## ----style, echo = FALSE, results = 'asis'------------------------------------ BiocStyle::markdown() ## ----env, include=FALSE, echo=FALSE, cache=FALSE------------------------------ library("knitr") opts_chunk$set(stop_on_error = 1L) suppressPackageStartupMessages(library("MetNet")) ## ----install, eval=FALSE------------------------------------------------------ # if (!requireNamespace("BiocManager", quietly = TRUE)) # install.packages("BiocManager") # # BiocManager::install("MetNet") ## ----load_MetNet,eval=TRUE---------------------------------------------------- library(MetNet) ## ----data,eval=TRUE,echo=TRUE------------------------------------------------- data("x_test", package = "MetNet") x_test <- as.matrix(x_test) ## ----transformation_example,echo=TRUE,eval=TRUE------------------------------- ## define the search space for biochemical transformation transformations <- rbind( c("Hydroxylation (-H)", "O", 15.9949146221, "-"), c("Malonyl group (-H2O)", "C3H2O3", 86.0003939305, "+"), c("D-ribose (-H2O) (ribosylation)", "C5H8O4", 132.0422587452, "-"), c("C6H10O6", "C6H10O6", 178.0477380536, "-"), c("Rhamnose (-H20)", "C6H10O4", 146.057910, "-"), c("Monosaccharide (-H2O)", "C6H10O5", 162.0528234315, "-"), c("Disaccharide (-H2O) #1", "C12H20O10", 324.105649, "-"), c("Disaccharide (-H2O) #2", "C12H20O11", 340.1005614851, "-"), c("Trisaccharide (-H2O)", "C18H30O15", 486.1584702945, "-"), c("Glucuronic acid (-H2O)", "C6H8O6", 176.0320879894, "?"), c("coumaroyl (-H2O)", "C9H6O2", 146.0367794368, "?"), c("feruloyl (-H2O)", "C9H6O2OCH2", 176.0473441231, "?"), c("sinapoyl (-H2O)", "C9H6O2OCH2OCH2", 206.0579088094, "?"), c("putrescine to spermidine (+C3H7N)", "C3H7N", 57.0578492299, "?")) ## convert to data frame transformations <- data.frame( group = transformations[, 1], formula = transformations[, 2], mass = as.numeric(transformations[, 3]), rt = transformations[, 4]) ## ----structure, eval=TRUE,echo=TRUE------------------------------------------- struct_adj <- structural(x = x_test, transformation = transformations, var = c("group", "formula", "mass"), ppm = 10) ## ----structure_dir, eval=TRUE,echo=TRUE--------------------------------------- struct_adj_dir <- structural(x = x_test, transformation = transformations, var = c("group", "formula", "mass"), ppm = 10, directed = TRUE) ## ----visualization_directed--------------------------------------------------- g_undirected <- igraph::graph_from_adjacency_matrix( assay(struct_adj, "binary"), mode = "directed", weighted = NULL) plot(g_undirected, edge.width = 1, edge.arrow.size = 0.5, vertex.label.cex = 0.5, edge.color = "grey") g_directed <- igraph::graph_from_adjacency_matrix( assay(struct_adj_dir, "binary"), mode = "directed", weighted = NULL) plot(g_directed, edge.width = 1, edge.arrow.size = 0.5, vertex.label.cex = 0.5, edge.color = "grey") ## ----rt_correction, eval=TRUE, echo=TRUE-------------------------------------- struct_adj <- rtCorrection(am = struct_adj, x = x_test, transformation = transformations, var = "group") ## ----eval=TRUE,echo=TRUE------------------------------------------------------ struct_df <- as.data.frame(struct_adj) struct_df <- struct_df[struct_df$binary == 1, ] ## ----mz_summary,eval=TRUE,echo=TRUE------------------------------------------- mz_sum <- mz_summary(struct_adj, var = "group") mz_vis(mz_sum, var = "group") ## ----mz_summary_filter-------------------------------------------------------- mz_summary(struct_adj, filter = 4) mz_summary(struct_adj, filter = 5) ## ----eval=TRUE,echo=TRUE------------------------------------------------------ data("x_annotation", package = "MetNet") x_annotation <- as.data.frame(x_annotation) ## add annotations to the structural AdjacencyMatrix object rowData(struct_adj) <- x_annotation ## display annotation for the feature "1856" rowData(struct_adj)["x1856", ] ## ----spectral,eval = TRUE, echo = TRUE---------------------------------------- # required for ndotproduct calculus library(MsCoreUtils) library(Spectra) ## create spectral similarity matrix f <- system.file("spectra_matrix/ms2_test.RDS", package = "MetNet") sps_sub <- readRDS(f) adj_spec <- Spectra::compareSpectra(sps_sub, FUN = ndotproduct) colnames(adj_spec) <- sps_sub$id rownames(adj_spec) <- sps_sub$id spect_adj <- addSpectralSimilarity(am_structural = struct_adj, ms2_similarity = list("ndotproduct" = adj_spec)) ## ----threshold structural,eval=TRUE,echo=TRUE--------------------------------- ## the assayNames in spect_adj are used to define the filter criteria assayNames(spect_adj) ## return edges with normalized dotproduct > 0.10 args_thr <- list(filter = "ndotproduct > 0.1") ## return edges with normalized dotproduct > 0.10, even if no mass-difference ## was detected between pairs of features args_thr <- list(filter = "ndotproduct > 0.1 | binary == 1 & is.na(ndotproduct)") ## pass the filtering criteria to the args argument and set type to "threshold" spect_adj_thr <- threshold(am = spect_adj, type = "threshold", args = args_thr) ## ----statistical,eval=TRUE,echo=TRUE------------------------------------------ x_int <- x_test[, 3:ncol(x_test)] |> as.matrix() stat_adj <- statistical(x_int, model = c("pearson", "spearman")) ## ----threshold,eval=TRUE,echo=TRUE-------------------------------------------- ## type = "threshold" ## the assayNames in stat_adj are used to define the filter criteria assayNames(stat_adj) ## return edges with positive Pearson correlation coefficients > 0.95 args_thr <- list(filter = "pearson_coef > 0.95") ## return edges with positive Spearman correlation coefficients > 0.95 args_thr <- list(filter = "spearman_coef > 0.95") ## return edges with absolute Pearson correlation coefficients > 0.95 and ## associated p-values <= 0.05 args_thr <- list(filter = "abs(pearson_coef) > 0.95 & pearson_pvalue <= 0.05") ## return edges with absolute Pearson OR Spearman correlation coefficients > 0.95 args_thr <- list(filter = "abs(pearson_coef) > 0.95 | abs(spearman_coef) > 0.95") ## return edges with absolute Pearson AND Spearman correlation coefficients > 0.95 args_thr <- list(filter = "abs(pearson_coef) > 0.95 & abs(spearman_coef) > 0.95") ## pass the filtering criteria to the args argument and set type to "threshold" stat_adj_thr <- threshold(am = stat_adj, type = "threshold", args = args_thr) ## alternatively, use the types "top1", "top2", "mean" ## retrieve the feature pairs which have the 100 highest coefficients args_top <- list(n = 100) ## type = "top1" stat_adj_top1 <- threshold(am = stat_adj, type = "top1", args = args_top) ## type = "top2" stat_adj_top2 <- threshold(am = stat_adj, type = "top2", args = args_top) ## type = "mean" stat_adj_mean <- threshold(am = stat_adj, type = "mean", args = args_top) ## ----combine,eval=TRUE,echo=TRUE---------------------------------------------- comb_adj <- combine(am_structural = struct_adj, am_statistical = stat_adj_mean) ## ----combine spect,eval=TRUE,echo=TRUE---------------------------------------- comb_spect_adj <- combine(am_structural = spect_adj_thr, am_statistical = stat_adj_mean) ## ----visualisation,eval=TRUE,echo=TRUE,fig.cap='_Ab initio_ network inferred from structural and quantitative mass spectrometry data. Vertices are connected that are separated by given metabolic transformation and statistical association.'---- adj <- assay(comb_adj, "combine_binary") g <- igraph::graph_from_adjacency_matrix(adj, mode = "undirected") plot(g, edge.width = 2, vertex.label.cex = 0.5, edge.color = "grey") ## ----session,eval=TRUE,echo=FALSE--------------------------------------------- sessionInfo() ## ----ttransformations,eval=TRUE,echo=TRUE------------------------------------- transformations <- rbind( c("Alanine", "C3H5NO", "71.0371137878"), c("Arginine", "C6H12N4O", "156.1011110281"), c("Asparagine", "C4H6N2O2", "114.0429274472"), c("Guanosine 5-diphosphate (-H2O)", "C10H13N5O10P2", "425.0137646843"), c("Guanosine 5-monophosphate (-H2O)", "C10H12N5O7P", "345.0474342759"), c("Guanine (-H)", "C5H4N5O", "150.0415847765"), c("Aspartic acid", "C4H5NO3", "115.0269430320"), c("Guanosine (-H2O)", "C10H11N5O4", "265.0811038675"), c("Cysteine", "C3H5NOS", "103.0091844778"), c("Deoxythymidine 5'-diphosphate (-H2O)", "C10H14N2O10P2", "384.01236770"), c("Cystine", "C6H10N2O3S2", "222.0132835777"), c("Thymidine (-H2O)", "C10H12N2O4", "224.0797068840"), c("Glutamic acid", "C5H7NO3", "129.0425930962"), c("Thymine (-H)", "C5H5N2O2", "125.0351024151"), c("Glutamine", "C5H8N2O2", "128.0585775114"), c("Thymidine 5'-monophosphate (-H2O)", "C10H13N2O7P", "304.0460372924"), c("Glycine", "C2H3NO", "57.0214637236"), c("Uridine 5'-diphosphate (-H2O)", "C9H12N2O11P2", "385.9916322587"), c("Histidine", "C6H7N3O", "137.0589118624"), c("Uridine 5'-monophosphate (-H2O)", "C9H11N2O8P", "306.0253018503"), c("Isoleucine", "C6H11NO", "113.0840639804"), c("Uracil (-H)", "C4H3N2O2", "111.0194523509"), c("Leucine", "C6H11NO", "113.0840639804"), c("Uridine (-H2O)", "C9H10N2O5", "226.0589714419"), c("Lysine", "C6H12N2O", "128.0949630177"), c("Acetylation (-H)", "C2H3O2", "59.0133043405"), c("Methionine", "C5H9NOS", "131.0404846062"), c("Acetylation (-H2O)", "C2H2O", "42.0105646863"), c("Phenylalanine", "C9H9NO", "147.0684139162"), c("C2H2", "C2H2", "26.0156500642"), c("Proline", "C5H7NO", "97.0527638520"), c("Carboxylation", "CO2", "43.9898292442"), c("Serine", "C3H5NO2", "87.0320284099"), c("CHO2", "CHO2", "44.9976542763"), c("Threonine", "C4H7NO2", "101.0476784741"), c("Condensation/dehydration", "H2O", "18.0105646863"), c("Tryptophan", "C11H10N2O", "186.0793129535"), c("Diphosphate", "H3O6P2", "160.9404858489"), c("Tyrosine", "C9H9NO2", "163.0633285383"), c("Ethyl addition (-H2O)", "C2H4", "28.0313001284"), c("Valine", "C5H9NO", "99.0684139162"), c("Formic Acid (-H2O)", "CO", "27.9949146221"), c("Acetotacetate (-H2O)", "C4H4O2", "84.0211293726"), c("Glyoxylate (-H2O)", "C2O2", "55.9898292442"), c("Acetone (-H)", "C3H5O", "57.0340397826"), c("Hydrogenation/dehydrogenation", "H2", "2.0156500642"), c("Adenylate (-H2O)", "C10H12N5O6P", "329.0525196538"), c("Hydroxylation (-H)", "O", "15.9949146221"), c("Biotinyl (-H)", "C10H15N2O3S", "243.0803380482"), c("Inorganic phosphate", "P", "30.9737615100"), c("Biotinyl (-H2O)", "C10H14N2O2S", "226.0775983940"), c("Ketol group (-H2O)", "C2H2O", "42.0105646863"), c("Carbamoyl P transfer (-H2PO4)", "CH2ON", "44.0136386915"), c("Methanol (-H2O)", "CH2", "14.0156500642"), c("Co-enzyme A (-H)", "C21H34N7O16P3S", "765.0995583014"), c("Phosphate", "HPO3", "79.9663304084"), c("Co-enzyme A (-H2O)", "C21H33N7O15P3S", "748.0968186472"), c("Primary amine", "NH2", "16.0187240694"), c("Glutathione (-H2O)", "C10H15N3O5S", "289.0732412976"), c("Pyrophosphate", "PP", "61.9475230200"), c("Isoprene addition (-H)", "C5H7", "67.0547752247"), c("Secondary amine", "NH", "15.0108990373"), c("Malonyl group (-H2O)", "C3H2O3", "86.0003939305"), c("Sulfate (-H2O)", "SO3", "79.9568145563"), c("Palmitoylation (-H2O)", "C16H30O", "238.2296655851"), c("Tertiary amine", "N", "14.0030740052"), c("Pyridoxal phosphate (-H2O)", "C8H8NO5P", "229.0140088825"), c("C6H10O5", "C6H10O5", "162.0528234315"), c("Urea addition (-H)", "CH3N2O", "59.0245377288"), c("C6H10O6", "C6H10O6", "178.0477380536"), c("Adenine (-H)", "C5H4N5", "134.0466701544"), c("D-ribose (-H2O) (ribosylation)", "C5H8O4", "132.0422587452"), c("Adenosine (-H2O)", "C10H11N5O3", "249.0861892454"), c("Disaccharide (-H2O) #1", "C12H20O10", "324.105649"), c("Disaccharide (-H2O) #2", "C12H20O11", "340.1005614851"), c("Adenosine 5'-diphosphate (-H2O)", "C10H13N5O9P2", "409.0188500622"), c("Glucose-N-phosphate (-H2O)", "C6H11O8P", "242.0191538399"), c("Adenosine 5'-monophosphate (-H2O)", "C10H12N5O6P", "329.0525196538"), c("Glucuronic acid (-H2O)", "C6H8O6", "176.0320879894"), c("Cytidine 5'-diphosphate (-H2O)", "C9H13N3O10P2", "385.0076166739"), c("Monosaccharide (-H2O)", "C6H10O5", "162.0528234315"), c("Cytidine 5'-monophsophate (-H2O)", "C9H12N3O7P", "305.0412862655"), c("Trisaccharide (-H2O)", "C18H30O15", "486.1584702945"), c("Cytosine (-H)", "C4H4N3O", "110.0354367661")) transformations <- data.frame(group = transformations[, 1], formula = transformations[, 2], mass = as.numeric(transformations[, 3]))