7] "Peptide_Score_histogram_target-decoy.png"
## [8] "ppm"
## [9] "PROTEIN_FDR.CSV"
## [10] "Protein_FDR.png"
## [11] "Protein_ID_score_rank_SQRTP.csv"
## [12] "PROTEIN_Score_histogram.png"
## [13] "Spectrum.csv"
## [14] "unique_peptide_ranking_vs_mz_feature.png"
In this folder, you will find the FDR plots for protein and peptide
annotation. The software will take the proscore and its FDR model to
trim the final identification results. The
unique_peptide_ranking_vs_mz_feature.png is a plot that could tell you
the number of peptide candidates that have been matched to the mz
features in the first round run. You can also access the peptide
spectrum match (first MS dimension) data via the “/ppm” subfolder.
library(magick)
p_FDR_peptide<-image_read(paste0(wd,datafile," ID/3/FDR.png"))
p_FDR_protein<-image_read(paste0(wd,datafile," ID/3/protein_FDR.png"))
p_FDR_peptide_his<-image_read(paste0(wd,datafile," ID/3/Peptide_Score_histogram_target-decoy.png"))
p_FDR_protein_his<-image_read(paste0(wd,datafile," ID/3/PROTEIN_Score_histogram.png"))
p_combined<-image_append(c(p_FDR_peptide,p_FDR_peptide_his,p_FDR_protein,p_FDR_protein_his))
print(p_combined)
## # A tibble: 1 x 7
## format width height colorspace matte filesize density
## <chr> <int> <int> <chr> <lgl> <int> <chr>
## 1 PNG 1920 480 sRGB FALSE 0 72x72
You will also find a Matching_Score_vs_mz plot for further
investigation on peptide matching quality.
library(magick)
#plot Matching_Score_vs_mz
p_Matching_Score_vs_mz<-image_read(paste0(wd,datafile," ID/3/Matching_Score_vs_mz_target-decoy.png"))
print(p_Matching_Score_vs_mz)
## # A tibble: 1 x 7
## format width height colorspace matte filesize density
## <chr> <int> <int> <chr> <lgl> <int> <chr>
## 1 PNG 480 480 sRGB FALSE 47438 72x72
Identification summary and cluster imaging
In the project summary folder, you will find four files and a
sub-folder:
wd_sum=paste(wd,"/Summary folder", sep="")
dir(wd_sum)
## [1] "candidatelist.csv" "cluster Ion images"
## [3] "Peptide_Summary.csv" "Protein_feature_list_trimmed.csv"
## [5] "protein_index.csv" "Protein_Summary.csv"
“candidatelist.csv” and “protein_index.csv” contains the candidates used
for this analysis. They are output after the candidate processing while
output_candidatelist set as TRUE, and can be used repeatedly while
use_previous_candidates set as TRUE.
We have implemented a functionality to perform additional statistical
analysis around the number of enzymatically generated peptides derived
from a given proteome database. If the user sets the argument
‘Database_stats’ to TRUE in the main workflow, the function will be
called. Briefly, the function will list all of the m/z’s of a unique
formulae from the peptide candidate pool within a given m/z range. The
m/z’s will then be binned using three tolerance window: 1 ppm, 2 ppm and
5 ppm. A plot showing the number of unique formulae vs. m/z bins will be
generated and exported to the summary folder (DB_stats_mz_bin).
Proteome database stats
“Peptide_Summary.csv” and “Protein_Summary.csv” contains the table of
the project identification summary. You could set the
plot_cluster_image_grid as TRUE to enable the cluster imaging
function. Please be noted that you could indicate Rotate_IMG with a
CSV file path that indicates the rotation degree of image files.
Note: 90°, 180° and 270° are recommended for image rotation. You may
find an example CSV file in the
expdata/MouseBrain_Trypsin_FT/file_rotationbk.csv.
library(dplyr)
Protein_desc_of_interest<-c("Crystallin","Actin")
Protein_Summary_tb<-read.csv(paste(wd,"/Summary folder","/Protein_Summary.csv", sep=""),stringsAsFactors = F)
Finally, you are able visualize the annotated proteins and their
associated peptide distributions via the cluster image rendering
function.
vimentin:
β-crystallin:
α-crystallin:
Secernin 1
CX6A1 cytochrome coxidase subunit 6A1
Myelin basic protein
Details of parameter setting
Modification
You can choose one or a list of modifications from the unimod
modification list. Peptide_modification function is used to
load/rebuild the modification database into the global enviornment of R.
It will be called automatically in the identification work flow. you can
use the code_name or record_id to refer the modification (see
example data “peptide calibrants” to find more details). The pipeline
will select the non-hidden modifications.
HiTMaP:::Peptide_modification(retrive_ID=NULL,update_unimod=F)
modification_list<-merge(unimod.df$modifications,unimod.df$specificity,by.x=c("record_id"),by.y=c("mod_key"),all.x=T)
head(modification_list['&'(modification_list$code_name=="Phospho",modification_list$hidden!=1),c("code_name","record_id","composition","mono_mass","position_key","one_letter")])
## code_name record_id composition mono_mass position_key one_letter
## 1615 Phospho 21 H O(3) P 79.966331 2 Y
## 1618 Phospho 21 H O(3) P 79.966331 2 T
## 1620 Phospho 21 H O(3) P 79.966331 2 S
head(modification_list['&'(modification_list$code_name=="Amide",modification_list$hidden!=1),c("code_name","record_id","composition","mono_mass","position_key","one_letter")])
## code_name record_id composition mono_mass position_key one_letter
## 1552 Amide 2 H N O(-1) -0.984016 6 C-term
## 1553 Amide 2 H N O(-1) -0.984016 4 C-term
head(modification_list['&'(stringr::str_detect(modification_list$code_name,"Ca"),modification_list$hidden!=1),c("code_name","record_id","composition","mono_mass","position_key","one_letter")])
## code_name record_id composition mono_mass position_key one_letter
## 1946 Carbamidomethyl 4 H(3) C(2) N O 57.021464 2 C
## 1949 Carbamidomethyl 4 H(3) C(2) N O 57.021464 3 N-term
## 2119 Carbamyl 5 H C N O 43.005814 3 N-term
## 2121 Carbamyl 5 H C N O 43.005814 2 K
## 2271 Carboxymethyl 6 H(2) C(2) O(2) 58.005479 2 C
If a modification occurs on a particular site, you will also need to
specify the position of a modifications.
- Anywhere, side chain of possible amino acids
- Any N-term, any N-term of enzymatic peptide
- Protein N-term, any N-term of protein
unimod.df[["positions"]]
## position record_id
## 1 - 1
## 2 Anywhere 2
## 3 Any N-term 3
## 4 Any C-term 4
## 5 Protein N-term 5
## 6 Protein C-term 6
Amino acid substitution
You can set the Substitute_AA to make the uncommon amino acid
available to the workflow:
Substitute_AA=list(AA=c(“X”),AA_new_formula=c(“C5H5NO2”),Formula_with_water=c(FALSE))
- AA: the single letter amino acid to be replaced
- AA_new_formula: the new formula for the amino acid
- Formula_with_water: Set TRUE to indicate the formula represents the
intact amino acid, FALSE to indicate that the formula already lost
one H2O molecule and can be considered as AA backbone.
Digestion site and enzyme
The Digestion_site allows you to specify a list of pre-defined enzyme
and customized digestion rules in regular expression format. You can
either use the enzyme name, customized cleavage rule or combination of
them to get the enzymatics peptides list.
Cleavage_rules<-Cleavage_rules_fun()
Cleavage_df<-data.frame(Enzyme=names(Cleavage_rules),Cleavage_rules=unname(Cleavage_rules),stringsAsFactors = F)
library(gridExtra)
grid.ftable(Cleavage_df, gp = gpar(fontsize=9,fill = rep(c("grey90", "grey95"))))
Imaging-MS data preprocessing
preprocess\$mz_bin_list is an argument for costumized peak-picking
and mz bining purpose. If it is not NULL, the workflow will bypass
signal smooth, noise reduction, and peakpicking steps. User need to give
a numeric vector as the mz input to this argument. The workflow will
first filter the vector with the given ppm tolerance to ensure there’s
no overlapped mz bins (mz +/- ppm tolerance). Then, a m/z binning
procedure will be conducted to the image data to produce a peak-picked
dataset (the peak bin width will be the ppm tolerance).
If user uses a processed IMS data that contains the centroid feature
value (e.g. exported from scils lab with feature list reduced data).
User will still be safe to use this mz_bin_list in order to mount the
centroid data properly. In this case, the ppm tolerance will only
applied to the following annotation procedure.
normalize=list(method=c(“Disable”,“rms”,“tic”,“reference”)[1],mz=1)
the current IMS normalization is done on pixel-to-pixel level, which
will affect the feature distribution in some tissue. We use “Disable” in
the example dataset to minimize the required RAM space and working time.
The step may result in a big RAM usage on some IMS data. If the error
message mentioned a “vector allocation” issue, Please consider to
disable the normalization.
Example workflow command
Below is a list of commands including the parameters for the example
data sets.
Peptide calibrant
#peptide calibrant
library(HiTMaP)
datafile=c("Peptide_calibrants_FT/trypsin_non-decell_w.calibrant_FTICR")
wd="~/expdata/"
# Calibrants dataset analysis with modification
imaging_identification(datafile=paste0(wd,datafile),
Digestion_site="trypsin",
Fastadatabase="uniprot_cali.fasta",
output_candidatelist=T,
plot_matching_score=T,
spectra_segments_per_file=1,
use_previous_candidates=F,
peptide_ID_filter=1,ppm=5,missedCleavages=0:5,
Modifications=list(fixed=NULL,fixmod_position=NULL,variable=c("Amide"),varmod_position=c(6)),
FDR_cutoff=0.1,
Substitute_AA=list(AA=c("X"),AA_new_formula=c("C5H5NO2"),Formula_with_water=c(FALSE)))
# Calibrants dataset analysis with no modification
imaging_identification(datafile=paste0(wd,datafile),
Digestion_site="trypsin",
Fastadatabase="uniprot_cali.fasta",
output_candidatelist=T,
plot_matching_score=T,
spectra_segments_per_file=1,
use_previous_candidates=T,
peptide_ID_filter=1,ppm=5,missedCleavages=0:5,
FDR_cutoff=0.1)
library(HiTMaP)
datafile=c("Peptide_calibrants_FT/trypsin_non-decell_w.calibrant_FTICR")
wd="~/expdata/"
# Calibrants dataset analysis with modification
imaging_identification(datafile=paste0(wd,datafile),
Digestion_site="trypsin",
Fastadatabase="calibrants.fasta",
output_candidatelist=T,
plot_matching_score=T,
spectra_segments_per_file=1,
use_previous_candidates=F,
peptide_ID_filter=1,ppm=5,missedCleavages=0:5,
Modifications=list(fixed=NULL,fixmod_position=NULL,variable=c("Amide"),varmod_position=c(6)),
FDR_cutoff=0.1,
Substitute_AA=list(AA=c("X"),AA_new_formula=c("C5H5NO2"),Formula_with_water=c(FALSE)),Thread = 1)
Bovine lens
library(HiTMaP)
datafile=c("Bovinlens_Trypsin_FT/Bovin_lens.imzML")
wd="~/expdata/"
# Data pre-processing and proteomics annotation
library(HiTMaP)
imaging_identification(datafile=paste0(wd,datafile),Digestion_site="trypsin",
Fastadatabase="uniprot-bovin.fasta",output_candidatelist=T,
preprocess=list(force_preprocess=TRUE,
use_preprocessRDS=TRUE,
smoothSignal=list(method="Disable"),
reduceBaseline=list(method="Disable"),
peakPick=list(method="adaptive"),
peakAlign=list(tolerance=5, units="ppm"),
normalize=list(method=c("Disable","rms","tic","reference")[1],mz=1)),
spectra_segments_per_file=4,use_previous_candidates=F,ppm=5,FDR_cutoff = 0.05,IMS_analysis=T,
Rotate_IMG="file_rotationbk.csv",plot_cluster_image_grid=F)
datafile=c("Bovinlens_Trypsin_FT/Bovin_lens.imzML")
wd="~/expdata/"
library(HiTMaP)
imaging_identification(datafile=paste0(wd,datafile),Digestion_site="trypsin",
Fastadatabase="uniprot-bovin.fasta",output_candidatelist=T,use_previous_candidates=T,
preprocess=list(force_preprocess=F,
use_preprocessRDS=TRUE,
smoothSignal=list(method="Disable"),
reduceBaseline=list(method="Disable"),
peakPick=list(method="Default"),
peakAlign=list(tolerance=5, units="ppm"),
normalize=list(method=c("Disable","rms","tic","reference")[1],mz=1)),
spectra_segments_per_file=4,ppm=5,FDR_cutoff = 0.05,IMS_analysis=T,
Rotate_IMG="file_rotationbk.csv",plot_cluster_image_grid=F)
# Re-analysis and cluster image rendering
library(HiTMaP)
datafile=c("Bovinlens_Trypsin_FT/Bovin_lens.imzML")
wd="~/expdata/"
imaging_identification(datafile=paste0(wd,datafile),Digestion_site="trypsin",
Fastadatabase="uniprot-bovin.fasta",
use_previous_candidates=T,ppm=5,IMS_analysis=F,
plot_cluster_image_grid=T,
export_Header_table=T,
img_brightness=250,
plot_cluster_image_overwrite=T,
cluster_rds_path = "/Bovin_lens ID/preprocessed_imdata.RDS",pixel_size_um = 150,
Plot_score_abs_cutoff=-0.1,
remove_score_outlier=T,
Protein_desc_of_interest=c("Crystallin","Phakinin","Filensin","Actin","Vimentin","Cortactin","Visinin","Arpin","Tropomyosin","Myosin Light Chain 3","Kinesin Family Member 14","Dynein Regulatory Complex","Ankyrin Repeat Domain 45"))
# Re-analysis and cluster image rendering using color scale
library(HiTMaP)
datafile=c("Bovinlens_Trypsin_FT/Bovin_lens.imzML")
wd="~/expdata/"
imaging_identification(datafile=paste0(wd,datafile),Digestion_site="trypsin",
Fastadatabase="uniprot-bovin.fasta",
use_previous_candidates=T,ppm=5,IMS_analysis=F,
plot_cluster_image_grid=T,
export_Header_table=T,
img_brightness=250,
plot_cluster_image_overwrite=T,
cluster_rds_path = "/Bovin_lens ID/preprocessed_imdata.RDS",pixel_size_um = 150,
Plot_score_abs_cutoff=-0.1,
remove_score_outlier=T,cluster_color_scale="fleximaging",
Protein_desc_of_interest=c("Crystallin","Phakinin","Filensin","Actin","Vimentin","Cortactin","Visinin","Arpin","Tropomyosin","Myosin Light Chain 3","Kinesin Family Member 14","Dynein Regulatory Complex","Ankyrin Repeat Domain 45"))
Mouse brain
library(HiTMaP)
datafile=c("MouseBrain_Trypsin_FT/Mouse_brain.imzML")
wd="~/expdata/"
# Data pre-processing and proteomics annotation
library(HiTMaP)
imaging_identification(datafile=paste0(wd,datafile),Digestion_site="trypsin",
Fastadatabase="uniprot_mouse_20210107.fasta",output_candidatelist=T,
preprocess=list(force_preprocess=T,
use_preprocessRDS=TRUE,
smoothSignal=list(method="Disable"),
reduceBaseline=list(method="Disable"),
peakPick=list(method="adaptive"),
peakAlign=list(tolerance=5, units="ppm"),
normalize=list(method=c("Disable","rms","tic","reference")[1],mz=1)),
spectra_segments_per_file=9,use_previous_candidates=F,ppm=10,FDR_cutoff = 0.05,IMS_analysis=T,
Rotate_IMG="file_rotationbk.csv",
mzrange = c(500,4000),plot_cluster_image_grid=F)
imaging_identification(datafile=paste0(wd,datafile),Digestion_site="trypsin",
Fastadatabase="uniprot_mouse_20210107.fasta",output_candidatelist=T,
preprocess=list(force_preprocess=T,
use_preprocessRDS=TRUE,
smoothSignal=list(method="gaussian"),
reduceBaseline=list(method="locmin"),
peakPick=list(method="adaptive"),
peakAlign=list(tolerance=5, units="ppm"),
normalize=list(method=c("Disable","rms","tic","reference")[1],mz=1)),
spectra_segments_per_file=9,use_previous_candidates=F,ppm=10,FDR_cutoff = 0.05,IMS_analysis=T,
Rotate_IMG="file_rotationbk.csv",
mzrange = c(500,4000),plot_cluster_image_grid=F)
# Re-analysis and cluster image rendering
library(HiTMaP)
datafile=c("MouseBrain_Trypsin_FT/Mouse_brain.imzML")
wd="~/expdata/"
imaging_identification(datafile=paste0(wd,datafile),Digestion_site="trypsin",
Fastadatabase="uniprot_mouse_20210107.fasta",
preprocess=list(force_preprocess=FALSE),
spectra_segments_per_file=9,use_previous_candidates=T,ppm=10,FDR_cutoff = 0.05,IMS_analysis=F,
mzrange = c(500,4000),plot_cluster_image_grid=T,
img_brightness=250, plot_cluster_image_overwrite=T,
cluster_rds_path = "/Mouse_brain ID/preprocessed_imdata.RDS",
pixel_size_um = 50,
Plot_score_abs_cutoff=-0.1,
remove_score_outlier=T,
Protein_desc_of_interest=c("Secernin","GN=MBP","Cytochrome"))
library(HiTMaP)
datafile=c("MouseBrain_Trypsin_FT_200brit_man_seg/Mouse_brain.imzML")
wd="~/expdata/"
imaging_identification(datafile=paste0(wd,datafile),Digestion_site="trypsin",
Fastadatabase="uniprot_mouse_20210107.fasta",
preprocess=list(force_preprocess=FALSE),
spectra_segments_per_file=9,use_previous_candidates=T,ppm=10,FDR_cutoff = 0.05,IMS_analysis=F,
mzrange = c(500,4000),plot_cluster_image_grid=T,
img_brightness=250, plot_cluster_image_overwrite=T,
cluster_rds_path = "/Mouse_brain ID/preprocessed_imdata.RDS",
pixel_size_um = 50,
Plot_score_abs_cutoff=-0.1,
remove_score_outlier=T,
Protein_desc_of_interest=c("GTR9"))
Cite this project
This study has been accepted by Nature Communications:
“Automated annotation and visualisation
of high-resolution spatial proteomic mass spectrometry imaging data
using HIT-MAP” online on the 28th May 2021.
Session information
sessionInfo()
## R version 4.1.3 (2022-03-10)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19044)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_Australia.1252 LC_CTYPE=English_Australia.1252
## [3] LC_MONETARY=English_Australia.1252 LC_NUMERIC=C
## [5] LC_TIME=English_Australia.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] gridExtra_2.3 XML_3.99-0.10 protViz_0.7.3 dplyr_1.0.9 magick_2.7.3
## [6] HiTMaP_1.0.0
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.9 pillar_1.8.0 compiler_4.1.3 highr_0.9
## [5] tools_4.1.3 digest_0.6.29 evaluate_0.15 lifecycle_1.0.1
## [9] tibble_3.1.8 gtable_0.3.0 pkgconfig_2.0.3 png_0.1-7
## [13] rlang_1.0.4 cli_3.3.0 DBI_1.1.3 rstudioapi_0.13
## [17] yaml_2.3.5 xfun_0.31 fastmap_1.1.0 stringr_1.4.0
## [21] knitr_1.39 generics_0.1.3 vctrs_0.4.1 tidyselect_1.1.2
## [25] glue_1.6.2 R6_2.5.1 fansi_1.0.3 rmarkdown_2.14
## [29] purrr_0.3.4 magrittr_2.0.3 codetools_0.2-18 htmltools_0.5.3
## [33] assertthat_0.2.1 utf8_1.2.2 stringi_1.7.8
End of the tutorial, Enjoy~
References
R Packages used in this project:
-
viridisLite[@viridisLite]
-
rcdklibs[@rcdklibs]
-
rJava[@rJava]
-
data.table[@data.table]
-
RColorBrewer[@RColorBrewer]
-
magick[@magick]
-
ggplot2[@ggplot2]
-
dplyr[@dplyr]
-
stringr[@stringr]
-
protViz[@protViz]
-
cleaver[@cleaver]
-
Biostrings[@Biostrings]
-
IRanges[@IRanges]
-
Cardinal[@Cardinal]
-
tcltk[@tcltk]
-
BiocParallel[@BiocParallel]
-
spdep[@spdep1]
-
FTICRMS[@FTICRMS]
-
UniProt.ws[@UniProt.ws]
MASHUOA/HiTMaP documentation built on April 5, 2024, 5:48 p.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
HiT-MaP
- Package installation
- Installation of docker image
- Installation code for R console installation
- Codes for Linux OS building enviornment
- Codes for Mac OS building enviornment (optional)
- Example data and source code
- Proteomics identification on maldi-imaging dataset
- Project folder and result structure
- Identification result visulasation and interpretation
- Scoring system for protein and peptide
- Identification summary and cluster imaging
- Details of parameter setting
- Modification
- Amino acid substitution
- Digestion site and enzyme
- Imaging-MS data preprocessing
- Example workflow command
- Peptide calibrant
- Bovine lens
- Mouse brain
- Cite this project
- Session information
- References
– An R package of High-resolution Informatics Toolbox for Maldi-imaging Proteomics
Find our published research article on Nature Communications:
https://doi.org/10.1038/s41467-021-23461-w
https://doi.org/10.1038/s41467-021-23461-w
zenodo
Maintainer: George Guo george.guo@auckland.ac.nz
About us:
Mass Spectrometry Hub \| University of Auckland
Cancer research theme \| Garvan Institute of Medical Research
MSRC Schey lab \| Vanderbilt University
Package installation
This is a tutorial for the use of HiTMaP (An R package of High-resolution Informatics Toolbox for Maldi-imaging Proteomics). User’s may run HiTMaP using Docker, or through R console, however Docker is recommended to avoid issues with package dependency.
Installation of docker image
HiTMaP has been encapsulated into a docker image. After a proper installation and configuration of Docker engine (Docker documentation), user’s can download the latest version of HiT-MaP by using the bash code as below.
docker pull mashuoa/hitmap
Tags of available docker images:
-
mashuoa/hitmap:latest contains the stable build release (built from the Dockerfile at MASHUOA/hitmap_docker with the effort from John Reeves j.reeves@garvan.org.au).
-
mashuoa/hitmap:natcomms contains the original version when this project been accepted (minor changes applied to enhance the multi-files cluster image rendering).
-
mashuoa/hitmap:gui_latest contains the developing graphical user interface of HiTMaP. Please map the 3838 port to the container and access the GUI via http://localhost:3838/. We are happy to hear your voice regarding the High-RES IMS pre-processing, segmentation and annotation as well as their corresponding GUI configurations.
-
We are able to supply a singularity template to the users who want to deploy the HiTMaP on an HPC server. This scripts also are available at the MASHUOA/hitmap/dockerfiles.
Setting up and running the docker container:
# For windows user's, run the image with a local user\Documents\expdata folder mapped to the docker container:
docker run --name hitmap -v %userprofile%\Documents\expdata:/root/expdata -a stdin -a stdout -i -t mashuoa/hitmap /bin/bash
docker run --name hitmap_r411 -v %userprofile%\Documents\expdata:/root/expdata -a stdin -a stdout -i -t mashuoa/hitmap:base_R411 /bin/bash
# For linux or mac user's, run the image with a local user/expdata folder mapped to the docker container:
docker run --name hitmap -v ~/expdata:/root/expdata -a stdin -a stdout -i -t mashuoa/hitmap /bin/bash
#Run the R console
R
Revoke Docker terminal:
#use ctrl+d to exit the docker container shell
#Restart the container and connect to the shell
docker restart hitmap
docker container exec -it hitmap /bin/bash
Stop/remove docker container (warning: if no local disk is mapped to “~/expdata”, please backup your existing result files from the container before you remove it):
docker stop hitmap
docker rm hitmap
If you are using docker GUI, pull the docker image using the codes above and follow the image as below to setup the container. if you are using mashuoa/hitmap:shiny_server, please also map local host:3838 to the container (Ports -> local hosts -> 3838).
Docker GUI setting
Installation code for R console installation
The code below is used for an experienced R user to build a local R/HiTMaP running environment. Major dependencies to note:
- R base
- java running library (for linux, additional configuration is needed: R CMD javareconf)
- orca for plotly (https://github.com/plotly/orca/releases/tag/v1.3.1)
- magick++ (for Linux, additional configuration is needed to expand the pixel limitation)
#install the git package
install.packages("remotes")
install.packages("devtools")
#library(devtools)
library(remotes)
Sys.setenv("R_REMOTES_NO_ERRORS_FROM_WARNINGS" = "true")
remotes::install_github("MASHUOA/HiTMaP",force=T)
remotes::install_github("kuwisdelu/Cardinal",force=T)
3
no
#Update all dependencies
BiocManager::install(ask = F)
yes
library(HiTMaP)
For windows users, Rtools (https://cran.r-project.org/bin/windows/Rtools/) is required.
Codes for Linux OS building enviornment
Run the codes as below to enable the required components in Linux console.
sudo apt-get install tcl-dev tk-dev
sudo apt-get install r-cran-ncdf4
sudo apt-get install libz-dev
sudo apt install libxml2-dev
sudo apt install libssl-dev
sudo apt install libcurl4-openssl-dev
sudo apt-get install libnss-winbind winbind
sudo apt install dirmngr gnupg apt-transport-https ca-certificates software-properties-common
sudo add-apt-repository ppa:webupd8team/y-ppa-manager
sudo apt-get update
sudo apt-get install y-ppa-manager
sudo y-ppa-manager
sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys
sudo add-apt-repository 'deb https://cloud.r-project.org/bin/linux/ubuntu focal-cran40/'
sudo apt-cache policy r-base
sudo apt-get purge r-base
sudo apt-get install r-base-core="4.0.2-1.2004.0"
sudo apt-get install libmagick++-dev
sudo apt-get install libfftw3-dev
sudo apt-get install r-base-dev texlive-full
sudo apt-get install libudunits2-dev
sudo apt-get install libgdal-dev
Codes for Mac OS building enviornment (optional)
The following code is for a local GUI purpose. Hitmap now has been built on the shiny server system. You can skip this step in the later version. You may need to update the Xcode. Go to your Mac OS terminal and input:
xcode-select --install
You’ll then receive: xcode-select: note: install requested for command line developer tools You will be prompted at this point in a window to update Xcode Command Line tools.
You may also need to install the X11.app and tcl/tk support for Mac system:
-
X11.app: https://www.xquartz.org/
-
Use the following link to download and install the correct tcltk package for your OS version. https://cran.r-project.org/bin/macosx/tools/
Example data and source code
The HitMaP comes with a series of maldi-imaging datasets acquired by FT-ICR mass spectromety. With the following code, you can download these raw data set into a local folder.
You can download the example data manually through this link: “https://github.com/MASHUOA/HiTMaP/releases/download/1.0.1/Data.tar.gz”
Or download the files in a R console:
if(!require(piggyback)) install.packages("piggyback")
library(piggyback)
#made sure that this folder has enough space
wd="~/expdata/"
dir.create(wd)
setwd(wd)
pb_download("HiTMaP-master.zip", repo = "MASHUOA/HiTMaP", dest = ".",show_progress = F)
pb_download("Data.tar.gz", repo = "MASHUOA/HiTMaP", dest = ".")
untar('Data.tar.gz',exdir =".", tar="tar")
#unlink('Data.tar.gz')
list.dirs()
The example data contains three folders for three individual IMS datasets, which each contain a configuration file, and the fasta database, respectively: “./Bovinlens_Trypsin_FT” “./MouseBrain_Trypsin_FT” “./Peptide_calibrants_FT”
An Tiny version of data set is also available by using the code below:
if(!require(piggyback)) install.packages("piggyback")
library(piggyback)
#made sure that this folder has enough space
wd="~/expdata/"
dir.create(wd)
setwd(wd)
pb_download("Data_tiny.tar.gz", repo = "MASHUOA/HiTMaP", dest = ".")
untar('Data_tiny.tar.gz',exdir =".", tar="tar")
#unlink('Data.tar.gz')
list.dirs()
The tiny version dataset was generated from the Bovinlens and MouseBrain original data:
-
m/z range: 700 - 1400
-
pixel range:
x \<= 20%, y >= 80% (Bovinlens)
x \<= 30%, y \<= 20% (MouseBrain)
Proteomics identification on maldi-imaging dataset
To perform false-discovery rate controlled peptide and protein annotation, run the following script below in your R session:
#create candidate list
library(HiTMaP)
#set project folder that contains imzML, .ibd and fasta files
#wd=paste0(file.path(path.package(package="HiTMaP")),"/data/")
#set a series of imzML files to be processed
datafile=c("Bovinlens_Trypsin_FT/Bovin_lens.imzML")
wd="~/expdata/"
imaging_identification(
#==============Choose the imzml raw data file(s) to process make sure the fasta file in the same folder
datafile=paste0(wd,datafile),
threshold=0.005,
ppm=5,
FDR_cutoff = 0.05,
#==============specify the digestion enzyme specificity
Digestion_site="trypsin",
#==============specify the range of missed Cleavages
missedCleavages=0:1,
#==============Set the target fasta file
Fastadatabase="uniprot-bovin.fasta",
#==============Set the possible adducts and fixed modifications
adducts=c("M+H"),
Modifications=list(fixed=NULL,fixmod_position=NULL,variable=NULL,varmod_position=NULL),
#==============The decoy mode: could be one of the "adducts", "elements" or "isotope"
Decoy_mode = "isotope",
use_previous_candidates=F,
output_candidatelist=T,
#==============The pre-processing param
preprocess=list(force_preprocess=TRUE,
use_preprocessRDS=TRUE,
smoothSignal=list(method="Disable"),
reduceBaseline=list(method="Disable"),
peakPick=list(method="adaptive"),
peakAlign=list(tolerance=5, units="ppm"),
normalize=list(method=c("Disable","rms","tic","reference")[1],mz=1)),
#==============Set the parameters for image segmentation
spectra_segments_per_file=4,
Segmentation="spatialKMeans",
Smooth_range=1,
Virtual_segmentation=FALSE,
Virtual_segmentation_rankfile=NULL,
#==============Set the Score method for hi-resolution isotopic pattern matching
score_method="SQRTP",
peptide_ID_filter=2,
#==============Summarise the protein and peptide features across the project the result can be found at the summary folder
Protein_feature_summary=TRUE,
Peptide_feature_summary=TRUE,
Region_feature_summary=TRUE,
#==============The parameters for Cluster imaging. Specify the annotations of interest, the program will perform a case-insensitive search on the result file, extract the protein(s) of interest and plot them in the cluster imaging mode
plot_cluster_image_grid=FALSE,
ClusterID_colname="Protein",
componentID_colname="Peptide",
Protein_desc_of_interest=c("Crystallin","Actin"),
Rotate_IMG=NULL,
)
Project folder and result structure
In the above function, you have performed proteomics analysis on the sample data file. It is a tryptic Bovin lens MALDI-imaging file which is acquired on an FT-ICR MS. The function will take the selected data files’ root directory as the project folder. In this example, the project folder will be:
library(HiTMaP)
wd=paste0("D:\\GITHUB LFS\\HiTMaP-Data\\inst","/data/Bovinlens_Trypsin_FT/")
datafile=c("Bovin_lens")
After the whole identification process, you will get two sub-folders within the project folder:
list.dirs(wd, recursive=FALSE)
## [1] "D:\\GITHUB LFS\\HiTMaP-Data\\inst/data/Bovinlens_Trypsin_FT//Bovin_lens ID"
## [2] "D:\\GITHUB LFS\\HiTMaP-Data\\inst/data/Bovinlens_Trypsin_FT//Summary folder"
-
The one which has an identical name to an input data file contains the identification result of that input:
- the protein and peptides list of each segmentation region
- the PMF matching plot of each segmentation
- the image that indicates segmentations’ boundary (applies to either K-mean segmentation (powered by Cardinal) or manually defined segmentation)
- folders of each region contains the detailed identification process, FDR plots and isotopic pattern matching plots
-
“Summary folder” contains:
- the identification summary of protein and peptides across all the data
- the candidate list of all possible proteins and peptides (if use_previous_candidates is set as TRUE)
- the Cluster imaging files of the protein of interest
- the database stats result for resolution-based candidates binning (optional)
Identification result visulasation and interpretation
To plot the MALDI-image peptide and protein images, use the following functions:
To check the segmentation result over the sample, you need to navigate to each data file ID folder and find the “spatialKMeans_image_plot.png” (if you are using the spatial K-means method for segmentation.)
library(magick)
p<-image_read(paste0(wd,datafile," ID/spatialKMeans_image_plot.png"))
print(p)
## # A tibble: 1 x 7
## format width height colorspace matte filesize density
## <chr> <int> <int> <chr> <lgl> <int> <chr>
## 1 PNG 1024 2640 sRGB FALSE 30726 72x72
The pixels in image data now has been categorized into four regions according to the initial setting of segmentation (spectra_segments_per_file=5). The rainbow shaped bovine lens segmentation image (on the left panel) shows a unique statistical classification based on the mz features of each region (on the right panel).
The mouse brain example segmentation result (spatialKmeans n=9) shown as below:
For further investigation of the segmentation process, you may find a PCA images set in the “Datafile ID” folder. THe PCA images are good summary of features and potential region of interests within a data file. The combination of these PCs of interest will guide you to the insightful tissue structure profile.
The identification will take place on the mean spectra of each region. To check the peptide mass fingerprint (PMF) matching quality, you could locate the PMF spectrum matching plot of each individual region.
library(magick)
p_pmf<-image_read(paste0(wd,datafile," ID/Bovin_lens 3PMF spectrum match.png"))
print(p_pmf)
## # A tibble: 1 x 7
## format width height colorspace matte filesize density
## <chr> <int> <int> <chr> <lgl> <int> <chr>
## 1 PNG 1980 1080 sRGB FALSE 17664 72x72
A list of the peptides and proteins annotated within each region has also been created for manual exploration of the results.
peptide_pmf_result<-read.csv(paste0(wd,datafile," ID/Peptide_segment_PMF_RESULT_3.csv"))
head(peptide_pmf_result)
## Protein mz Protein_coverage isdecoy Peptide Modification pepmz
## 1 48 1300.664 0.06875544 0 HLEQFATEGLR NA 1299.657
## 2 48 1300.661 0.06875544 0 QYFLDLALSCK NA 1299.653
## 3 48 1324.643 0.06875544 0 GSKCILYCFYK NA 1323.636
## 4 53 1328.747 0.05542725 0 FKNINPFPVPR NA 1327.740
## 5 53 1449.712 0.05542725 0 AVQNFTEYNVHK NA 1448.705
## 6 53 1605.813 0.05542725 0 AVQNFTEYNVHKR NA 1604.806
## formula adduct charge start end pro_end mz_align Score Rank
## 1 C57H90N17O18 M+H 1 580 590 1149 1300.666 2.4633527 4
## 2 C60H94N13O17S1 M+H 1 744 754 1149 1300.666 2.0216690 10
## 3 C62H94N13O15S2 M+H 1 840 850 1149 1324.647 -0.2644896 32
## 4 C64H98N17O14 M+H 1 207 217 433 1328.747 1.0865820 7
## 5 C65H97N18O20 M+H 1 92 103 433 1449.714 0.7060553 10
## 6 C71H109N22O21 M+H 1 92 104 433 1605.806 2.7178547 11
## moleculeNames Region Delta_ppm Intensity peptide_count
## 1 HLEQFATEGLR 3 0.9026772 4672324.6 3
## 2 QYFLDLALSCK 3 1.4117311 4672324.6 3
## 3 GSKCILYCFYK 3 1.5164261 145191.4 3
## 4 FKNINPFPVPR 3 0.9094769 191636.4 3
## 5 AVQNFTEYNVHK 3 2.8830137 1275214.1 3
## 6 AVQNFTEYNVHKR 3 1.6464326 558610.4 3
## desc.x
## 1 sp|Q29449|AT8A1_BOVIN Probable phospholipid-transporting ATPase IA OS=Bos taurus OX=9913 GN=ATP8A1 PE=1 SV=2
## 2 sp|Q29449|AT8A1_BOVIN Probable phospholipid-transporting ATPase IA OS=Bos taurus OX=9913 GN=ATP8A1 PE=1 SV=2
## 3 sp|Q29449|AT8A1_BOVIN Probable phospholipid-transporting ATPase IA OS=Bos taurus OX=9913 GN=ATP8A1 PE=1 SV=2
## 4 sp|Q3SX05|ECSIT_BOVIN Evolutionarily conserved signaling intermediate in Toll pathway, mitochondrial OS=Bos taurus OX=9913 GN=ECSIT PE=2 SV=1
## 5 sp|Q3SX05|ECSIT_BOVIN Evolutionarily conserved signaling intermediate in Toll pathway, mitochondrial OS=Bos taurus OX=9913 GN=ECSIT PE=2 SV=1
## 6 sp|Q3SX05|ECSIT_BOVIN Evolutionarily conserved signaling intermediate in Toll pathway, mitochondrial OS=Bos taurus OX=9913 GN=ECSIT PE=2 SV=1
## desc.y
## 1 sp|Q29449|AT8A1_BOVIN Probable phospholipid-transporting ATPase IA OS=Bos taurus OX=9913 GN=ATP8A1 PE=1 SV=2
## 2 sp|Q29449|AT8A1_BOVIN Probable phospholipid-transporting ATPase IA OS=Bos taurus OX=9913 GN=ATP8A1 PE=1 SV=2
## 3 sp|Q29449|AT8A1_BOVIN Probable phospholipid-transporting ATPase IA OS=Bos taurus OX=9913 GN=ATP8A1 PE=1 SV=2
## 4 sp|Q3SX05|ECSIT_BOVIN Evolutionarily conserved signaling intermediate in Toll pathway, mitochondrial OS=Bos taurus OX=9913 GN=ECSIT PE=2 SV=1
## 5 sp|Q3SX05|ECSIT_BOVIN Evolutionarily conserved signaling intermediate in Toll pathway, mitochondrial OS=Bos taurus OX=9913 GN=ECSIT PE=2 SV=1
## 6 sp|Q3SX05|ECSIT_BOVIN Evolutionarily conserved signaling intermediate in Toll pathway, mitochondrial OS=Bos taurus OX=9913 GN=ECSIT PE=2 SV=1
protein_pmf_result<-read.csv(paste0(wd,datafile," ID/Protein_segment_PMF_RESULT_3.csv"))
head(protein_pmf_result)
## Protein Proscore isdecoy Intensity Score peptide_count Protein_coverage
## 1 10134 0.13943597 0 2873903.1 1.9269417 3 0.06715328
## 2 10204 0.13654123 0 380571.3 0.7940642 3 0.18468468
## 3 10370 0.20365140 0 1877250.1 2.0776861 4 0.09364548
## 4 10659 0.11239668 0 327352.4 0.7448240 3 0.16400000
## 5 10888 0.07975644 0 532832.0 1.2420183 3 0.06720978
## 6 11270 0.10687770 0 2944154.2 1.3292158 3 0.07449857
## Intensity_norm
## 1 1.0775539
## 2 0.9310593
## 3 1.0466962
## 4 0.9201443
## 5 0.9554442
## 6 1.0793038
## desc
## 1 tr|G3N2M8|G3N2M8_BOVIN Sterile alpha motif domain containing 15 OS=Bos taurus OX=9913 GN=SAMD15 PE=4 SV=2
## 2 tr|A0A3Q1LYB6|A0A3Q1LYB6_BOVIN Uncharacterized protein OS=Bos taurus OX=9913 PE=4 SV=1
## 3 tr|E1B9U7|E1B9U7_BOVIN Polypeptide N-acetylgalactosaminyltransferase OS=Bos taurus OX=9913 GN=GALNT17 PE=3 SV=3
## 4 tr|A0A3Q1M1B1|A0A3Q1M1B1_BOVIN Phosphatidylinositol transfer protein beta isoform OS=Bos taurus OX=9913 GN=PITPNB PE=4 SV=1
## 5 tr|F1MMD4|F1MMD4_BOVIN Matrix metallopeptidase 11 OS=Bos taurus OX=9913 GN=MMP11 PE=3 SV=2
## 6 tr|F6RR01|F6RR01_BOVIN Ribosome production factor 1 homolog OS=Bos taurus OX=9913 GN=RPF1 PE=4 SV=1
Scoring system for protein and peptide
Score in peptide result table shows the isotopic pattern matching score of the peptide (Pepscore). In Protein result table, it shows the protein score (Proscore). The ‘Pepscore’ consist of two parts: Intensity_Score and Mass_error_Score:
-
Intensity_Score indicates how well a putative isotopic pattern can be matched to the observed spectrum.The default scoring method is SQRTP. It combines the ‘square root mean’ differences between observed and theoretical peaks and observed proportion of the isotopic peaks above a certain relative intensity threshold.
-
Mass_error_Score indicates the summary of mass error (in ppm) for every detected isotopic peak. In order to integrate the Mass_error_Score in to scoring system, the mean ppm error has been normalized by ppm tolerance, and supplied to the probability normal distributions (pnorm function for R). The resulting value (quantiles of the given probability density) is deducted by 0.5 and converted into an absolute value.
Proscore in the protein result table shows the overall estimation of
the protein identification Accuracy.
A Peptide_region_file.csv has also been created to summarise all the IDs in this data file:
Identification_summary_table<-read.csv(paste0(wd,datafile," ID/Peptide_region_file.csv"))
head(Identification_summary_table)
## Protein mz Protein_coverage isdecoy Peptide Modification
## 1 24 1143.5793 0.06119704 0 GFPGQDGLAGPK NA
## 2 24 1684.8878 0.06119704 0 DGANGIPGPIGPPGPRGR NA
## 3 24 742.3478 0.06119704 0 GDSGPPGR NA
## 4 24 1693.8214 0.06119704 0 LLSTEGSQNITYHCK NA
## 5 24 1881.9276 0.06119704 0 GQPGVMGFPGPKGANGEPGK NA
## 6 48 1216.7008 0.03481288 0 ASTSVQNRLLK NA
## pepmz formula adduct charge start end pro_end mz_align
## 1 1142.5720 C51H79N14O16 M+H 1 516 527 1487 1143.5828
## 2 1683.8805 C72H118N25O22 M+H 1 1175 1192 1487 1684.8830
## 3 741.3406 C29H48N11O12 M+H 1 933 940 1487 742.3504
## 4 1692.8141 C72H117N20O25S1 M+H 1 1380 1394 1487 1693.8197
## 5 1880.9203 C82H129N24O25S1 M+H 1 597 616 1487 1881.9268
## 6 1215.6935 C51H94N17O17 M+H 1 614 624 1149 1216.7047
## Score Rank moleculeNames Region Delta_ppm Intensity peptide_count
## 1 1.4443497 2 GFPGQDGLAGPK 2 1.3471596 250698.3 5
## 2 1.9337304 2 DGANGIPGPIGPPGPRGR 2 1.5937657 2696717.3 5
## 3 1.2698949 1 GDSGPPGR 2 0.1407633 190469.7 5
## 4 1.3660521 3 LLSTEGSQNITYHCK 2 2.2329023 368927.9 5
## 5 0.5868561 17 GQPGVMGFPGPKGANGEPGK 2 3.0817671 974427.3 5
## 6 1.9039495 1 ASTSVQNRLLK 2 1.8837090 2036000.7 1
## desc.x
## 1 sp|P02459|CO2A1_BOVIN Collagen alpha-1(II) chain OS=Bos taurus OX=9913 GN=COL2A1 PE=1 SV=4
## 2 sp|P02459|CO2A1_BOVIN Collagen alpha-1(II) chain OS=Bos taurus OX=9913 GN=COL2A1 PE=1 SV=4
## 3 sp|P02459|CO2A1_BOVIN Collagen alpha-1(II) chain OS=Bos taurus OX=9913 GN=COL2A1 PE=1 SV=4
## 4 sp|P02459|CO2A1_BOVIN Collagen alpha-1(II) chain OS=Bos taurus OX=9913 GN=COL2A1 PE=1 SV=4
## 5 sp|P02459|CO2A1_BOVIN Collagen alpha-1(II) chain OS=Bos taurus OX=9913 GN=COL2A1 PE=1 SV=4
## 6 sp|Q29449|AT8A1_BOVIN Probable phospholipid-transporting ATPase IA OS=Bos taurus OX=9913 GN=ATP8A1 PE=1 SV=2
## desc.y
## 1 sp|P02459|CO2A1_BOVIN Collagen alpha-1(II) chain OS=Bos taurus OX=9913 GN=COL2A1 PE=1 SV=4
## 2 sp|P02459|CO2A1_BOVIN Collagen alpha-1(II) chain OS=Bos taurus OX=9913 GN=COL2A1 PE=1 SV=4
## 3 sp|P02459|CO2A1_BOVIN Collagen alpha-1(II) chain OS=Bos taurus OX=9913 GN=COL2A1 PE=1 SV=4
## 4 sp|P02459|CO2A1_BOVIN Collagen alpha-1(II) chain OS=Bos taurus OX=9913 GN=COL2A1 PE=1 SV=4
## 5 sp|P02459|CO2A1_BOVIN Collagen alpha-1(II) chain OS=Bos taurus OX=9913 GN=COL2A1 PE=1 SV=4
## 6 sp|Q29449|AT8A1_BOVIN Probable phospholipid-transporting ATPase IA OS=Bos taurus OX=9913 GN=ATP8A1 PE=1 SV=2
The details of protein/peptide identification process has been save to the folder named by the segmentation:
list.dirs(paste0(wd,datafile," ID/"), recursive=FALSE)
## [1] "D:\\GITHUB LFS\\HiTMaP-Data\\inst/data/Bovinlens_Trypsin_FT/Bovin_lens ID//1"
## [2] "D:\\GITHUB LFS\\HiTMaP-Data\\inst/data/Bovinlens_Trypsin_FT/Bovin_lens ID//2"
## [3] "D:\\GITHUB LFS\\HiTMaP-Data\\inst/data/Bovinlens_Trypsin_FT/Bovin_lens ID//3"
## [4] "D:\\GITHUB LFS\\HiTMaP-Data\\inst/data/Bovinlens_Trypsin_FT/Bovin_lens ID//4"
In the identification details folder, you will find a series of FDR file and plots to demonstrate the FDR model and score cutoff threshold:
dir(paste0(wd,datafile," ID/1/"), recursive=FALSE)
## [1] "FDR.CSV"
## [2] "FDR.png"
## [3] "Matching_Score_vs_mz_target-decoy.png"
## [4] "Peptide_1st_ID.csv"
## [5] "Peptide_1st_ID_score_rank_SQRTP.csv"
## [6] "Peptide_2nd_ID_score_rankSQRTP_Rank_above_3.csv"
## [7] "Peptide_Score_histogram_target-decoy.png"
## [8] "ppm"
## [9] "PROTEIN_FDR.CSV"
## [10] "Protein_FDR.png"
## [11] "Protein_ID_score_rank_SQRTP.csv"
## [12] "PROTEIN_Score_histogram.png"
## [13] "Spectrum.csv"
## [14] "unique_peptide_ranking_vs_mz_feature.png"
In this folder, you will find the FDR plots for protein and peptide annotation. The software will take the proscore and its FDR model to trim the final identification results. The unique_peptide_ranking_vs_mz_feature.png is a plot that could tell you the number of peptide candidates that have been matched to the mz features in the first round run. You can also access the peptide spectrum match (first MS dimension) data via the “/ppm” subfolder.
library(magick)
p_FDR_peptide<-image_read(paste0(wd,datafile," ID/3/FDR.png"))
p_FDR_protein<-image_read(paste0(wd,datafile," ID/3/protein_FDR.png"))
p_FDR_peptide_his<-image_read(paste0(wd,datafile," ID/3/Peptide_Score_histogram_target-decoy.png"))
p_FDR_protein_his<-image_read(paste0(wd,datafile," ID/3/PROTEIN_Score_histogram.png"))
p_combined<-image_append(c(p_FDR_peptide,p_FDR_peptide_his,p_FDR_protein,p_FDR_protein_his))
print(p_combined)
## # A tibble: 1 x 7
## format width height colorspace matte filesize density
## <chr> <int> <int> <chr> <lgl> <int> <chr>
## 1 PNG 1920 480 sRGB FALSE 0 72x72
You will also find a Matching_Score_vs_mz plot for further investigation on peptide matching quality.
library(magick)
#plot Matching_Score_vs_mz
p_Matching_Score_vs_mz<-image_read(paste0(wd,datafile," ID/3/Matching_Score_vs_mz_target-decoy.png"))
print(p_Matching_Score_vs_mz)
## # A tibble: 1 x 7
## format width height colorspace matte filesize density
## <chr> <int> <int> <chr> <lgl> <int> <chr>
## 1 PNG 480 480 sRGB FALSE 47438 72x72
Identification summary and cluster imaging
In the project summary folder, you will find four files and a sub-folder:
wd_sum=paste(wd,"/Summary folder", sep="")
dir(wd_sum)
## [1] "candidatelist.csv" "cluster Ion images"
## [3] "Peptide_Summary.csv" "Protein_feature_list_trimmed.csv"
## [5] "protein_index.csv" "Protein_Summary.csv"
“candidatelist.csv” and “protein_index.csv” contains the candidates used for this analysis. They are output after the candidate processing while output_candidatelist set as TRUE, and can be used repeatedly while use_previous_candidates set as TRUE.
We have implemented a functionality to perform additional statistical analysis around the number of enzymatically generated peptides derived from a given proteome database. If the user sets the argument ‘Database_stats’ to TRUE in the main workflow, the function will be called. Briefly, the function will list all of the m/z’s of a unique formulae from the peptide candidate pool within a given m/z range. The m/z’s will then be binned using three tolerance window: 1 ppm, 2 ppm and 5 ppm. A plot showing the number of unique formulae vs. m/z bins will be generated and exported to the summary folder (DB_stats_mz_bin).
Proteome database stats
“Peptide_Summary.csv” and “Protein_Summary.csv” contains the table of the project identification summary. You could set the plot_cluster_image_grid as TRUE to enable the cluster imaging function. Please be noted that you could indicate Rotate_IMG with a CSV file path that indicates the rotation degree of image files.
Note: 90°, 180° and 270° are recommended for image rotation. You may find an example CSV file in the expdata/MouseBrain_Trypsin_FT/file_rotationbk.csv.
library(dplyr)
Protein_desc_of_interest<-c("Crystallin","Actin")
Protein_Summary_tb<-read.csv(paste(wd,"/Summary folder","/Protein_Summary.csv", sep=""),stringsAsFactors = F)
Finally, you are able visualize the annotated proteins and their associated peptide distributions via the cluster image rendering function.
vimentin:
β-crystallin:
α-crystallin:
Secernin 1
CX6A1 cytochrome coxidase subunit 6A1
Myelin basic protein
Details of parameter setting
Modification
You can choose one or a list of modifications from the unimod modification list. Peptide_modification function is used to load/rebuild the modification database into the global enviornment of R. It will be called automatically in the identification work flow. you can use the code_name or record_id to refer the modification (see example data “peptide calibrants” to find more details). The pipeline will select the non-hidden modifications.
HiTMaP:::Peptide_modification(retrive_ID=NULL,update_unimod=F)
modification_list<-merge(unimod.df$modifications,unimod.df$specificity,by.x=c("record_id"),by.y=c("mod_key"),all.x=T)
head(modification_list['&'(modification_list$code_name=="Phospho",modification_list$hidden!=1),c("code_name","record_id","composition","mono_mass","position_key","one_letter")])
## code_name record_id composition mono_mass position_key one_letter
## 1615 Phospho 21 H O(3) P 79.966331 2 Y
## 1618 Phospho 21 H O(3) P 79.966331 2 T
## 1620 Phospho 21 H O(3) P 79.966331 2 S
head(modification_list['&'(modification_list$code_name=="Amide",modification_list$hidden!=1),c("code_name","record_id","composition","mono_mass","position_key","one_letter")])
## code_name record_id composition mono_mass position_key one_letter
## 1552 Amide 2 H N O(-1) -0.984016 6 C-term
## 1553 Amide 2 H N O(-1) -0.984016 4 C-term
head(modification_list['&'(stringr::str_detect(modification_list$code_name,"Ca"),modification_list$hidden!=1),c("code_name","record_id","composition","mono_mass","position_key","one_letter")])
## code_name record_id composition mono_mass position_key one_letter
## 1946 Carbamidomethyl 4 H(3) C(2) N O 57.021464 2 C
## 1949 Carbamidomethyl 4 H(3) C(2) N O 57.021464 3 N-term
## 2119 Carbamyl 5 H C N O 43.005814 3 N-term
## 2121 Carbamyl 5 H C N O 43.005814 2 K
## 2271 Carboxymethyl 6 H(2) C(2) O(2) 58.005479 2 C
If a modification occurs on a particular site, you will also need to specify the position of a modifications.
- Anywhere, side chain of possible amino acids
- Any N-term, any N-term of enzymatic peptide
- Protein N-term, any N-term of protein
unimod.df[["positions"]]
## position record_id
## 1 - 1
## 2 Anywhere 2
## 3 Any N-term 3
## 4 Any C-term 4
## 5 Protein N-term 5
## 6 Protein C-term 6
Amino acid substitution
You can set the Substitute_AA to make the uncommon amino acid available to the workflow: Substitute_AA=list(AA=c(“X”),AA_new_formula=c(“C5H5NO2”),Formula_with_water=c(FALSE))
- AA: the single letter amino acid to be replaced
- AA_new_formula: the new formula for the amino acid
- Formula_with_water: Set TRUE to indicate the formula represents the intact amino acid, FALSE to indicate that the formula already lost one H2O molecule and can be considered as AA backbone.
Digestion site and enzyme
The Digestion_site allows you to specify a list of pre-defined enzyme and customized digestion rules in regular expression format. You can either use the enzyme name, customized cleavage rule or combination of them to get the enzymatics peptides list.
Cleavage_rules<-Cleavage_rules_fun()
Cleavage_df<-data.frame(Enzyme=names(Cleavage_rules),Cleavage_rules=unname(Cleavage_rules),stringsAsFactors = F)
library(gridExtra)
grid.ftable(Cleavage_df, gp = gpar(fontsize=9,fill = rep(c("grey90", "grey95"))))
Imaging-MS data preprocessing
preprocess\$mz_bin_list is an argument for costumized peak-picking and mz bining purpose. If it is not NULL, the workflow will bypass signal smooth, noise reduction, and peakpicking steps. User need to give a numeric vector as the mz input to this argument. The workflow will first filter the vector with the given ppm tolerance to ensure there’s no overlapped mz bins (mz +/- ppm tolerance). Then, a m/z binning procedure will be conducted to the image data to produce a peak-picked dataset (the peak bin width will be the ppm tolerance).
If user uses a processed IMS data that contains the centroid feature value (e.g. exported from scils lab with feature list reduced data). User will still be safe to use this mz_bin_list in order to mount the centroid data properly. In this case, the ppm tolerance will only applied to the following annotation procedure.
normalize=list(method=c(“Disable”,“rms”,“tic”,“reference”)[1],mz=1) the current IMS normalization is done on pixel-to-pixel level, which will affect the feature distribution in some tissue. We use “Disable” in the example dataset to minimize the required RAM space and working time. The step may result in a big RAM usage on some IMS data. If the error message mentioned a “vector allocation” issue, Please consider to disable the normalization.
Example workflow command
Below is a list of commands including the parameters for the example data sets.
Peptide calibrant
#peptide calibrant
library(HiTMaP)
datafile=c("Peptide_calibrants_FT/trypsin_non-decell_w.calibrant_FTICR")
wd="~/expdata/"
# Calibrants dataset analysis with modification
imaging_identification(datafile=paste0(wd,datafile),
Digestion_site="trypsin",
Fastadatabase="uniprot_cali.fasta",
output_candidatelist=T,
plot_matching_score=T,
spectra_segments_per_file=1,
use_previous_candidates=F,
peptide_ID_filter=1,ppm=5,missedCleavages=0:5,
Modifications=list(fixed=NULL,fixmod_position=NULL,variable=c("Amide"),varmod_position=c(6)),
FDR_cutoff=0.1,
Substitute_AA=list(AA=c("X"),AA_new_formula=c("C5H5NO2"),Formula_with_water=c(FALSE)))
# Calibrants dataset analysis with no modification
imaging_identification(datafile=paste0(wd,datafile),
Digestion_site="trypsin",
Fastadatabase="uniprot_cali.fasta",
output_candidatelist=T,
plot_matching_score=T,
spectra_segments_per_file=1,
use_previous_candidates=T,
peptide_ID_filter=1,ppm=5,missedCleavages=0:5,
FDR_cutoff=0.1)
library(HiTMaP)
datafile=c("Peptide_calibrants_FT/trypsin_non-decell_w.calibrant_FTICR")
wd="~/expdata/"
# Calibrants dataset analysis with modification
imaging_identification(datafile=paste0(wd,datafile),
Digestion_site="trypsin",
Fastadatabase="calibrants.fasta",
output_candidatelist=T,
plot_matching_score=T,
spectra_segments_per_file=1,
use_previous_candidates=F,
peptide_ID_filter=1,ppm=5,missedCleavages=0:5,
Modifications=list(fixed=NULL,fixmod_position=NULL,variable=c("Amide"),varmod_position=c(6)),
FDR_cutoff=0.1,
Substitute_AA=list(AA=c("X"),AA_new_formula=c("C5H5NO2"),Formula_with_water=c(FALSE)),Thread = 1)
Bovine lens
library(HiTMaP)
datafile=c("Bovinlens_Trypsin_FT/Bovin_lens.imzML")
wd="~/expdata/"
# Data pre-processing and proteomics annotation
library(HiTMaP)
imaging_identification(datafile=paste0(wd,datafile),Digestion_site="trypsin",
Fastadatabase="uniprot-bovin.fasta",output_candidatelist=T,
preprocess=list(force_preprocess=TRUE,
use_preprocessRDS=TRUE,
smoothSignal=list(method="Disable"),
reduceBaseline=list(method="Disable"),
peakPick=list(method="adaptive"),
peakAlign=list(tolerance=5, units="ppm"),
normalize=list(method=c("Disable","rms","tic","reference")[1],mz=1)),
spectra_segments_per_file=4,use_previous_candidates=F,ppm=5,FDR_cutoff = 0.05,IMS_analysis=T,
Rotate_IMG="file_rotationbk.csv",plot_cluster_image_grid=F)
datafile=c("Bovinlens_Trypsin_FT/Bovin_lens.imzML")
wd="~/expdata/"
library(HiTMaP)
imaging_identification(datafile=paste0(wd,datafile),Digestion_site="trypsin",
Fastadatabase="uniprot-bovin.fasta",output_candidatelist=T,use_previous_candidates=T,
preprocess=list(force_preprocess=F,
use_preprocessRDS=TRUE,
smoothSignal=list(method="Disable"),
reduceBaseline=list(method="Disable"),
peakPick=list(method="Default"),
peakAlign=list(tolerance=5, units="ppm"),
normalize=list(method=c("Disable","rms","tic","reference")[1],mz=1)),
spectra_segments_per_file=4,ppm=5,FDR_cutoff = 0.05,IMS_analysis=T,
Rotate_IMG="file_rotationbk.csv",plot_cluster_image_grid=F)
# Re-analysis and cluster image rendering
library(HiTMaP)
datafile=c("Bovinlens_Trypsin_FT/Bovin_lens.imzML")
wd="~/expdata/"
imaging_identification(datafile=paste0(wd,datafile),Digestion_site="trypsin",
Fastadatabase="uniprot-bovin.fasta",
use_previous_candidates=T,ppm=5,IMS_analysis=F,
plot_cluster_image_grid=T,
export_Header_table=T,
img_brightness=250,
plot_cluster_image_overwrite=T,
cluster_rds_path = "/Bovin_lens ID/preprocessed_imdata.RDS",pixel_size_um = 150,
Plot_score_abs_cutoff=-0.1,
remove_score_outlier=T,
Protein_desc_of_interest=c("Crystallin","Phakinin","Filensin","Actin","Vimentin","Cortactin","Visinin","Arpin","Tropomyosin","Myosin Light Chain 3","Kinesin Family Member 14","Dynein Regulatory Complex","Ankyrin Repeat Domain 45"))
# Re-analysis and cluster image rendering using color scale
library(HiTMaP)
datafile=c("Bovinlens_Trypsin_FT/Bovin_lens.imzML")
wd="~/expdata/"
imaging_identification(datafile=paste0(wd,datafile),Digestion_site="trypsin",
Fastadatabase="uniprot-bovin.fasta",
use_previous_candidates=T,ppm=5,IMS_analysis=F,
plot_cluster_image_grid=T,
export_Header_table=T,
img_brightness=250,
plot_cluster_image_overwrite=T,
cluster_rds_path = "/Bovin_lens ID/preprocessed_imdata.RDS",pixel_size_um = 150,
Plot_score_abs_cutoff=-0.1,
remove_score_outlier=T,cluster_color_scale="fleximaging",
Protein_desc_of_interest=c("Crystallin","Phakinin","Filensin","Actin","Vimentin","Cortactin","Visinin","Arpin","Tropomyosin","Myosin Light Chain 3","Kinesin Family Member 14","Dynein Regulatory Complex","Ankyrin Repeat Domain 45"))
Mouse brain
library(HiTMaP)
datafile=c("MouseBrain_Trypsin_FT/Mouse_brain.imzML")
wd="~/expdata/"
# Data pre-processing and proteomics annotation
library(HiTMaP)
imaging_identification(datafile=paste0(wd,datafile),Digestion_site="trypsin",
Fastadatabase="uniprot_mouse_20210107.fasta",output_candidatelist=T,
preprocess=list(force_preprocess=T,
use_preprocessRDS=TRUE,
smoothSignal=list(method="Disable"),
reduceBaseline=list(method="Disable"),
peakPick=list(method="adaptive"),
peakAlign=list(tolerance=5, units="ppm"),
normalize=list(method=c("Disable","rms","tic","reference")[1],mz=1)),
spectra_segments_per_file=9,use_previous_candidates=F,ppm=10,FDR_cutoff = 0.05,IMS_analysis=T,
Rotate_IMG="file_rotationbk.csv",
mzrange = c(500,4000),plot_cluster_image_grid=F)
imaging_identification(datafile=paste0(wd,datafile),Digestion_site="trypsin",
Fastadatabase="uniprot_mouse_20210107.fasta",output_candidatelist=T,
preprocess=list(force_preprocess=T,
use_preprocessRDS=TRUE,
smoothSignal=list(method="gaussian"),
reduceBaseline=list(method="locmin"),
peakPick=list(method="adaptive"),
peakAlign=list(tolerance=5, units="ppm"),
normalize=list(method=c("Disable","rms","tic","reference")[1],mz=1)),
spectra_segments_per_file=9,use_previous_candidates=F,ppm=10,FDR_cutoff = 0.05,IMS_analysis=T,
Rotate_IMG="file_rotationbk.csv",
mzrange = c(500,4000),plot_cluster_image_grid=F)
# Re-analysis and cluster image rendering
library(HiTMaP)
datafile=c("MouseBrain_Trypsin_FT/Mouse_brain.imzML")
wd="~/expdata/"
imaging_identification(datafile=paste0(wd,datafile),Digestion_site="trypsin",
Fastadatabase="uniprot_mouse_20210107.fasta",
preprocess=list(force_preprocess=FALSE),
spectra_segments_per_file=9,use_previous_candidates=T,ppm=10,FDR_cutoff = 0.05,IMS_analysis=F,
mzrange = c(500,4000),plot_cluster_image_grid=T,
img_brightness=250, plot_cluster_image_overwrite=T,
cluster_rds_path = "/Mouse_brain ID/preprocessed_imdata.RDS",
pixel_size_um = 50,
Plot_score_abs_cutoff=-0.1,
remove_score_outlier=T,
Protein_desc_of_interest=c("Secernin","GN=MBP","Cytochrome"))
library(HiTMaP)
datafile=c("MouseBrain_Trypsin_FT_200brit_man_seg/Mouse_brain.imzML")
wd="~/expdata/"
imaging_identification(datafile=paste0(wd,datafile),Digestion_site="trypsin",
Fastadatabase="uniprot_mouse_20210107.fasta",
preprocess=list(force_preprocess=FALSE),
spectra_segments_per_file=9,use_previous_candidates=T,ppm=10,FDR_cutoff = 0.05,IMS_analysis=F,
mzrange = c(500,4000),plot_cluster_image_grid=T,
img_brightness=250, plot_cluster_image_overwrite=T,
cluster_rds_path = "/Mouse_brain ID/preprocessed_imdata.RDS",
pixel_size_um = 50,
Plot_score_abs_cutoff=-0.1,
remove_score_outlier=T,
Protein_desc_of_interest=c("GTR9"))
Cite this project
This study has been accepted by Nature Communications: “Automated annotation and visualisation of high-resolution spatial proteomic mass spectrometry imaging data using HIT-MAP” online on the 28th May 2021.
Session information
sessionInfo()
## R version 4.1.3 (2022-03-10)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19044)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_Australia.1252 LC_CTYPE=English_Australia.1252
## [3] LC_MONETARY=English_Australia.1252 LC_NUMERIC=C
## [5] LC_TIME=English_Australia.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] gridExtra_2.3 XML_3.99-0.10 protViz_0.7.3 dplyr_1.0.9 magick_2.7.3
## [6] HiTMaP_1.0.0
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.9 pillar_1.8.0 compiler_4.1.3 highr_0.9
## [5] tools_4.1.3 digest_0.6.29 evaluate_0.15 lifecycle_1.0.1
## [9] tibble_3.1.8 gtable_0.3.0 pkgconfig_2.0.3 png_0.1-7
## [13] rlang_1.0.4 cli_3.3.0 DBI_1.1.3 rstudioapi_0.13
## [17] yaml_2.3.5 xfun_0.31 fastmap_1.1.0 stringr_1.4.0
## [21] knitr_1.39 generics_0.1.3 vctrs_0.4.1 tidyselect_1.1.2
## [25] glue_1.6.2 R6_2.5.1 fansi_1.0.3 rmarkdown_2.14
## [29] purrr_0.3.4 magrittr_2.0.3 codetools_0.2-18 htmltools_0.5.3
## [33] assertthat_0.2.1 utf8_1.2.2 stringi_1.7.8
End of the tutorial, Enjoy~
References
R Packages used in this project:
-
viridisLite[@viridisLite]
-
rcdklibs[@rcdklibs]
-
rJava[@rJava]
-
data.table[@data.table]
-
RColorBrewer[@RColorBrewer]
-
magick[@magick]
-
ggplot2[@ggplot2]
-
dplyr[@dplyr]
-
stringr[@stringr]
-
protViz[@protViz]
-
cleaver[@cleaver]
-
Biostrings[@Biostrings]
-
IRanges[@IRanges]
-
Cardinal[@Cardinal]
-
tcltk[@tcltk]
-
BiocParallel[@BiocParallel]
-
spdep[@spdep1]
-
FTICRMS[@FTICRMS]
-
UniProt.ws[@UniProt.ws]
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