Introduction
The following tutorials is based on a single condition experiment
data.
2.0 Loading One Condition Example
## Loading One Condition Data
data("oneConditionExample", package = "NetPhorce")
DT::datatable(oneConditionExample,
rownames = FALSE,
options = list(
pageLength = 5,
scrollX = TRUE,
rownames = FALSE,
autoWidth = TRUE,
columnDefs = list(list(targets=c(7), width = "700px"),
list(className = 'dt.center', targets = "_all"))
))
3.0 Pre-processing the raw MaxQuant data
3.1 Identifying the Key Columns
## Identify the Key Columns
identifiedCols <- confirmColumnNames(rawMaxQuant = oneConditionExample,
positionCol = "Position",
reverseCol = "Reverse",
localizationProbCol = "Localization prob",
potentialContaminationCol = "Potential contaminant",
aminoAcidCol = "Amino acid",
uniqueIDCol = "Protein",
seqWindowIDCol = "Sequence window",
fastaIDCol = "Fasta headers")
#> [1] "MaxQuant Data Matrix Statistics: "
#> [1] "Number of Rows (Peptides): 340"
#> [1] "Number of Columns (Variables): 83"
#> [1] "Required Columns are all Found"
3.2 Identifying the pattern for the intensity columns
## Identify the Intensity Columns with Condition, Time Point and Replication Information
intensityCols <- confirmIntensityColumns(rawMaxQuant = oneConditionExample,
intensityPattern = "con_time_rep",
verbose = TRUE)
#> First Found Column Name is:
#> Intensity HT_0min_A___1
#> ---------------------------------------------------------------------------------------
#> Summary below is shown as unique Replications per Condition for each unique Time Point:
#> Time HT
#> 0min A;B;C;D;E
#> 12min A;B;C;D;E
#> 3min A;B;C;D;E
#> 6min A;B;C;D;E
#> 9min A;B;C;D;E
4.0 Process the raw MaxQuant data
## Process the data based on the identified columns
netPhorceData <- processData(rawMaxQuant = oneConditionExample,
processedColNames = identifiedCols,
processedIntensity = intensityCols,
minReplication = 3,
minLocalProb = 0.75)
#>
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#>
#> Number of Conditions Found: 1. Undergoing a time consuming step, please wait...
#>
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#>
#> Complete.
5.0 Validate the Kinases and Phosphatase data for the processed
netPhorce data
## Validating the Kinase/Phosphatase Information
netPhorceData <- validateKinaseTable(netPhorceData = netPhorceData,
defaultKinaseTable = TRUE,
abbrev = "Ath")
#> Kinase and Phosphatase Matching Table:
#> Matched Proteins Matched Peptides
#> Kinase 5 12
#> Phosphatase 3 8
#> Unmatched 77 183
6.0 Confirmation on the Regulation Thresholds for the netPhorce
data
## Regulation Validation based on user inputs
netPhorceData <- regulationCheck(netPhorceData = netPhorceData,
upReg = 0.25,
downReg = 0.25,
absMinThreshold = 0.1,
qValueCutOff = 0.05,
verbose = TRUE)
#> # Fold change occurances
#> Dephosphorylation 72
#> Unchanged 55
#> Phosphorylation 89
7.0 Network Anlaysis
## Network Analysis
netPhorceData <- networkAnalysis(netPhorceData = netPhorceData,
requestPlotData = TRUE)
#>
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8.0 Session Info
sessionInfo()
#> R version 4.1.0 (2021-05-18)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows 10 x64 (build 19044)
#>
#> Matrix products: default
#>
#> locale:
#> [1] LC_COLLATE=English_United States.1252
#> [2] LC_CTYPE=English_United States.1252
#> [3] LC_MONETARY=English_United States.1252
#> [4] LC_NUMERIC=C
#> [5] LC_TIME=English_United States.1252
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] NetPhorce_1.0.0 DT_0.27
#>
#> loaded via a namespace (and not attached):
#> [1] bitops_1.0-7 matrixStats_0.63.0
#> [3] fs_1.6.1 httr_1.4.5
#> [5] rprojroot_2.0.3 GenomeInfoDb_1.28.4
#> [7] dynamicTreeCut_1.63-1 tools_4.1.0
#> [9] backports_1.4.1 bslib_0.4.2
#> [11] affyio_1.62.0 utf8_1.2.3
#> [13] R6_2.5.1 DBI_1.1.3
#> [15] BiocGenerics_0.38.0 lazyeval_0.2.2
#> [17] colorspace_2.1-0 withr_2.5.0
#> [19] tidyselect_1.2.0 curl_5.0.0
#> [21] preprocessCore_1.54.0 compiler_4.1.0
#> [23] textshaping_0.3.6 cli_3.6.0
#> [25] Biobase_2.52.0 desc_1.4.2
#> [27] DelayedArray_0.18.0 plotly_4.10.1
#> [29] sass_0.4.5 scales_1.2.1
#> [31] affy_1.70.0 pkgdown_2.0.7
#> [33] systemfonts_1.0.4 stringr_1.5.0
#> [35] digest_0.6.31 rmarkdown_2.20
#> [37] XVector_0.32.0 pkgconfig_2.0.3
#> [39] htmltools_0.5.4 MatrixGenerics_1.4.3
#> [41] limma_3.48.3 fastmap_1.1.1
#> [43] htmlwidgets_1.6.1 rlang_1.0.6
#> [45] rstudioapi_0.14 visNetwork_2.1.2
#> [47] jquerylib_0.1.4 generics_0.1.3
#> [49] jsonlite_1.8.4 crosstalk_1.2.0
#> [51] dplyr_1.1.0 car_3.1-1
#> [53] RCurl_1.98-1.10 magrittr_2.0.3
#> [55] GenomeInfoDbData_1.2.6 Matrix_1.5-3
#> [57] Rcpp_1.0.10 munsell_0.5.0
#> [59] S4Vectors_0.30.2 fansi_1.0.4
#> [61] abind_1.4-5 vsn_3.60.0
#> [63] lifecycle_1.0.3 stringi_1.7.12
#> [65] yaml_2.3.7 carData_3.0-5
#> [67] SummarizedExperiment_1.22.0 zlibbioc_1.38.0
#> [69] Rtsne_0.16 plyr_1.8.8
#> [71] qvalue_2.24.0 grid_4.1.0
#> [73] randomcoloR_1.1.0.1 parallel_4.1.0
#> [75] forcats_1.0.0 lattice_0.20-44
#> [77] splines_4.1.0 knitr_1.42
#> [79] pillar_1.8.1 igraph_1.4.1
#> [81] ggpubr_0.6.0 GenomicRanges_1.44.0
#> [83] ggsignif_0.6.4 reshape2_1.4.4
#> [85] stats4_4.1.0 glue_1.6.2
#> [87] evaluate_0.20 V8_4.2.2
#> [89] BiocManager_1.30.20 data.table_1.14.8
#> [91] vctrs_0.5.2 gtable_0.3.1
#> [93] purrr_1.0.1 tidyr_1.3.0
#> [95] assertthat_0.2.1 cachem_1.0.7
#> [97] ggplot2_3.4.1 xfun_0.37
#> [99] broom_1.0.4 rstatix_0.7.2
#> [101] ragg_1.2.5 viridisLite_0.4.1
#> [103] tibble_3.2.0 memoise_2.0.1
#> [105] IRanges_2.26.0 ellipse_0.4.3
#> [107] cluster_2.1.2 ellipsis_0.3.2