Mercurial > repos > bgruening > music_deconvolution

annotate scripts/estimateprops.R @ 0:224721e76869 draft

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"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit 08c6fd3885bdfbf8b5c3f4dcc2d04729b577e3e1"
author bgruening
date Sun, 12 Sep 2021 19:48:48 +0000
parents
children 3ca0132c182a
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224721e76869 "planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit 08c6fd3885bdfbf8b5c3f4dcc2d04729b577e3e1"
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1 suppressWarnings(suppressPackageStartupMessages(library(xbioc)))
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2 suppressWarnings(suppressPackageStartupMessages(library(MuSiC)))
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3 suppressWarnings(suppressPackageStartupMessages(library(reshape2)))
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4 suppressWarnings(suppressPackageStartupMessages(library(cowplot)))
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5 ## We use this script to estimate the effectiveness of proportion methods
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7 ## Load Conf
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8 args <- commandArgs(trailingOnly = TRUE)
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9 source(args[1])
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11 ## Estimate cell type proportions
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12 est_prop <- music_prop(
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13 bulk.eset = bulk_eset, sc.eset = scrna_eset,
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14 clusters = celltypes_label,
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15 samples = samples_label, select.ct = celltypes, verbose = T)
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18 ## Show different in estimation methods
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19 ## Jitter plot of estimated cell type proportions
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20 jitter.fig <- Jitter_Est(
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21 list(data.matrix(est_prop$Est.prop.weighted),
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22 data.matrix(est_prop$Est.prop.allgene)),
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23 method.name = methods, title = "Jitter plot of Est Proportions")
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26 ## Make a Plot
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27 ## A more sophisticated jitter plot is provided as below. We separated
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28 ## the T2D subjects and normal subjects by their HbA1c levels.
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29 m_prop <- rbind(melt(est_prop$Est.prop.weighted),
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30 melt(est_prop$Est.prop.allgene))
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32 colnames(m_prop) <- c("Sub", "CellType", "Prop")
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34 m_prop$CellType <- factor(m_prop$CellType, levels = celltypes) # nolint
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35 m_prop$Method <- factor(rep(methods, each = 89 * 6), levels = methods) # nolint
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36 m_prop$HbA1c <- rep(bulk_eset$hba1c, 2 * 6) # nolint
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37 m_prop <- m_prop[!is.na(m_prop$HbA1c), ]
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38 m_prop$Disease <- factor(sample_groups[(m_prop$HbA1c > 6.5) + 1], # nolint
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39 levels = sample_groups)
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41 m_prop$D <- (m_prop$Disease == # nolint
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42 sample_disease_group) / sample_disease_group_scale
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43 m_prop <- rbind(subset(m_prop, Disease == healthy_phenotype),
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44 subset(m_prop, Disease != healthy_phenotype))
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45
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46 jitter.new <- ggplot(m_prop, aes(Method, Prop)) +
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47 geom_point(aes(fill = Method, color = Disease, stroke = D, shape = Disease),
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48 size = 2, alpha = 0.7,
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49 position = position_jitter(width = 0.25, height = 0)) +
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50 facet_wrap(~ CellType, scales = "free") +
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51 scale_colour_manual(values = c("white", "gray20")) +
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52 scale_shape_manual(values = c(21, 24)) + theme_minimal()
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53
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54 ## Plot to compare method effectiveness
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55 ## Create dataframe for beta cell proportions and HbA1c levels
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56 m_prop_ana <- data.frame(pData(bulk_eset)[rep(1:89, 2), phenotype_factors],
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57 ct.prop = c(est_prop$Est.prop.weighted[, 2],
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58 est_prop$Est.prop.allgene[, 2]),
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59 Method = factor(rep(methods, each = 89),
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60 levels = methods))
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61 colnames(m_prop_ana)[1:4] <- phenotype_factors
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62 m_prop_ana <- subset(m_prop_ana, !is.na(m_prop_ana[phenotype_gene]))
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63 m_prop_ana$Disease <- factor(sample_groups[( # nolint
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64 m_prop_ana[phenotype_gene] > 6.5) + 1], sample_groups)
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65 m_prop_ana$D <- (m_prop_ana$Disease == # nolint
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66 sample_disease_group) / sample_disease_group_scale
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67
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68 jitt_compare <- ggplot(m_prop_ana, aes_string(phenotype_gene, "ct.prop")) +
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69 geom_smooth(method = "lm", se = FALSE, col = "black", lwd = 0.25) +
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70 geom_point(aes(fill = Method, color = Disease, stroke = D, shape = Disease),
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71 size = 2, alpha = 0.7) + facet_wrap(~ Method) +
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72 ggtitle(compare_title) + theme_minimal() +
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73 scale_colour_manual(values = c("white", "gray20")) +
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74 scale_shape_manual(values = c(21, 24))
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75
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76
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77 pdf(file = outfile_pdf, width = 8, height = 8)
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78 plot_grid(jitter.fig, jitter.new, labels = "auto", ncol = 1, nrow = 2)
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79 jitt_compare
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80 dev.off()
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81
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82 ## Summary table
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83 for (meth in methods) {
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84 ##lm_beta_meth = lm(ct.prop ~ age + bmi + hba1c + gender, data =
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85 ##subset(m_prop_ana, Method == meth))
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86 lm_beta_meth <- lm(as.formula(
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87 paste("ct.prop", paste(phenotype_factors, collapse = " + "),
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88 sep = " ~ ")),
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89 data = subset(m_prop_ana, Method == meth))
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90 print(paste0("Summary: ", meth))
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91 capture.output(summary(lm_beta_meth),
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92 file = paste0("report_data/summ_", meth, ".txt"))
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93 }