Mercurial > repos > bgruening > music_manipulate_eset
annotate scripts/compare.R @ 4:b5185a4f5209 draft default tip
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit 7b4e1e85d9d288a904444eb9fcb96bcdc856b9ff
author | bgruening |
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date | Wed, 06 Nov 2024 23:21:49 +0000 |
parents | 22232092be53 |
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rev | line source |
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22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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1 suppressWarnings(suppressPackageStartupMessages(library(xbioc))) |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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2 suppressWarnings(suppressPackageStartupMessages(library(MuSiC))) |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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3 suppressWarnings(suppressPackageStartupMessages(library(reshape2))) |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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4 suppressWarnings(suppressPackageStartupMessages(library(cowplot))) |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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5 ## We use this script to estimate the effectiveness of proportion methods |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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6 |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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7 ## Load Conf |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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8 args <- commandArgs(trailingOnly = TRUE) |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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9 source(args[1]) |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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10 |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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11 method_key <- list("MuSiC" = "est_music", |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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12 "NNLS" = "est_nnls")[[est_method]] |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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13 |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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14 delim <- "::" ## separator bulk datasets and their samples |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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15 |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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16 scale_yaxes <- function(gplot, value) { |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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17 if (is.na(value)) { |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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18 gplot |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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19 } else { |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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20 gplot + scale_y_continuous(lim = c(0, value)) |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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21 } |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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22 } |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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23 |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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24 |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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25 set_factor_data <- function(bulk_data, factor_name = NULL) { |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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26 if (is.null(factor_name)) { |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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27 factor_name <- "None" ## change to something plottable |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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28 } |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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29 pdat <- pData(bulk_data) |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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30 sam_fact <- NULL |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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31 if (factor_name %in% colnames(pdat)) { |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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32 sam_fact <- cbind(rownames(pdat), |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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33 as.character(pdat[[factor_name]])) |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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34 cat(paste0(" - factor: ", factor_name, |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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35 " found in phenotypes\n")) |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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36 } else { |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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37 ## We assign this as the factor for the entire dataset |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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38 sam_fact <- cbind(rownames(pdat), |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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39 factor_name) |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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40 cat(paste0(" - factor: assigning \"", factor_name, |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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41 "\" to whole dataset\n")) |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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42 } |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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43 colnames(sam_fact) <- c("Samples", "Factors") |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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44 return(as.data.frame(sam_fact)) |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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45 } |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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46 |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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47 ## Due to limiting sizes, we need to load and unload |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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48 ## possibly very large datasets. |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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49 process_pair <- function(sc_data, bulk_data, |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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50 ctypes_label, samples_label, ctypes, |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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51 factor_group) { |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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52 ## - Generate |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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53 est_prop <- music_prop( |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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54 bulk.eset = bulk_data, sc.eset = sc_data, |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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55 clusters = ctypes_label, |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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56 samples = samples_label, select.ct = ctypes, verbose = T) |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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57 ## - |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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58 estimated_music_props <- est_prop$Est.prop.weighted |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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59 estimated_nnls_props <- est_prop$Est.prop.allgene |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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60 ## - |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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61 fact_data <- set_factor_data(bulk_data, factor_group) |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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62 ## - |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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63 return(list(est_music = estimated_music_props, |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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64 est_nnls = estimated_nnls_props, |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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65 bulk_sample_totals = colSums(exprs(bulk_data)), |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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66 plot_groups = fact_data)) |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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67 } |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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68 |
22232092be53
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69 music_on_all <- function(files) { |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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70 results <- list() |
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"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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71 for (sc_name in names(files)) { |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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72 cat(paste0("sc-group:", sc_name, "\n")) |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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73 scgroup <- files[[sc_name]] |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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74 ## - sc Data |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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75 sc_est <- readRDS(scgroup$dataset) |
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"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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76 ## - params |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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77 celltypes_label <- scgroup$label_cell |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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78 samples_label <- scgroup$label_sample |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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79 celltypes <- scgroup$celltype |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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80 |
22232092be53
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81 results[[sc_name]] <- list() |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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82 for (bulk_name in names(scgroup$bulk)) { |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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83 cat(paste0(" - bulk-group:", bulk_name, "\n")) |
22232092be53
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"
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84 bulkgroup <- scgroup$bulk[[bulk_name]] |
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85 ## - bulk Data |
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86 bulk_est <- readRDS(bulkgroup$dataset) |
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87 ## - bulk params |
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88 pheno_facts <- bulkgroup$pheno_facts |
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89 pheno_excl <- bulkgroup$pheno_excl |
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90 ## |
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91 results[[sc_name]][[bulk_name]] <- process_pair( |
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92 sc_est, bulk_est, |
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93 celltypes_label, samples_label, |
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94 celltypes, bulkgroup$factor_group) |
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95 ## |
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96 rm(bulk_est) ## unload |
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97 } |
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98 rm(sc_est) ## unload |
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99 } |
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100 return(results) |
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101 } |
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102 |
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103 plot_all_individual_heatmaps <- function(results) { |
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104 pdf(out_heatmulti_pdf, width = 8, height = 8) |
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105 for (sc_name in names(results)) { |
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106 for (bk_name in names(results[[sc_name]])) { |
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107 res <- results[[sc_name]][[bk_name]] |
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108 plot_hmap <- Prop_heat_Est( |
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109 data.matrix(res[[method_key]]), method.name = est_method) + |
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110 ggtitle(paste0("[", est_method, "Cell type ", |
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111 "proportions in ", |
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112 bk_name, " (Bulk) based on ", |
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113 sc_name, " (scRNA)")) + |
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114 xlab("Cell Types (scRNA)") + |
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115 ylab("Samples (Bulk)") + |
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116 theme(axis.text.x = element_text(angle = -90), |
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117 axis.text.y = element_text(size = 6)) |
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118 print(plot_hmap) |
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119 } |
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120 } |
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121 dev.off() |
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122 } |
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123 |
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124 merge_factors_spread <- function(grudat_spread, factor_groups) { |
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125 ## Generated |
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126 merge_it <- function(matr, plot_groups, valname) { |
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127 ren <- melt(lapply(matr, function(mat) { |
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128 mat["ct"] <- rownames(mat); return(mat)})) |
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129 ## - Grab factors and merge into list |
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130 ren_new <- merge(ren, plot_groups, by.x = "variable", by.y = "Samples") |
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131 colnames(ren_new) <- c("Sample", "Cell", valname, "Bulk", "Factors") |
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132 return(ren_new) |
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133 } |
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134 tab <- merge(merge_it(grudat$spread$prop, factor_groups, "value.prop"), |
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135 merge_it(grudat$spread$scale, factor_groups, "value.scale"), |
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136 by = c("Sample", "Cell", "Bulk", "Factors")) |
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137 return(tab) |
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138 } |
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139 |
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140 unlist_names <- function(results, method, prepend_bkname=FALSE) { |
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141 unique(sort( |
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142 unlist(lapply(names(results), function(scname) { |
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143 lapply(names(results[[scname]]), function(bkname) { |
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144 res <- get(method)(results[[scname]][[bkname]][[method_key]]) |
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145 if (prepend_bkname) { |
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146 ## We *do not* assume unique bulk sample names |
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147 ## across different bulk datasets. |
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148 res <- paste0(bkname, delim, res) |
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149 } |
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150 return(res) |
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151 }) |
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152 })) |
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153 )) |
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154 } |
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155 |
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156 summarized_matrix <- function(results) { # nolint |
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157 ## We assume that cell types MUST be unique, but that sample |
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158 ## names do not need to be. For this reason, we must prepend |
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159 ## the bulk dataset name to the individual sample names. |
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160 all_celltypes <- unlist_names(results, "colnames") |
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161 all_samples <- unlist_names(results, "rownames", prepend_bkname = TRUE) |
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162 |
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163 ## Iterate through all possible samples and populate a table. |
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164 ddff <- data.frame() |
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165 ddff_scale <- data.frame() |
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166 for (cell in all_celltypes) { |
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167 for (sample in all_samples) { |
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168 group_sname <- unlist(strsplit(sample, split = delim)) |
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169 bulk <- group_sname[1] |
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170 id_sample <- group_sname[2] |
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171 for (scgroup in names(results)) { |
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172 if (bulk %in% names(results[[scgroup]])) { |
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173 mat_prop <- results[[scgroup]][[bulk]][[method_key]] |
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174 vec_counts <- results[[scgroup]][[bulk]]$bulk_sample_totals |
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175 ## - We use sample instead of id_sample because we need to |
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176 ## extract bulk sets from the complete matrix later. It's |
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177 ## messy, yes. |
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178 if (cell %in% colnames(mat_prop)) { |
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179 ddff[cell, sample] <- mat_prop[id_sample, cell] |
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180 ddff_scale[cell, sample] <- mat_prop[id_sample, cell] * vec_counts[[id_sample]] #nolint |
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181 } else { |
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182 ddff[cell, sample] <- 0 |
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183 ddff_scale[cell, sample] <- 0 |
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184 } |
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185 } |
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186 } |
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187 } |
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188 } |
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189 return(list(prop = ddff, scaled = ddff_scale)) |
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190 } |
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191 |
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192 flatten_factor_list <- function(results) { |
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193 ## Get a 2d DF of all factors across all bulk samples. |
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194 res <- c() |
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195 for (scgroup in names(results)) { |
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196 for (bulkgroup in names(results[[scgroup]])) { |
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197 dat <- results[[scgroup]][[bulkgroup]]$plot_groups |
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198 dat$Samples <- paste0(bulkgroup, delim, dat$Samples) #nolint |
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199 res <- rbind(res, dat) |
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200 } |
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201 } |
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202 return(res) |
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203 } |
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204 |
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205 group_by_dataset <- function(summat) { |
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206 bulk_names <- unlist( |
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207 lapply(names(files), function(x) names(files[[x]]$bulk))) |
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208 mat_names <- colnames(summat$prop) |
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209 bd <- list() |
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210 bd_scale <- list() |
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211 bd_spread_scale <- list() |
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212 bd_spread_prop <- list() |
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213 for (bname in bulk_names) { |
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214 subs <- mat_names[startsWith(mat_names, paste0(bname, delim))] |
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215 ## - |
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216 bd[[bname]] <- rowSums(summat$prop[, subs]) |
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217 bd_scale[[bname]] <- rowSums(summat$scaled[, subs]) |
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218 bd_spread_scale[[bname]] <- summat$scaled[, subs] |
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219 bd_spread_prop[[bname]] <- summat$prop[, subs] |
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220 } |
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221 return(list(prop = as.data.frame(bd), |
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222 scaled = as.data.frame(bd_scale), |
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223 spread = list(scale = bd_spread_scale, |
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224 prop = bd_spread_prop))) |
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225 } |
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226 |
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227 do_cluster <- function(grudat_spread_melt, xaxis, yaxis, value_name, |
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228 xlabs="", ylabs="", titled="", |
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229 order_col=T, order_row=T, size=11) { |
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230 |
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231 data_m <- grudat_spread_melt |
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232 data_matrix <- { |
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233 tmp <- dcast(data_m, formula(paste0(yaxis, " ~ ", xaxis)), value.var = value_name) |
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234 rownames(tmp) <- tmp[[yaxis]] |
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235 tmp[[yaxis]] <- NULL |
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236 tmp |
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237 } |
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238 dist_method <- "euclidean" |
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239 clust_method <- "complete" |
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240 |
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241 if (order_row) { |
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242 dd_row <- as.dendrogram(hclust(dist(data_matrix, method = dist_method), method = clust_method)) |
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243 row_ord <- order.dendrogram(dd_row) |
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244 ordered_row_names <- row.names(data_matrix[row_ord, ]) |
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245 data_m[[yaxis]] <- factor(data_m[[yaxis]], levels = ordered_row_names) |
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246 } |
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247 |
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248 if (order_col) { |
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249 dd_col <- as.dendrogram(hclust(dist(t(data_matrix), method = dist_method), |
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250 method = clust_method)) |
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251 col_ord <- order.dendrogram(dd_col) |
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252 ordered_col_names <- colnames(data_matrix[, col_ord]) |
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253 data_m[[xaxis]] <- factor(data_m[[xaxis]], levels = ordered_col_names) |
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254 } |
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255 |
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256 heat_plot <- ggplot(data_m, aes_string(x = xaxis, y = yaxis, fill = value_name)) + |
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257 geom_tile(colour = "white") + |
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258 scale_fill_gradient2(low = "steelblue", high = "red", mid = "white", |
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259 name = element_blank()) + |
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260 scale_y_discrete(position = "right") + |
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261 theme(axis.text.x = element_text(angle = -90, hjust = 0, |
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262 size = size)) + |
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263 ggtitle(label = titled) + xlab(xlabs) + ylab(ylabs) |
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264 |
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265 ## Graphics |
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266 dendro_linesize <- 0.5 |
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267 dendro_colunit <- 0.2 |
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268 dendro_rowunit <- 0.1 |
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269 final_plot <- heat_plot |
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270 |
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271 if (order_row) { |
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272 dendro_data_row <- ggdendro::dendro_data(dd_row, type = "rectangle") |
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273 dendro_row <- cowplot::axis_canvas(heat_plot, axis = "y", coord_flip = TRUE) + |
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274 ggplot2::geom_segment(data = ggdendro::segment(dendro_data_row), |
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275 ggplot2::aes(y = -y, x = x, xend = xend, yend = -yend), |
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276 size = dendro_linesize) + ggplot2::coord_flip() |
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277 final_plot <- cowplot::insert_yaxis_grob( |
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278 final_plot, dendro_row, grid::unit(dendro_colunit, "null"), |
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279 position = "left") |
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280 } |
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281 if (order_col) { |
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282 dendro_data_col <- ggdendro::dendro_data(dd_col, type = "rectangle") |
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283 dendro_col <- cowplot::axis_canvas(heat_plot, axis = "x") + |
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284 ggplot2::geom_segment(data = ggdendro::segment(dendro_data_col), |
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285 ggplot2::aes(x = x, y = y, xend = xend, yend = yend), |
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286 size = dendro_linesize) |
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287 final_plot <- cowplot::insert_xaxis_grob( |
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288 final_plot, dendro_col, grid::unit(dendro_rowunit, "null"), |
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289 position = "top") |
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290 } |
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291 return(cowplot::ggdraw(final_plot)) |
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292 } |
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293 |
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294 summarize_heatmaps <- function(grudat_spread_melt, do_factors, cluster="None") { |
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295 ## - Cluster is either "Rows", "Cols", "Both", or "None" |
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296 do_single <- function(grudat_melted, yaxis, xaxis, fillval, title, |
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297 ylabs = element_blank(), xlabs = element_blank(), |
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298 use_log = TRUE, size = 11) { |
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299 ## Convert from matrix to long format |
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300 melted <- grudat_melted ## copy? |
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301 if (use_log) { |
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302 melted[[fillval]] <- log10(melted[[fillval]] + 1) |
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303 } |
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304 if (cluster == "None") { |
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305 return(ggplot(melted) + |
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306 geom_tile(aes_string(y = yaxis, x = xaxis, fill = fillval), |
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307 colour = "white") + |
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308 scale_fill_gradient2( |
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309 low = "steelblue", high = "red", mid = "white", |
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310 name = element_blank()) + |
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311 theme(axis.text.x = element_text( |
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312 angle = -90, hjust = 0, size = size)) + |
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313 ggtitle(label = title) + xlab(xlabs) + ylab(ylabs)) |
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314 } else { |
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315 return(do_cluster(grudat_spread_melt, xaxis, yaxis, fillval, |
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316 xlabs, ylabs, title, |
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317 (cluster %in% c("Cols", "Both")), |
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318 (cluster %in% c("Rows", "Both")))) |
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319 } |
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320 } |
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321 |
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322 do_gridplot <- function(title, xvar, plot="both", ncol=2, size = 11) { |
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323 do_logged <- (plot %in% c("log", "both")) |
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324 do_normal <- (plot %in% c("normal", "both")) |
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325 plist <- list() |
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326 if (do_logged) { |
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327 plist[["1"]] <- do_single(grudat_spread_melt, "Cell", xvar, |
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328 "value.scale", "Reads (log10+1)", |
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329 size = size) |
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330 plist[["2"]] <- do_single(grudat_spread_melt, "Cell", xvar, |
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331 "value.prop", "Sample (log10+1)", |
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332 size = size) |
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333 } |
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334 if (do_normal) { |
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335 plist[["A"]] <- do_single(grudat_spread_melt, "Cell", xvar, |
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336 "value.scale", "Reads", use_log = F, |
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337 size = size) |
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338 plist[["B"]] <- do_single(grudat_spread_melt, "Cell", xvar, |
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339 "value.prop", "Sample", use_log = F, |
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340 size = size) |
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341 } |
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342 return(plot_grid(ggdraw() + draw_label(title, fontface = "bold"), |
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343 plot_grid(plotlist = plist, ncol = ncol), |
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344 ncol = 1, rel_heights = c(0.05, 0.95))) |
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345 } |
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346 |
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347 p1 <- do_gridplot("Cell Types vs Bulk Datasets", "Bulk", "both") |
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348 p2a <- do_gridplot("Cell Types vs Samples", "Sample", "normal", |
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349 ncol = 1, size = 8) |
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350 p2b <- do_gridplot("Cell Types vs Samples (log10+1)", "Sample", "log", |
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351 ncol = 1, size = 8) |
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352 p3 <- ggplot + theme_void() |
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353 if (do_factors) { |
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354 p3 <- do_gridplot("Cell Types vs Factors", "Factors", "both") |
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355 } |
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356 return(list(bulk = p1, |
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357 samples = list(log = p2b, normal = p2a), |
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358 factors = p3)) |
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359 } |
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360 |
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361 summarize_boxplots <- function(grudat_spread, do_factors) { |
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362 common1 <- ggplot(grudat_spread, aes(x = value.prop)) + ggtitle("Sample") + |
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363 xlab(element_blank()) + ylab(element_blank()) |
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364 common2 <- ggplot(grudat_spread, aes(x = value.scale)) + ggtitle("Reads") + |
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365 xlab(element_blank()) + ylab(element_blank()) |
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366 |
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367 A <- B <- list() #nolint |
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368 ## Cell type by sample |
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369 A$p1 <- common2 + geom_boxplot(aes(y = Cell, color = Bulk)) |
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370 A$p2 <- common1 + geom_boxplot(aes(y = Cell, color = Bulk)) |
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371 ## Sample by Cell type |
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372 B$p1 <- common2 + geom_boxplot(aes(y = Bulk, color = Cell)) + |
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373 ylab("Bulk Dataset") |
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374 B$p2 <- common1 + geom_boxplot(aes(y = Bulk, color = Cell)) + |
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375 ylab("Bulk Dataset") |
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376 ## -- Factor plots are optional |
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377 A$p3 <- B$p3 <- A$p4 <- B$p4 <- ggplot() + theme_void() |
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378 |
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379 if (do_factors) { |
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380 A$p3 <- common1 + geom_boxplot(aes(y = Cell, color = Factors)) |
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381 A$p4 <- common2 + geom_boxplot(aes(y = Cell, color = Factors)) |
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382 B$p3 <- common1 + geom_boxplot(aes(y = Bulk, color = Factors)) + |
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383 ylab("Bulk Dataset") |
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384 B$p4 <- common2 + geom_boxplot(aes(y = Bulk, color = Factors)) + |
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385 ylab("Bulk Dataset") |
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386 } |
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387 |
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388 title_a <- "Cell Types vs Bulk Datasets" |
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389 title_b <- "Bulk Datasets vs Cell Types" |
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390 if (do_factors) { |
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391 title_a <- paste0(title_a, " and Factors") |
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392 title_b <- paste0(title_b, " and Factors") |
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393 } |
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394 |
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395 a_all <- plot_grid(ggdraw() + draw_label(title_a, fontface = "bold"), |
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396 plot_grid(plotlist = A, ncol = 2), |
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397 ncol = 1, rel_heights = c(0.05, 0.95)) |
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398 b_all <- plot_grid(ggdraw() + draw_label(title_b, fontface = "bold"), |
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399 plot_grid(plotlist = B, ncol = 2), |
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400 ncol = 1, rel_heights = c(0.05, 0.95)) |
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401 return(list(cell = a_all, bulk = b_all)) |
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402 } |
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403 |
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404 filter_output <- function(grudat_spread_melt, out_filt) { |
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405 print_red <- function(comment, red_list) { |
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406 cat(paste(comment, paste(red_list, collapse = ", "), "\n")) |
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407 } |
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408 grudat_filt <- grudat_spread_melt |
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409 print_red("Total Cell types:", unique(grudat_filt$Cell)) |
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410 if (!is.null(out_filt$cells)) { |
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411 grudat_filt <- grudat_filt[grudat_filt$Cell %in% out_filt$cells, ] |
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412 print_red(" - selecting:", out_filt$cells) |
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413 } |
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414 print_red("Total Factors:", unique(grudat_spread_melt$Factors)) |
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415 if (!is.null(out_filt$facts)) { |
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416 grudat_filt <- grudat_filt[grudat_filt$Factors %in% out_filt$facts, ] |
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417 print_red(" - selecting:", out_filt$facts) |
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418 } |
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419 return(grudat_filt) |
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420 } |
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421 |
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422 writable2 <- function(obj, prefix, title) { |
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423 write.table(obj, |
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424 file = paste0("report_data/", prefix, "_", |
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425 title, ".tabular"), |
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426 quote = F, sep = "\t", col.names = NA) |
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427 } |
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428 |
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429 |
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430 results <- music_on_all(files) |
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431 summat <- summarized_matrix(results) |
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432 grudat <- group_by_dataset(summat) |
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433 grudat_spread_melt <- merge_factors_spread(grudat$spread, |
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434 flatten_factor_list(results)) |
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435 grudat_spread_melt_filt <- filter_output(grudat_spread_melt, out_filt) |
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436 |
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437 plot_all_individual_heatmaps(results) |
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438 |
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439 ## The output filters ONLY apply to boxplots, since these take |
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440 do_factors <- (length(unique(grudat_spread_melt[["Factors"]])) > 1) |
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441 box_plots <- summarize_boxplots(grudat_spread_melt_filt, do_factors) |
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442 heat_maps <- summarize_heatmaps(grudat_spread_melt_filt, do_factors, |
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443 dendro_setting) |
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444 |
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445 pdf(out_heatsumm_pdf, width = 14, height = 14) |
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446 print(heat_maps) |
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447 print(box_plots) |
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448 dev.off() |
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449 |
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450 ## Generate output tables |
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451 stats_prop <- lapply(grudat$spread$prop, function(x) { |
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452 t(apply(x, 1, summary))}) |
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453 stats_scale <- lapply(grudat$spread$scale, function(x) { |
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454 t(apply(x, 1, summary))}) |
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455 |
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456 ## Make the value table printable |
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457 grudat_spread_melt$value.scale <- as.integer(grudat_spread_melt$value.scale) # nolint |
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458 colnames(grudat_spread_melt) <- c("Sample", "Cell", "Bulk", "Factors", |
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459 "CT Prop in Sample", "Number of Reads") |
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460 |
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461 writable2(grudat_spread_melt, "values", "Data Table") |
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462 writable2(summat$prop, "values", "Matrix of Cell Type Sample Proportions") |
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463 writable2({ |
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464 aa <- as.matrix(summat$scaled); mode(aa) <- "integer"; aa |
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465 }, "values", "Matrix of Cell Type Read Counts") |
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466 |
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467 for (bname in names(stats_prop)) { |
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468 writable2(stats_prop[[bname]], "stats", paste0(bname, ": Sample Props")) |
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469 writable2(stats_scale[[bname]], "stats", paste0(bname, ": Read Props")) |
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470 } |