diff --git a/R/Utilities.R b/R/Utilities.R index 09823ea..6301b24 100644 --- a/R/Utilities.R +++ b/R/Utilities.R @@ -678,7 +678,7 @@ cluster_image_grid<-function(clusterID, cluster_desc<-unique(candidate$desc)[1] } - require(reshape2) + suppressMessages(suppressWarnings(require(reshape2))) Header_table<-NULL Header_table$mz=candidateunique diff --git a/README.Rmd b/README.Rmd index 79d25c2..15bf7e5 100644 --- a/README.Rmd +++ b/README.Rmd @@ -10,6 +10,7 @@ output: highlight: zenburn pdf_document: default word_document: default +bibliography: references.bib --- -- An R package of High-resolution Informatics Toolbox for Maldi-imaging Proteomics @@ -238,6 +239,50 @@ p_cluster3<-image_read(paste0(wd,"/Summary folder/cluster Ion images/5479_cluste print(p_cluster3) ``` + +## 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] + + + ## Session information ```{r} @@ -246,6 +291,4 @@ sessionInfo() - - End of the tutorial, Enjoy~ diff --git a/README.html b/README.html index 8761a1b..7e4ef71 100644 --- a/README.html +++ b/README.html @@ -1,11 +1,10 @@ - +
- @@ -410,6 +409,7 @@ border: none; display: inline-block; border-radius: 4px; + background-color: transparent; } .tabset-dropdown > .nav-tabs.nav-tabs-open > li { @@ -453,7 +453,8 @@The pixels in image data now has been categorized into five 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 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)
@@ -572,20 +573,20 @@ 0.4 Identification result visulas
## # 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 17113 72x72
-
+## 1 PNG 1980 1080 sRGB FALSE 17165 72x72
+
list of Peptides and proteins of each region has also been created so that you may check each individual region’s result.
peptide_pmf_result<-read.csv(paste0(wd,datafile," ID/Peptide_segment_PMF_RESULT_3.csv"))
head(peptide_pmf_result)
## # A tibble: 6 x 16
## Protein mz Peptide adduct formula isdecoy pepmz charge mz_align Score
## <int> <dbl> <fct> <fct> <fct> <int> <dbl> <int> <dbl> <dbl>
-## 1 9 1467. GGNELD~ M+H C60H96~ 0 1466. 1 1467. 0.369
-## 2 9 1323. QEDQLQ~ M+H C55H88~ 0 1322. 1 1323. 0.573
+## 1 9 1323. QIDQKE~ M+H C55H88~ 0 1322. 1 1323. 0.573
+## 2 9 1467. GGNELD~ M+H C60H96~ 0 1466. 1 1467. 0.369
## 3 9 1469. NEEPSS~ M+H C62H94~ 0 1468. 1 1469. 3.44
-## 4 9 1323. QIDQKE~ M+H C55H88~ 0 1322. 1 1323. 0.573
-## 5 13 1493. MDPTDA~ M+H C62H98~ 0 1492. 1 1493. 2.43
-## 6 13 1359. LKELEV~ M+H C58H10~ 0 1358. 1 1359. 0.542
+## 4 9 1323. QEDQLQ~ M+H C55H88~ 0 1322. 1 1323. 0.573
+## 5 13 1359. LKELEV~ M+H C58H10~ 0 1358. 1 1359. 0.542
+## 6 13 1493. MDPTDA~ M+H C62H98~ 0 1492. 1 1493. 2.43
## # ... with 6 more variables: Rank <int>, Intensity <dbl>,
## # moleculeNames <fct>, Region <int>, Delta_ppm <dbl>, desc <fct>
protein_pmf_result<-read.csv(paste0(wd,datafile," ID/Protein_segment_PMF_RESULT_3.csv"))
@@ -593,12 +594,12 @@ 0.4 Identification result visulas
## # A tibble: 6 x 9
## Protein Proscore isdecoy Intensity Score peptide_count Protein_coverage
## <int> <dbl> <int> <dbl> <dbl> <int> <dbl>
-## 1 10134 0.147 0 1189179. 1.30 5 0.0978
-## 2 10204 0.179 0 167823. 0.912 4 0.198
-## 3 10370 0.169 0 990324. 2.06 3 0.0719
-## 4 10628 0.0617 0 340804. 1.06 2 0.0553
-## 5 10691 0.125 0 442322. 1.36 3 0.0861
-## 6 10754 0.0699 0 93787. 2.31 1 0.0321
+## 1 10003 0.0552 0 88609. 0.855 2 0.0685
+## 2 1002 0.0556 0 580348. 0.843 4 0.0600
+## 3 10112 0.100 0 641587. 0.889 2 0.102
+## 4 10134 0.0914 0 1455153. 1.05 4 0.0745
+## 5 10204 0.180 0 167823. 0.912 4 0.198
+## 6 10370 0.170 0 990324. 2.06 3 0.0719
## # ... with 2 more variables: Intensity_norm <dbl>, desc <fct>
## # A tibble: 6 x 16
## Protein mz Peptide adduct formula isdecoy pepmz charge mz_align Score
## <int> <dbl> <fct> <fct> <fct> <int> <dbl> <int> <dbl> <dbl>
-## 1 393 1881. GMSIDQ~ M+H C81H13~ 0 1880. 1 1881. 2.56
-## 2 393 1301. RPAEIY~ M+H C55H90~ 0 1300. 1 1301. 0.765
-## 3 393 1170. LGALWV~ M+H C54H89~ 0 1169. 1 1170. 0.224
-## 4 452 1837. DGQVIN~ M+H C74H11~ 0 1836. 1 1837. 1.72
-## 5 452 932. LLEGEE~ M+H C38H66~ 0 931. 1 932. 1.78
-## 6 452 2203. EMEENF~ M+H C92H14~ 0 2202. 1 2203. 0.631
+## 1 148 1138. MVEFAG~ M+H C50H81~ 0 1137. 1 1138. 1.39
+## 2 148 2594. AFIVWN~ M+H C121H1~ 0 2593. 1 2594. 2.31
+## 3 216 1881. ITTLQQ~ M+H C77H13~ 0 1880. 1 1881. 1.64
+## 4 216 1458. ELELGE~ M+H C61H10~ 0 1457. 1 1458. 0.794
+## 5 393 1170. LGALWV~ M+H C54H89~ 0 1169. 1 1170. 0.224
+## 6 393 1301. RPAEIY~ M+H C55H90~ 0 1300. 1 1301. 0.765
## # ... with 6 more variables: Rank <int>, Intensity <dbl>,
## # moleculeNames <fct>, Region <int>, Delta_ppm <dbl>, desc <fct>
The details of protein/peptide identification process has been save to the folder named by the segmentation:
@@ -651,7 +652,7 @@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"))
@@ -662,7 +663,7 @@ 0.5 Scoring system for protein an
## 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 plots for further investigation on peptide matching quality.
library(magick)
#plot Matching_Score_vs_mz
@@ -672,7 +673,7 @@ 0.5 Scoring system for protein an
## format width height colorspace matte filesize density
## <chr> <int> <int> <chr> <lgl> <int> <chr>
## 1 PNG 480 480 sRGB FALSE 54177 72x72
-
+
p_cluster3<-image_read(paste0(wd,"/Summary folder/cluster Ion images/5479_cluster_imaging.png"))
print(p_cluster3)
## # A tibble: 1 x 7
## format width height colorspace matte filesize density
## <chr> <int> <int> <chr> <lgl> <int> <chr>
## 1 PNG 1980 385 sRGB TRUE 390494 118x118
-
+
+R Packages used in this project:
+viridisLite(Garnier 2018)
rcdklibs(Guha 2017)
rJava(Urbanek 2019)
data.table(Dowle and Srinivasan 2019)
RColorBrewer(Neuwirth 2014)
magick(Ooms 2019)
ggplot2(Wickham 2016)
dplyr(Wickham et al. 2019)
stringr(Wickham 2019)
protViz(Panse and Grossmann 2019)
cleaver(Gibb 2019)
Biostrings(Pag�s et al. 2019)
IRanges(Lawrence et al. 2013)
Cardinal(Bemis et al. 2015)
tcltk(R Core Team 2019)
BiocParallel(Morgan et al. 2019)
spdep(Bivand and Wong 2018)
FTICRMS(Barkauskas 2012)
UniProt.ws(Carlson 2019)
## R version 3.6.1 (2019-07-05)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
@@ -741,12 +767,71 @@ 0.7 Session information
## [28] grid_3.6.1 glue_1.3.1 Biobase_2.44.0
## [31] R6_2.4.0 fansi_0.4.0 tcltk_3.6.1
## [34] XML_3.98-1.20 survival_2.44-1.1 BiocParallel_1.18.1
-## [37] pacman_0.5.1 rmarkdown_1.16 purrr_0.3.2
+## [37] pacman_0.5.1 rmarkdown_1.18 purrr_0.3.2
## [40] magrittr_1.5 backports_1.1.5 MASS_7.3-51.4
## [43] codetools_0.2-16 htmltools_0.4.0 BiocGenerics_0.30.0
## [46] splines_3.6.1 assertthat_0.2.1 utf8_1.1.4
## [49] stringi_1.4.3 doParallel_1.0.15 crayon_1.3.4
End of the tutorial, Enjoy~
+Barkauskas, Don. 2012. FTICRMS: Programs for Analyzing Fourier Transform-Ion Cyclotron Resonance Mass Spectrometry Data. https://CRAN.R-project.org/package=FTICRMS.
+Bemis, Kyle D., April Harry, Livia S. Eberlin, Christina Ferreira, Stephanie M. van de Ven, Parag Mallick, Mark Stolowitz, and Olga Vitek. 2015. “Cardinal: An R Package for Statistical Analysis of Mass Spectrometry-Based Imaging Experiments.” Bioinformatics. https://doi.org/10.1093/bioinformatics/btv146.
+Bivand, Roger, and David W. S. Wong. 2018. “Comparing Implementations of Global and Local Indicators of Spatial Association.” TEST 27 (3): 716–48. https://doi.org/10.1007/s11749-018-0599-x.
+Carlson, Marc. 2019. UniProt.ws: R Interface to Uniprot Web Services.
+Dowle, Matt, and Arun Srinivasan. 2019. Data.table: Extension of ‘Data.frame‘. https://CRAN.R-project.org/package=data.table.
+Garnier, Simon. 2018. ViridisLite: Default Color Maps from ’Matplotlib’ (Lite Version). https://CRAN.R-project.org/package=viridisLite.
+Gibb, Sebastian. 2019. Cleaver: Cleavage of Polypeptide Sequences. https://github.com/sgibb/cleaver/.
+Guha, Rajarshi. 2017. Rcdklibs: The Cdk Libraries Packaged for R. https://CRAN.R-project.org/package=rcdklibs.
+Lawrence, Michael, Wolfgang Huber, Hervé Pagès, Patrick Aboyoun, Marc Carlson, Robert Gentleman, Martin Morgan, and Vincent Carey. 2013. “Software for Computing and Annotating Genomic Ranges.” PLoS Computational Biology 9 (8). https://doi.org/10.1371/journal.pcbi.1003118.
+Morgan, Martin, Valerie Obenchain, Michel Lang, Ryan Thompson, and Nitesh Turaga. 2019. BiocParallel: Bioconductor Facilities for Parallel Evaluation. https://github.com/Bioconductor/BiocParallel.
+Neuwirth, Erich. 2014. RColorBrewer: ColorBrewer Palettes. https://CRAN.R-project.org/package=RColorBrewer.
+Ooms, Jeroen. 2019. Magick: Advanced Graphics and Image-Processing in R. https://CRAN.R-project.org/package=magick.
+Pag�s, H., P. Aboyoun, R. Gentleman, and S. DebRoy. 2019. Biostrings: Efficient Manipulation of Biological Strings.
+Panse, Christian, and Jonas Grossmann. 2019. ProtViz: Visualizing and Analyzing Mass Spectrometry Related Data in Proteomics.
+R Core Team. 2019. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
+Urbanek, Simon. 2019. RJava: Low-Level R to Java Interface. https://CRAN.R-project.org/package=rJava.
+Wickham, Hadley. 2016. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org.
+———. 2019. Stringr: Simple, Consistent Wrappers for Common String Operations. https://CRAN.R-project.org/package=stringr.
+Wickham, Hadley, Romain Fran�ois, Lionel Henry, and Kirill Muller. 2019. Dplyr: A Grammar of Data Manipulation. https://CRAN.R-project.org/package=dplyr.
+