Mercurial > repos > gaelcge > r_signac_galaxy

annotate Signac.R @ 0:6e0b320d8b6a draft default tip

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"planemo upload commit dc808171975d0012e25bd7b32adc7a5a5c56a145-dirty"
author gaelcge
date Tue, 02 Aug 2022 19:11:27 +0000
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1 rm(list = ls())
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2
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3 library(Signac)
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4 library(Seurat)
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5 library(GenomeInfoDb)
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6 library(EnsDb.Hsapiens.v75)
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7 library(ggplot2)
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8 library(patchwork)
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9
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10 library(future)
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11 plan("multicore", workers = (availableCores()-1))
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12 options(future.globals.maxSize = 3000000 * 1024^2)
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13
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14 setwd("/home/gaelcge/projects/def-jsjoyal/gaelcge/scATACseq/10XData/atac_v1_pbmc_10k_output/")
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15
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16 counts <- Read10X_h5(filename = "./atac_v1_pbmc_10k_filtered_peak_bc_matrix.h5")
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18
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19 metadata <- read.csv(
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20 file = "./atac_v1_pbmc_10k_singlecell.csv",
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21 header = TRUE,
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22 row.names = 1
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23 )
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24
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25 metadata <- metadata[colnames(counts),]
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26
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27 chrom_assay <- CreateChromatinAssay(
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28 counts = counts,
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29 sep = c(":", "-"),
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30 genome = 'hg19',
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31 fragments = './atac_v1_pbmc_10k_fragments.tsv.gz',
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32 min.cells = 10,
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33 min.features = 200
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34 )
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35
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36 pbmc <- CreateSeuratObject(
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37 counts = chrom_assay,
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38 assay = "peaks",
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39 meta.data = metadata
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40 )
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41
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42 setwd("/home/gaelcge/projects/def-jsjoyal/gaelcge/scATACseq/Signac_analysis/atac_pbmc_500_nextgem")
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43
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44 pbmc[['peaks']]
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45
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46 granges(pbmc)
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47
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48 # extract gene annotations from EnsDb
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49 annotations <- GetGRangesFromEnsDb(ensdb = EnsDb.Hsapiens.v75)
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50
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51 # change to UCSC style since the data was mapped to GRCh38
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52 seqlevelsStyle(annotations) <- 'UCSC'
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53 genome(annotations) <- "GRCh38"
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54
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55 # add the gene information to the object
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56 Annotation(pbmc) <- annotations
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57
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58
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59 # compute nucleosome signal score per cell
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60 pbmc <- NucleosomeSignal(object = pbmc)
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61
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62 # compute TSS enrichment score per cell
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63 pbmc <- TSSEnrichment(object = pbmc, fast = FALSE)
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64
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65 # add blacklist ratio and fraction of reads in peaks
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66 pbmc$pct_reads_in_peaks <- pbmc$peak_region_fragments / pbmc$passed_filters * 100
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67 pbmc$blacklist_ratio <- pbmc$blacklist_region_fragments / pbmc$peak_region_fragments
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68
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70
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71 pbmc$high.tss <- ifelse(pbmc$TSS.enrichment > 2, 'High', 'Low')
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72
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73 png("Tssplot.png")
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74 TSSPlot(pbmc, group.by = 'high.tss') + NoLegend()
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75 dev.off()
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76
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78
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79 pbmc$nucleosome_group <- ifelse(pbmc$nucleosome_signal > 4, 'NS > 4', 'NS < 4')
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80 png("FragmentHistogram.png")
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81 FragmentHistogram(object = pbmc, group.by = 'nucleosome_group')
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82 dev.off()
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83
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84 png("VlnPlot_QC.png", width=1000)
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85 VlnPlot(
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86 object = pbmc,
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87 features = c('pct_reads_in_peaks', 'peak_region_fragments',
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88 'TSS.enrichment', 'blacklist_ratio', 'nucleosome_signal'),
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89 pt.size = 0.1,
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90 ncol = 5
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91 )
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92 dev.off()
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93
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94
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95 pbmc <- subset(
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96 x = pbmc,
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97 subset = peak_region_fragments > 3000 &
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98 peak_region_fragments < 20000 &
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99 pct_reads_in_peaks > 15 &
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100 blacklist_ratio < 0.05 &
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101 nucleosome_signal < 2 &
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102 TSS.enrichment > 1
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103 )
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104 pbmc
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105
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106
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107 pbmc <- RunTFIDF(pbmc)
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108 pbmc <- FindTopFeatures(pbmc, min.cutoff = 'q0')
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109 pbmc <- RunSVD(pbmc)
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110
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111 png("DepthCor.png")
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112 DepthCor(pbmc)
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113 dev.off()
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114
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115 pbmc <- RunUMAP(object = pbmc, reduction = 'lsi', dims = 2:30)
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116 pbmc <- FindNeighbors(object = pbmc, reduction = 'lsi', dims = 2:30)
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117 pbmc <- FindClusters(object = pbmc, verbose = FALSE, algorithm = 3)
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118
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119
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120 png("UMAP.png")
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121 DimPlot(object = pbmc, label = TRUE) + NoLegend()
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122 dev.off()
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123
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124
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125 gene.activities <- GeneActivity(pbmc)
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126
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127
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128 # add the gene activity matrix to the Seurat object as a new assay and normalize it
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129 pbmc[['RNA']] <- CreateAssayObject(counts = gene.activities)
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130 pbmc <- NormalizeData(
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131 object = pbmc,
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132 assay = 'RNA',
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133 normalization.method = 'LogNormalize',
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134 scale.factor = median(pbmc$nCount_RNA)
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135 )
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136
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137
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138 DefaultAssay(pbmc) <- 'RNA'
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139
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140 png("FeaturePlot_knownMarkers.png", width=1000)
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141 FeaturePlot(
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142 object = pbmc,
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143 features = c('MS4A1', 'CD3D', 'LEF1', 'NKG7', 'TREM1', 'LYZ'),
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144 pt.size = 0.1,
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145 max.cutoff = 'q95',
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146 ncol = 3
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147 )
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148 dev.off()
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149
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150
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151 saveRDS(pbmc, paste("Seurat_object.rds", sep="."))
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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
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157
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158
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159
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160
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161
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162
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163
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164
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165
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166
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167
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168
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169
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170 q("no")