zcp 2024-12-22
ZCPstates is an R package primarily designed for proteomics data analysis. It will be continuously updated with new features, covering the entire proteomics workflow as well as general statistical analysis and visualization tools.
if(!require(devtools)){install.packages("devtools")}
devtools::install_github("Chuanping-Zhao/ZCPstates")## Using GitHub PAT from the git credential store.
## Skipping install of 'ZCPstates' from a github remote, the SHA1 (fb1633d0) has not changed since last install.
## Use `force = TRUE` to force installation
This package provides the uniprotEnrich() function for analyzing
proteomics search database results. Users only need to supply the
UniProt database with the same Taxonomy ID as the search fasta file.
Additionally, the package offers visualization options
[uniprotEnrich_plot()] for the results of the uniprotEnrich()
function, including enrichment network visualization based on the
aPEAR package, as well as conventional scatter plot and bar chart
visualizations:
The input format for differential analysis data is as follows:
uniprotEnrich.demo.diff## # A tibble: 2,000 × 2
## Entry Sig
## <chr> <chr>
## 1 Q0WLC6 Up
## 2 O82533 NotSig
## 3 P0C2F5 NotSig
## 4 Q9C9L4 NotSig
## 5 Q9SMP2 NotSig
## 6 Q9M2Q6 NotSig
## 7 Q8LE43 NotSig
## 8 A4FVR1 NotSig
## 9 O65258 NotSig
## 10 Q680P8 NotSig
## # ℹ 1,990 more rows
The UniProt database format should include the following mandatory
columns(Entry,Pathway,Gene Ontology (biological process),
Gene Ontology (cellular component),Gene Ontology (molecular function)),
which are essential for the enrichment analysis:
uniprotEnrich.demo.library.uniprot## # A tibble: 16,318 × 21
## Entry Reviewed `Entry Name` `Protein names` `Gene Names` Organism Length Gene Ontology (biolo…¹ Gene Ontology (cellu…² Gene Ontology (molec…³ `Gene Ontology (GO)`
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 A0A0A7EPL0 reviewed PIAL1_ARATH E4 SUMO-protei… PIAL1 EMB30… Arabido… 847 positive regulation o… nucleus [GO:0005634] ligase activity [GO:0… nucleus [GO:0005634…
## 2 A0A178VEK7 reviewed DUO1_ARATH Transcription … DUO1 MYB125… Arabido… 300 cell cycle G2/M phase… generative cell nucle… DNA-binding transcrip… generative cell nuc…
## 3 A0A178WF56 reviewed CSTM3_ARATH Protein CYSTEI… CYSTM3 At1g… Arabido… 71 negative regulation o… cytoplasm [GO:0005737… <NA> cytoplasm [GO:00057…
## 4 A0A1I9LMX5 reviewed PCEP9_ARATH Precursor of C… CEP9 At3g50… Arabido… 243 cellular response to … apoplast [GO:0048046] hormone activity [GO:… apoplast [GO:004804…
## 5 A0A1I9LN01 reviewed LAF3_ARATH Protein LONG A… LAF3 At3g55… Arabido… 583 de-etiolation [GO:000… membrane [GO:0016020]… hydrolase activity, a… membrane [GO:001602…
## 6 A0A1P8AQ95 reviewed STMP4_ARATH Secreted trans… STMP4 At1g6… Arabido… 97 response to ethylene … apoplast [GO:0048046]… LRR domain binding [G… apoplast [GO:004804…
## 7 A0A1P8ASY1 reviewed JHS1_ARATH DNA replicatio… JHS1 EMB241… Arabido… 1331 DNA damage response [… chromosome [GO:000569… 4 iron, 4 sulfur clus… chromosome [GO:0005…
## 8 A0A1P8AUY4 reviewed MDN1_ARATH Midasin (AtMDN… MDN1 DSR1 A… Arabido… 5400 abscisic acid-activat… chloroplast envelope … ATP binding [GO:00055… chloroplast envelop…
## 9 A0A1P8AW69 reviewed KTN81_ARATH Katanin p80 WD… KTN80.1 At1… Arabido… 1019 microtubule depolymer… cytoplasm [GO:0005737… microtubule binding [… cytoplasm [GO:00057…
## 10 A0JQ18 reviewed SOP14_ARATH Serine rich en… PROSCOOP14 … Arabido… 73 defense response [GO:… apoplast [GO:0048046]… LRR domain binding [G… apoplast [GO:004804…
## # ℹ 16,308 more rows
## # ℹ abbreviated names: ¹`Gene Ontology (biological process)`, ²`Gene Ontology (cellular component)`, ³`Gene Ontology (molecular function)`
## # ℹ 10 more variables: `Gene Ontology IDs` <chr>, BioGRID <chr>, CORUM <lgl>, ComplexPortal <chr>, DIP <chr>, ELM <chr>, IntAct <chr>, MINT <chr>, STRING <chr>,
## # Pathway <chr>
After inputting the above two datasets and setting the corresponding parameters, all enrichment results will be generated in the specified folder:
result <- ZCPstates::uniprotEnrich(
dt = uniprotEnrich.demo.diff,
library=uniprotEnrich.demo.library.uniprot,
protein.col.name="Entry",
Protein.separator=";",
diff.condition.col.name="Sig",
diff.markers=c("Up","Down","NotSig"),
dt.include.allproteins=TRUE,
cutoff=1,
savefile="outputfile"
)## zcp: 'outputfile' already exists!All results have been saved to outputfile --zcp
tibble::as_tibble(result$all$pathway_enrich_result)## # A tibble: 63 × 8
## Pathway proteincounts.per.pahtway.all counts p_value FDR Enrich_factor hited.proteins Type
## <chr> <int> <int> <dbl> <dbl> <dbl> <chr> <chr>
## 1 Phospholipid metabolism 4 2 0.763 0.876 0.5 A4GNA8/Q9SYC8 Path…
## 2 phosphatidylethanolamine biosynthesis 1 1 0.765 0.876 1 A4GNA8 Path…
## 3 phosphatidylethanolamine from CDP-diacylglycerol: step 2/2 1 1 0.765 0.876 1 A4GNA8 Path…
## 4 Metabolic intermediate biosynthesis 4 2 0.763 0.876 0.5 P43256/Q8S4Y1 Path…
## 5 (R)-mevalonate biosynthesis 2 2 0.945 0.975 1 P43256/Q8S4Y1 Path…
## 6 (R)-mevalonate from acetyl-CoA: step 3/3 1 1 0.765 0.876 1 P43256 Path…
## 7 Carbohydrate metabolism 4 1 0.342 0.876 0.25 P93033 Path…
## 8 tricarboxylic acid cycle 3 1 0.447 0.876 0.333 P93033 Path…
## 9 (S)-malate from fumarate: step 1/1 1 1 0.765 0.876 1 P93033 Path…
## 10 Protein modification 75 19 0.604 0.876 0.253 Q0WX00/Q8VZS9/Q93Z16/Q9LFU0/Q9XGM8/Q… Path…
## # ℹ 53 more rows
Subsequently, extract the required data from result, and use the
uniprotEnrich_plot() function to complete the visualization:
plotting network:
go.cc=as.data.frame(result$Up$go_enrichment) |> dplyr::filter(Type=="CC")
plt.go.cc.network=ZCPstates::uniprotEnrich_plot(dt = uniprotEnrichplot.demo.GO.CC, plot.type = c("bar","point","network")[3],enrich.type=c("pathway","GO")[2], GO.subset=c("BP","CC","MF","All")[2],network.fontsize=2,network.minclustersize=2,network.simMethod=c("jaccard", "cosine", "cor")[1])
ZCPstates::save_zcp(Fig = plt.go.cc.network,FigName = "plt.go.cc.network",outputfile = "Figure",widths = 5,heights = 4,ppt = F)## zcp: Figure already exists!
plotting scatter plots:
go.cc.top10=go.cc |> dplyr:: slice_max(order_by = counts, n = 10)
plt.go.cc.top10.scatter=uniprotEnrich_plot(dt = go.cc.top10,plot.type = c("bar","point","network")[2], enrich.type=c("pathway","GO")[2],GO.subset=c("BP","CC","MF","All")[2])
ZCPstates::save_zcp(Fig = plt.go.cc.top10.scatter,FigName = "plt.go.cc.top10.scatter",outputfile = "Figure",widths = 4,heights =3,ppt = F)## zcp: Figure already exists!
plotting bar charts:
plt.go.cc.top10.bar=uniprotEnrich_plot(dt = go.cc.top10,plot.type = c("bar","point","network")[1], enrich.type=c("pathway","GO")[2],GO.subset=c("BP","CC","MF","All")[2])
ZCPstates::save_zcp(Fig = plt.go.cc.top10.bar,FigName = "plt.go.cc.top10.bar",outputfile = "Figure",widths = 4,heights =4,ppt = F)## zcp: Figure already exists!
Additional functions will be added in future updates. If you encounter
any bugs or issues, please contact me via GitHub or the WeChat public
account [MS driven Multiomics].