if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("curatedTCGAData")Load packages:
library(curatedTCGAData)
library(MultiAssayExperiment)
library(TCGAutils)Checking available cancer codes and assays in TCGA data:
curatedTCGAData(diseaseCode = "*", assays = "*", dry.run = TRUE)## snapshotDate(): 2020-10-27## See '?curatedTCGAData' for 'diseaseCode' and 'assays' inputs## ah_id title file_size
## 1 EH558 ACC_CNASNP-20160128 0.8 Mb
## 2 EH559 ACC_CNVSNP-20160128 0.2 Mb
## 3 EH561 ACC_GISTIC_AllByGene-20160128 0.3 Mb
## 4 EH2115 ACC_GISTIC_Peaks-20160128 0 Mb
## 5 EH562 ACC_GISTIC_ThresholdedByGene-20160128 0.2 Mb
## 6 EH2116 ACC_Methylation-20160128_assays 236.4 Mb
## 7 EH2117 ACC_Methylation-20160128_se 6.1 Mb
## 8 EH565 ACC_miRNASeqGene-20160128 0.1 Mb
## 9 EH566 ACC_Mutation-20160128 0.7 Mb
## 10 EH567 ACC_RNASeq2GeneNorm-20160128 4 Mb
## 11 EH568 ACC_RPPAArray-20160128 0.1 Mb
## 12 EH570 BLCA_CNASeq-20160128 0.3 Mb
## 13 EH571 BLCA_CNASNP-20160128 4.3 Mb
## 14 EH572 BLCA_CNVSNP-20160128 1.1 Mb
## 15 EH574 BLCA_GISTIC_AllByGene-20160128 0.6 Mb
## 16 EH2118 BLCA_GISTIC_Peaks-20160128 0 Mb
## 17 EH575 BLCA_GISTIC_ThresholdedByGene-20160128 0.3 Mb
## 18 EH2119 BLCA_Methylation-20160128_assays 1283.4 Mb
## 19 EH2120 BLCA_Methylation-20160128_se 6.1 Mb
## 20 EH578 BLCA_miRNASeqGene-20160128 0.4 Mb
## 21 EH579 BLCA_Mutation-20160128 3.7 Mb
## 22 EH580 BLCA_RNASeq2GeneNorm-20160128 21.9 Mb
## 23 EH581 BLCA_RNASeqGene-20160128 2.4 Mb
## 24 EH582 BLCA_RPPAArray-20160128 0.5 Mb
## 25 EH584 BRCA_CNASeq-20160128 0 Mb
## 26 EH585 BRCA_CNASNP-20160128 9.8 Mb
## 27 EH586 BRCA_CNVSNP-20160128 2.8 Mb
## 28 EH588 BRCA_GISTIC_AllByGene-20160128 1.3 Mb
## 29 EH2121 BRCA_GISTIC_Peaks-20160128 0 Mb
## 30 EH589 BRCA_GISTIC_ThresholdedByGene-20160128 0.4 Mb
## 31 EH2122 BRCA_Methylation_methyl27-20160128_assays 63.2 Mb
## 32 EH2123 BRCA_Methylation_methyl27-20160128_se 0.4 Mb
## 33 EH2124 BRCA_Methylation_methyl450-20160128_assays 2613.2 Mb
## 34 EH2125 BRCA_Methylation_methyl450-20160128_se 6.1 Mb
## 35 EH593 BRCA_miRNASeqGene-20160128 0.6 Mb
## 36 EH594 BRCA_mRNAArray-20160128 27.3 Mb
## 37 EH595 BRCA_Mutation-20160128 4.5 Mb
## 38 EH596 BRCA_RNASeq2GeneNorm-20160128 64.5 Mb
## 39 EH597 BRCA_RNASeqGene-20160128 30 Mb
## 40 EH598 BRCA_RPPAArray-20160128 1.6 Mb
## 41 EH600 CESC_CNASeq-20160128 0.1 Mb
## 42 EH601 CESC_CNASNP-20160128 2.3 Mb
## 43 EH602 CESC_CNVSNP-20160128 0.6 Mb
## 44 EH604 CESC_GISTIC_AllByGene-20160128 0.5 Mb
## 45 EH2126 CESC_GISTIC_Peaks-20160128 0 Mb
## 46 EH605 CESC_GISTIC_ThresholdedByGene-20160128 0.3 Mb
## 47 EH2127 CESC_Methylation-20160128_assays 921.2 Mb
## 48 EH2128 CESC_Methylation-20160128_se 6.1 Mb
## 49 EH608 CESC_miRNASeqGene-20160128 0.3 Mb
## 50 EH609 CESC_Mutation-20160128 2 Mb
## 51 EH610 CESC_RNASeq2GeneNorm-20160128 16.1 Mb
## 52 EH611 CESC_RPPAArray-20160128 0.3 Mb
## 53 EH613 CHOL_CNASNP-20160128 0.4 Mb
## 54 EH614 CHOL_CNVSNP-20160128 0.1 Mb
## 55 EH616 CHOL_GISTIC_AllByGene-20160128 0.3 Mb
## 56 EH2129 CHOL_GISTIC_Peaks-20160128 0 Mb
## 57 EH617 CHOL_GISTIC_ThresholdedByGene-20160128 0.2 Mb
## 58 EH2130 CHOL_Methylation-20160128_assays 132.1 Mb
## 59 EH2131 CHOL_Methylation-20160128_se 6.1 Mb
## 60 EH620 CHOL_miRNASeqGene-20160128 0.1 Mb
## 61 EH621 CHOL_Mutation-20160128 0.2 Mb
## 62 EH622 CHOL_RNASeq2GeneNorm-20160128 2.4 Mb
## 63 EH623 CHOL_RPPAArray-20160128 0.1 Mb
## 64 EH625 COAD_CNASeq-20160128 0.3 Mb
## 65 EH626 COAD_CNASNP-20160128 3.9 Mb
## 66 EH627 COAD_CNVSNP-20160128 0.9 Mb
## 67 EH629 COAD_GISTIC_AllByGene-20160128 0.5 Mb
## 68 EH2132 COAD_GISTIC_Peaks-20160128 0 Mb
## 69 EH630 COAD_GISTIC_ThresholdedByGene-20160128 0.3 Mb
## 70 EH2133 COAD_Methylation_methyl27-20160128_assays 37.2 Mb
## 71 EH2134 COAD_Methylation_methyl27-20160128_se 0.4 Mb
## 72 EH2135 COAD_Methylation_methyl450-20160128_assays 983.8 Mb
## 73 EH2136 COAD_Methylation_methyl450-20160128_se 6.1 Mb
## 74 EH634 COAD_miRNASeqGene-20160128 0.2 Mb
## 75 EH635 COAD_mRNAArray-20160128 8.1 Mb
## 76 EH636 COAD_Mutation-20160128 1.2 Mb
## 77 EH637 COAD_RNASeq2GeneNorm-20160128 8.8 Mb
## 78 EH638 COAD_RNASeqGene-20160128 0.4 Mb
## 79 EH639 COAD_RPPAArray-20160128 0.6 Mb
## 80 EH641 DLBC_CNASNP-20160128 0.4 Mb
## 81 EH642 DLBC_CNVSNP-20160128 0.1 Mb
## 82 EH644 DLBC_GISTIC_AllByGene-20160128 0.2 Mb
## 83 EH2137 DLBC_GISTIC_Peaks-20160128 0 Mb
## 84 EH645 DLBC_GISTIC_ThresholdedByGene-20160128 0.2 Mb
## 85 EH2138 DLBC_Methylation-20160128_assays 141.8 Mb
## 86 EH2139 DLBC_Methylation-20160128_se 6.1 Mb
## 87 EH648 DLBC_miRNASeqGene-20160128 0.1 Mb
## 88 EH649 DLBC_Mutation-20160128 0.7 Mb
## 89 EH650 DLBC_RNASeq2GeneNorm-20160128 2.5 Mb
## 90 EH651 DLBC_RPPAArray-20160128 0.1 Mb
## 91 EH653 ESCA_CNASeq-20160128 0.2 Mb
## 92 EH654 ESCA_CNASNP-20160128 1.8 Mb
## 93 EH655 ESCA_CNVSNP-20160128 0.7 Mb
## 94 EH657 ESCA_GISTIC_AllByGene-20160128 0.5 Mb
## 95 EH2140 ESCA_GISTIC_Peaks-20160128 0 Mb
## 96 EH658 ESCA_GISTIC_ThresholdedByGene-20160128 0.2 Mb
## 97 EH2141 ESCA_Methylation-20160128_assays 596.9 Mb
## 98 EH2142 ESCA_Methylation-20160128_se 6.1 Mb
## 99 EH661 ESCA_miRNASeqGene-20160128 0.2 Mb
## 100 EH662 ESCA_Mutation-20160128 2.8 Mb
## 101 EH663 ESCA_RNASeq2GeneNorm-20160128 10.8 Mb
## 102 EH664 ESCA_RNASeqGene-20160128 8 Mb
## 103 EH665 ESCA_RPPAArray-20160128 0.2 Mb
## 104 EH667 GBM_CNACGH_CGH_hg_244a-20160128 0.7 Mb
## 105 EH668 GBM_CNACGH_CGH_hg_415k_g4124a-20160128 0.5 Mb
## 106 EH669 GBM_CNASNP-20160128 5.2 Mb
## 107 EH670 GBM_CNVSNP-20160128 1.5 Mb
## 108 EH672 GBM_GISTIC_AllByGene-20160128 0.7 Mb
## 109 EH2143 GBM_GISTIC_Peaks-20160128 0 Mb
## 110 EH673 GBM_GISTIC_ThresholdedByGene-20160128 0.3 Mb
## 111 EH2144 GBM_Methylation_methyl27-20160128_assays 52.4 Mb
## 112 EH2145 GBM_Methylation_methyl27-20160128_se 0.4 Mb
## 113 EH2146 GBM_Methylation_methyl450-20160128_assays 455.1 Mb
## 114 EH2147 GBM_Methylation_methyl450-20160128_se 6.1 Mb
## 115 EH677 GBM_miRNAArray-20160128 2.1 Mb
## 116 EH678 GBM_miRNASeqGene-20160128 0 Mb
## 117 EH679 GBM_mRNAArray_huex-20160128 56.3 Mb
## 118 EH2148 GBM_mRNAArray_TX_g4502a_1-20160128 22.5 Mb
## 119 EH680 GBM_mRNAArray_TX_g4502a-20160128 5.7 Mb
## 120 EH681 GBM_mRNAArray_TX_ht_hg_u133a-20160128 44.7 Mb
## 121 EH682 GBM_Mutation-20160128 2.1 Mb
## 122 EH683 GBM_RNASeq2GeneNorm-20160128 8.6 Mb
## 123 EH684 GBM_RPPAArray-20160128 0.4 Mb
## 124 EH686 HNSC_CNASeq-20160128 0.3 Mb
## 125 EH687 HNSC_CNASNP-20160128 4.2 Mb
## 126 EH688 HNSC_CNVSNP-20160128 1.1 Mb
## 127 EH690 HNSC_GISTIC_AllByGene-20160128 0.6 Mb
## 128 EH2149 HNSC_GISTIC_Peaks-20160128 0 Mb
## 129 EH691 HNSC_GISTIC_ThresholdedByGene-20160128 0.3 Mb
## 130 EH2150 HNSC_Methylation-20160128_assays 1714.7 Mb
## 131 EH2151 HNSC_Methylation-20160128_se 6.1 Mb
## 132 EH694 HNSC_miRNASeqGene-20160128 0.4 Mb
## 133 EH695 HNSC_Mutation-20160128 4.8 Mb
## 134 EH696 HNSC_RNASeq2GeneNorm-20160128 29.5 Mb
## 135 EH697 HNSC_RNASeqGene-20160128 10.3 Mb
## 136 EH698 HNSC_RPPAArray-20160128 0.3 Mb
## 137 EH700 KICH_CNASNP-20160128 0.5 Mb
## 138 EH701 KICH_CNVSNP-20160128 0.1 Mb
## 139 EH703 KICH_GISTIC_AllByGene-20160128 0.2 Mb
## 140 EH2152 KICH_GISTIC_Peaks-20160128 0 Mb
## 141 EH704 KICH_GISTIC_ThresholdedByGene-20160128 0.2 Mb
## 142 EH2153 KICH_Methylation-20160128_assays 195 Mb
## 143 EH2154 KICH_Methylation-20160128_se 6.1 Mb
## 144 EH707 KICH_miRNASeqGene-20160128 0.1 Mb
## 145 EH708 KICH_Mutation-20160128 0.1 Mb
## 146 EH709 KICH_RNASeq2GeneNorm-20160128 4.9 Mb
## 147 EH710 KICH_RPPAArray-20160128 0.1 Mb
## 148 EH712 KIRC_CNASNP-20160128 4.1 Mb
## 149 EH713 KIRC_CNVSNP-20160128 0.8 Mb
## 150 EH715 KIRC_GISTIC_AllByGene-20160128 0.5 Mb
## 151 EH2155 KIRC_GISTIC_Peaks-20160128 0 Mb
## 152 EH716 KIRC_GISTIC_ThresholdedByGene-20160128 0.2 Mb
## 153 EH2156 KIRC_Methylation_methyl27-20160128_assays 76.8 Mb
## 154 EH2157 KIRC_Methylation_methyl27-20160128_se 0.4 Mb
## 155 EH2158 KIRC_Methylation_methyl450-20160128_assays 1418.8 Mb
## 156 EH2159 KIRC_Methylation_methyl450-20160128_se 6.1 Mb
## 157 EH720 KIRC_miRNASeqGene-20160128 0.2 Mb
## 158 EH721 KIRC_mRNAArray-20160128 3.5 Mb
## 159 EH722 KIRC_Mutation-20160128 0.4 Mb
## 160 EH723 KIRC_RNASeq2GeneNorm-20160128 32.2 Mb
## 161 EH724 KIRC_RNASeqGene-20160128 18.2 Mb
## 162 EH725 KIRC_RPPAArray-20160128 0.8 Mb
## 163 EH727 KIRP_CNASNP-20160128 2.6 Mb
## 164 EH728 KIRP_CNVSNP-20160128 0.5 Mb
## 165 EH730 KIRP_GISTIC_AllByGene-20160128 0.3 Mb
## 166 EH2160 KIRP_GISTIC_Peaks-20160128 0 Mb
## 167 EH731 KIRP_GISTIC_ThresholdedByGene-20160128 0.2 Mb
## 168 EH2161 KIRP_Methylation_methyl27-20160128_assays 3.9 Mb
## 169 EH2162 KIRP_Methylation_methyl27-20160128_se 0.4 Mb
## 170 EH2163 KIRP_Methylation_methyl450-20160128_assays 948 Mb
## 171 EH2164 KIRP_Methylation_methyl450-20160128_se 6.1 Mb
## 172 EH735 KIRP_miRNASeqGene-20160128 0.3 Mb
## 173 EH736 KIRP_mRNAArray-20160128 0.9 Mb
## 174 EH737 KIRP_Mutation-20160128 0.5 Mb
## 175 EH738 KIRP_RNASeq2GeneNorm-20160128 16.7 Mb
## 176 EH739 KIRP_RNASeqGene-20160128 0.6 Mb
## 177 EH740 KIRP_RPPAArray-20160128 0.3 Mb
## 178 EH742 LAML_CNASNP-20160128 7.6 Mb
## 179 EH743 LAML_CNVSNP-20160128 0.3 Mb
## 180 EH2538 LAML_GISTIC_AllByGene-20160128 0.3 Mb
## 181 EH2539 LAML_GISTIC_Peaks-20160128 0 Mb
## 182 EH2540 LAML_GISTIC_ThresholdedByGene-20160128 0.2 Mb
## 183 EH2166 LAML_Methylation_methyl27-20160128_assays 35.6 Mb
## 184 EH2167 LAML_Methylation_methyl27-20160128_se 0.4 Mb
## 185 EH2168 LAML_Methylation_methyl450-20160128_assays 572.8 Mb
## 186 EH2169 LAML_Methylation_methyl450-20160128_se 6.1 Mb
## 187 EH748 LAML_Mutation-20160128 0.2 Mb
## 188 EH749 LAML_RNASeq2GeneNorm-20160128 8.1 Mb
## 189 EH750 LAML_RNASeqGene-20160128 5.4 Mb
## 190 EH752 LGG_CNASeq-20160128 0.1 Mb
## 191 EH753 LGG_CNASNP-20160128 3.3 Mb
## 192 EH754 LGG_CNVSNP-20160128 0.8 Mb
## 193 EH756 LGG_GISTIC_AllByGene-20160128 0.5 Mb
## 194 EH2170 LGG_GISTIC_Peaks-20160128 0 Mb
## 195 EH757 LGG_GISTIC_ThresholdedByGene-20160128 0.2 Mb
## 196 EH2171 LGG_Methylation-20160128_assays 1564.1 Mb
## 197 EH2172 LGG_Methylation-20160128_se 6.1 Mb
## 198 EH760 LGG_miRNASeqGene-20160128 0.4 Mb
## 199 EH761 LGG_mRNAArray-20160128 1.7 Mb
## 200 EH762 LGG_Mutation-20160128 0.2 Mb
## 201 EH763 LGG_RNASeq2GeneNorm-20160128 28.3 Mb
## 202 EH764 LGG_RPPAArray-20160128 0.6 Mb
## 203 EH766 LIHC_CNASNP-20160128 3.1 Mb
## 204 EH767 LIHC_CNVSNP-20160128 1 Mb
## 205 EH769 LIHC_GISTIC_AllByGene-20160128 0.6 Mb
## 206 EH2173 LIHC_GISTIC_Peaks-20160128 0 Mb
## 207 EH770 LIHC_GISTIC_ThresholdedByGene-20160128 0.3 Mb
## 208 EH2174 LIHC_Methylation-20160128_assays 1267.8 Mb
## 209 EH2175 LIHC_Methylation-20160128_se 6.1 Mb
## 210 EH773 LIHC_miRNASeqGene-20160128 0.3 Mb
## 211 EH774 LIHC_Mutation-20160128 0.9 Mb
## 212 EH775 LIHC_RNASeq2GeneNorm-20160128 20.6 Mb
## 213 EH776 LIHC_RNASeqGene-20160128 0.9 Mb
## 214 EH777 LIHC_RPPAArray-20160128 0.3 Mb
## 215 EH779 LUAD_CNASeq-20160128 3.8 Mb
## 216 EH780 LUAD_CNASNP-20160128 4.2 Mb
## 217 EH781 LUAD_CNVSNP-20160128 1.2 Mb
## 218 EH783 LUAD_GISTIC_AllByGene-20160128 0.7 Mb
## 219 EH2176 LUAD_GISTIC_Peaks-20160128 0 Mb
## 220 EH784 LUAD_GISTIC_ThresholdedByGene-20160128 0.3 Mb
## 221 EH2177 LUAD_Methylation_methyl27-20160128_assays 16.4 Mb
## 222 EH2178 LUAD_Methylation_methyl27-20160128_se 0.4 Mb
## 223 EH2179 LUAD_Methylation_methyl450-20160128_assays 1452.7 Mb
## 224 EH2180 LUAD_Methylation_methyl450-20160128_se 6.1 Mb
## 225 EH788 LUAD_miRNASeqGene-20160128 0.4 Mb
## 226 EH789 LUAD_mRNAArray-20160128 1.6 Mb
## 227 EH790 LUAD_Mutation-20160128 6.7 Mb
## 228 EH791 LUAD_RNASeq2GeneNorm-20160128 29.8 Mb
## 229 EH792 LUAD_RNASeqGene-20160128 5.6 Mb
## 230 EH793 LUAD_RPPAArray-20160128 0.6 Mb
## 231 EH795 LUSC_CNACGH-20160128 0.7 Mb
## 232 EH796 LUSC_CNASNP-20160128 4.7 Mb
## 233 EH797 LUSC_CNVSNP-20160128 1.4 Mb
## 234 EH799 LUSC_GISTIC_AllByGene-20160128 0.7 Mb
## 235 EH2181 LUSC_GISTIC_Peaks-20160128 0 Mb
## 236 EH800 LUSC_GISTIC_ThresholdedByGene-20160128 0.3 Mb
## 237 EH2182 LUSC_Methylation_methyl27-20160128_assays 29.5 Mb
## 238 EH2183 LUSC_Methylation_methyl27-20160128_se 0.4 Mb
## 239 EH2184 LUSC_Methylation_methyl450-20160128_assays 1218.4 Mb
## 240 EH2185 LUSC_Methylation_methyl450-20160128_se 6.1 Mb
## 241 EH804 LUSC_miRNASeqGene-20160128 0.3 Mb
## 242 EH805 LUSC_mRNAArray_huex-20160128 14.8 Mb
## 243 EH806 LUSC_mRNAArray_TX_g4502a-20160128 7.2 Mb
## 244 EH807 LUSC_mRNAArray_TX_ht_hg_u133a-20160128 11.4 Mb
## 245 EH808 LUSC_Mutation-20160128 5.7 Mb
## 246 EH809 LUSC_RNASeq2GeneNorm-20160128 29.2 Mb
## 247 EH810 LUSC_RNASeqGene-20160128 8.4 Mb
## 248 EH811 LUSC_RPPAArray-20160128 0.5 Mb
## 249 EH813 MESO_CNASNP-20160128 0.8 Mb
## 250 EH814 MESO_CNVSNP-20160128 0.2 Mb
## 251 EH816 MESO_GISTIC_AllByGene-20160128 0.3 Mb
## 252 EH2186 MESO_GISTIC_Peaks-20160128 0 Mb
## 253 EH817 MESO_GISTIC_ThresholdedByGene-20160128 0.2 Mb
## 254 EH2187 MESO_Methylation-20160128_assays 257.2 Mb
## 255 EH2188 MESO_Methylation-20160128_se 6.1 Mb
## 256 EH820 MESO_miRNASeqGene-20160128 0.1 Mb
## 257 EH821 MESO_RNASeq2GeneNorm-20160128 4.6 Mb
## 258 EH822 MESO_RPPAArray-20160128 0.1 Mb
## 259 EH824 OV_CNACGH_CGH_hg_244a-20160128 1.1 Mb
## 260 EH825 OV_CNACGH_CGH_hg_415k_g4124a-20160128 2.2 Mb
## 261 EH826 OV_CNASNP-20160128 8.2 Mb
## 262 EH827 OV_CNVSNP-20160128 2.7 Mb
## 263 EH829 OV_GISTIC_AllByGene-20160128 1.2 Mb
## 264 EH2189 OV_GISTIC_Peaks-20160128 0 Mb
## 265 EH830 OV_GISTIC_ThresholdedByGene-20160128 0.4 Mb
## 266 EH2190 OV_Methylation_methyl27-20160128_assays 108.7 Mb
## 267 EH2191 OV_Methylation_methyl27-20160128_se 0.4 Mb
## 268 EH2192 OV_Methylation_methyl450-20160128_assays 29.6 Mb
## 269 EH2193 OV_Methylation_methyl450-20160128_se 6.1 Mb
## 270 EH834 OV_miRNAArray-20160128 3.2 Mb
## 271 EH835 OV_miRNASeqGene-20160128 0.3 Mb
## 272 EH836 OV_mRNAArray_huex-20160128 75.2 Mb
## 273 EH2194 OV_mRNAArray_TX_g4502a_1-20160128 25.2 Mb
## 274 EH837 OV_mRNAArray_TX_g4502a-20160128 1.9 Mb
## 275 EH838 OV_mRNAArray_TX_ht_hg_u133a-20160128 44.6 Mb
## 276 EH839 OV_Mutation-20160128 0.5 Mb
## 277 EH840 OV_RNASeq2GeneNorm-20160128 16.7 Mb
## 278 EH841 OV_RNASeqGene-20160128 10.4 Mb
## 279 EH842 OV_RPPAArray-20160128 0.7 Mb
## 280 EH844 PAAD_CNASNP-20160128 1.7 Mb
## 281 EH845 PAAD_CNVSNP-20160128 0.4 Mb
## 282 EH847 PAAD_GISTIC_AllByGene-20160128 0.4 Mb
## 283 EH2195 PAAD_GISTIC_Peaks-20160128 0 Mb
## 284 EH848 PAAD_GISTIC_ThresholdedByGene-20160128 0.2 Mb
## 285 EH2196 PAAD_Methylation-20160128_assays 575.8 Mb
## 286 EH2197 PAAD_Methylation-20160128_se 6.1 Mb
## 287 EH851 PAAD_miRNASeqGene-20160128 0.2 Mb
## 288 EH852 PAAD_Mutation-20160128 6.8 Mb
## 289 EH853 PAAD_RNASeq2GeneNorm-20160128 9.7 Mb
## 290 EH854 PAAD_RPPAArray-20160128 0.2 Mb
## 291 EH856 PCPG_CNASNP-20160128 2.6 Mb
## 292 EH857 PCPG_CNVSNP-20160128 0.3 Mb
## 293 EH859 PCPG_GISTIC_AllByGene-20160128 0.3 Mb
## 294 EH2198 PCPG_GISTIC_Peaks-20160128 0 Mb
## 295 EH860 PCPG_GISTIC_ThresholdedByGene-20160128 0.2 Mb
## 296 EH2199 PCPG_Methylation-20160128_assays 552.9 Mb
## 297 EH2200 PCPG_Methylation-20160128_se 6.1 Mb
## 298 EH863 PCPG_miRNASeqGene-20160128 0.2 Mb
## 299 EH864 PCPG_Mutation-20160128 0.6 Mb
## 300 EH865 PCPG_RNASeq2GeneNorm-20160128 9.6 Mb
## 301 EH866 PCPG_RPPAArray-20160128 0.1 Mb
## 302 EH868 PRAD_CNASeq-20160128 0.2 Mb
## 303 EH869 PRAD_CNASNP-20160128 4.9 Mb
## 304 EH870 PRAD_CNVSNP-20160128 1.2 Mb
## 305 EH872 PRAD_GISTIC_AllByGene-20160128 0.6 Mb
## 306 EH2201 PRAD_GISTIC_Peaks-20160128 0 Mb
## 307 EH873 PRAD_GISTIC_ThresholdedByGene-20160128 0.2 Mb
## 308 EH2202 PRAD_Methylation-20160128_assays 1622.5 Mb
## 309 EH2203 PRAD_Methylation-20160128_se 6.1 Mb
## 310 EH876 PRAD_miRNASeqGene-20160128 0.4 Mb
## 311 EH877 PRAD_Mutation-20160128 1.4 Mb
## 312 EH878 PRAD_RNASeq2GeneNorm-20160128 28.9 Mb
## 313 EH879 PRAD_RPPAArray-20160128 0.5 Mb
## 314 EH881 READ_CNASeq-20160128 0.4 Mb
## 315 EH882 READ_CNASNP-20160128 1.4 Mb
## 316 EH883 READ_CNVSNP-20160128 0.4 Mb
## 317 EH885 READ_GISTIC_AllByGene-20160128 0.3 Mb
## 318 EH2204 READ_GISTIC_Peaks-20160128 0 Mb
## 319 EH886 READ_GISTIC_ThresholdedByGene-20160128 0.2 Mb
## 320 EH2205 READ_Methylation_methyl27-20160128_assays 13.5 Mb
## 321 EH2206 READ_Methylation_methyl27-20160128_se 0.4 Mb
## 322 EH2207 READ_Methylation_methyl450-20160128_assays 313.3 Mb
## 323 EH2208 READ_Methylation_methyl450-20160128_se 6.1 Mb
## 324 EH890 READ_miRNASeqGene-20160128 0.1 Mb
## 325 EH891 READ_mRNAArray-20160128 3.5 Mb
## 326 EH892 READ_Mutation-20160128 0.4 Mb
## 327 EH893 READ_RNASeq2GeneNorm-20160128 3.5 Mb
## 328 EH894 READ_RNASeqGene-20160128 2.1 Mb
## 329 EH895 READ_RPPAArray-20160128 0.2 Mb
## 330 EH897 SARC_CNASNP-20160128 3.1 Mb
## 331 EH898 SARC_CNVSNP-20160128 1.1 Mb
## 332 EH900 SARC_GISTIC_AllByGene-20160128 0.6 Mb
## 333 EH2209 SARC_GISTIC_Peaks-20160128 0 Mb
## 334 EH901 SARC_GISTIC_ThresholdedByGene-20160128 0.3 Mb
## 335 EH2210 SARC_Methylation-20160128_assays 794.3 Mb
## 336 EH2211 SARC_Methylation-20160128_se 6.1 Mb
## 337 EH904 SARC_miRNASeqGene-20160128 0.2 Mb
## 338 EH905 SARC_Mutation-20160128 1.1 Mb
## 339 EH906 SARC_RNASeq2GeneNorm-20160128 13.6 Mb
## 340 EH907 SARC_RPPAArray-20160128 0.3 Mb
## 341 EH1029 SKCM_CNASeq-20160128 0.3 Mb
## 342 EH1030 SKCM_CNASNP-20160128 3.8 Mb
## 343 EH1031 SKCM_CNVSNP-20160128 1.1 Mb
## 344 EH2541 SKCM_GISTIC_AllByGene-20160128 0.5 Mb
## 345 EH2542 SKCM_GISTIC_Peaks-20160128 0 Mb
## 346 EH2543 SKCM_GISTIC_ThresholdedByGene-20160128 0.2 Mb
## 347 EH2213 SKCM_Methylation-20160128_assays 1403.5 Mb
## 348 EH2214 SKCM_Methylation-20160128_se 6.1 Mb
## 349 EH1033 SKCM_miRNASeqGene-20160128 0.4 Mb
## 350 EH1034 SKCM_Mutation-20160128 26.7 Mb
## 351 EH1035 SKCM_RNASeq2GeneNorm-20160128 24.5 Mb
## 352 EH1036 SKCM_RPPAArray-20160128 0.6 Mb
## 353 EH920 STAD_CNASeq-20160128 0.3 Mb
## 354 EH921 STAD_CNASNP-20160128 3.8 Mb
## 355 EH922 STAD_CNVSNP-20160128 1.2 Mb
## 356 EH924 STAD_GISTIC_AllByGene-20160128 0.7 Mb
## 357 EH2215 STAD_GISTIC_Peaks-20160128 0 Mb
## 358 EH925 STAD_GISTIC_ThresholdedByGene-20160128 0.3 Mb
## 359 EH2216 STAD_Methylation_methyl27-20160128_assays 13.5 Mb
## 360 EH2217 STAD_Methylation_methyl27-20160128_se 0.4 Mb
## 361 EH2218 STAD_Methylation_methyl450-20160128_assays 1172.9 Mb
## 362 EH2219 STAD_Methylation_methyl450-20160128_se 6.1 Mb
## 363 EH929 STAD_miRNASeqGene-20160128 0.3 Mb
## 364 EH930 STAD_Mutation-20160128 13.2 Mb
## 365 EH931 STAD_RNASeq2GeneNorm-20160128 23.9 Mb
## 366 EH932 STAD_RNASeqGene-20160128 1.6 Mb
## 367 EH933 STAD_RPPAArray-20160128 0.5 Mb
## 368 EH935 TGCT_CNASNP-20160128 1.2 Mb
## 369 EH936 TGCT_CNVSNP-20160128 0.3 Mb
## 370 EH938 TGCT_GISTIC_AllByGene-20160128 0.3 Mb
## 371 EH2220 TGCT_GISTIC_Peaks-20160128 0 Mb
## 372 EH939 TGCT_GISTIC_ThresholdedByGene-20160128 0.2 Mb
## 373 EH2221 TGCT_Methylation-20160128_assays 411.3 Mb
## 374 EH2222 TGCT_Methylation-20160128_se 6.1 Mb
## 375 EH942 TGCT_miRNASeqGene-20160128 0.2 Mb
## 376 EH943 TGCT_Mutation-20160128 0.5 Mb
## 377 EH944 TGCT_RNASeq2GeneNorm-20160128 7.4 Mb
## 378 EH945 TGCT_RPPAArray-20160128 0.2 Mb
## 379 EH947 THCA_CNASeq-20160128 0 Mb
## 380 EH948 THCA_CNASNP-20160128 3.1 Mb
## 381 EH949 THCA_CNVSNP-20160128 0.5 Mb
## 382 EH951 THCA_GISTIC_AllByGene-20160128 0.3 Mb
## 383 EH2223 THCA_GISTIC_Peaks-20160128 0 Mb
## 384 EH952 THCA_GISTIC_ThresholdedByGene-20160128 0.2 Mb
## 385 EH2224 THCA_Methylation-20160128_assays 1674.6 Mb
## 386 EH2225 THCA_Methylation-20160128_se 6.1 Mb
## 387 EH955 THCA_miRNASeqGene-20160128 0.5 Mb
## 388 EH956 THCA_Mutation-20160128 0.9 Mb
## 389 EH957 THCA_RNASeq2GeneNorm-20160128 30.1 Mb
## 390 EH958 THCA_RNASeqGene-20160128 0.2 Mb
## 391 EH959 THCA_RPPAArray-20160128 0.3 Mb
## 392 EH961 THYM_CNASNP-20160128 0.9 Mb
## 393 EH962 THYM_CNVSNP-20160128 0.2 Mb
## 394 EH964 THYM_GISTIC_AllByGene-20160128 0.2 Mb
## 395 EH2226 THYM_GISTIC_Peaks-20160128 0 Mb
## 396 EH965 THYM_GISTIC_ThresholdedByGene-20160128 0.2 Mb
## 397 EH2227 THYM_Methylation-20160128_assays 372.2 Mb
## 398 EH2228 THYM_Methylation-20160128_se 6.1 Mb
## 399 EH968 THYM_miRNASeqGene-20160128 0.1 Mb
## 400 EH969 THYM_Mutation-20160128 0.2 Mb
## 401 EH970 THYM_RNASeq2GeneNorm-20160128 6.4 Mb
## 402 EH971 THYM_RPPAArray-20160128 0.2 Mb
## 403 EH973 UCEC_CNASeq-20160128 0.3 Mb
## 404 EH974 UCEC_CNASNP-20160128 5.4 Mb
## 405 EH975 UCEC_CNVSNP-20160128 1.3 Mb
## 406 EH977 UCEC_GISTIC_AllByGene-20160128 0.7 Mb
## 407 EH2229 UCEC_GISTIC_Peaks-20160128 0 Mb
## 408 EH978 UCEC_GISTIC_ThresholdedByGene-20160128 0.3 Mb
## 409 EH2230 UCEC_Methylation_methyl27-20160128_assays 21.7 Mb
## 410 EH2231 UCEC_Methylation_methyl27-20160128_se 0.4 Mb
## 411 EH2232 UCEC_Methylation_methyl450-20160128_assays 1377 Mb
## 412 EH2233 UCEC_Methylation_methyl450-20160128_se 6.1 Mb
## 413 EH982 UCEC_miRNASeqGene-20160128 0.4 Mb
## 414 EH983 UCEC_mRNAArray-20160128 2.6 Mb
## 415 EH984 UCEC_Mutation-20160128 4.6 Mb
## 416 EH985 UCEC_RNASeq2GeneNorm-20160128 18.4 Mb
## 417 EH986 UCEC_RNASeqGene-20160128 7.7 Mb
## 418 EH987 UCEC_RPPAArray-20160128 0.7 Mb
## 419 EH989 UCS_CNASNP-20160128 0.5 Mb
## 420 EH990 UCS_CNVSNP-20160128 0.2 Mb
## 421 EH992 UCS_GISTIC_AllByGene-20160128 0.3 Mb
## 422 EH2234 UCS_GISTIC_Peaks-20160128 0 Mb
## 423 EH993 UCS_GISTIC_ThresholdedByGene-20160128 0.3 Mb
## 424 EH2235 UCS_Methylation-20160128_assays 168.3 Mb
## 425 EH2236 UCS_Methylation-20160128_se 6.1 Mb
## 426 EH996 UCS_miRNASeqGene-20160128 0.1 Mb
## 427 EH997 UCS_Mutation-20160128 1.2 Mb
## 428 EH998 UCS_RNASeq2GeneNorm-20160128 3.1 Mb
## 429 EH999 UCS_RPPAArray-20160128 0.1 Mb
## 430 EH1001 UVM_CNASeq-20160128 0.1 Mb
## 431 EH1002 UVM_CNASNP-20160128 0.6 Mb
## 432 EH1003 UVM_CNVSNP-20160128 0.1 Mb
## 433 EH1005 UVM_GISTIC_AllByGene-20160128 0.3 Mb
## 434 EH2237 UVM_GISTIC_Peaks-20160128 0 Mb
## 435 EH1006 UVM_GISTIC_ThresholdedByGene-20160128 0.2 Mb
## 436 EH2238 UVM_Methylation-20160128_assays 236.4 Mb
## 437 EH2239 UVM_Methylation-20160128_se 6.1 Mb
## 438 EH1009 UVM_miRNASeqGene-20160128 0.1 Mb
## 439 EH1010 UVM_Mutation-20160128 0.9 Mb
## 440 EH1011 UVM_RNASeq2GeneNorm-20160128 4 Mb
## 441 EH1012 UVM_RPPAArray-20160128 0 Mb
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## 338 RaggedExperiment 2017-10-10 <NA>
## 339 SummarizedExperiment 2017-10-10 <NA>
## 340 SummarizedExperiment 2017-10-10 <NA>
## 341 RaggedExperiment 2017-10-16 <NA>
## 342 RaggedExperiment 2017-10-16 <NA>
## 343 RaggedExperiment 2017-10-16 <NA>
## 344 SummarizedExperiment 2019-04-29 <NA>
## 345 RangedSummarizedExperiment 2019-04-29 <NA>
## 346 SummarizedExperiment 2019-04-29 <NA>
## 347 SummarizedExperiment 2019-01-09 <NA>
## 348 RaggedExperiment 2019-01-09 <NA>
## 349 SummarizedExperiment 2017-10-16 <NA>
## 350 RaggedExperiment 2017-10-16 <NA>
## 351 SummarizedExperiment 2017-10-16 <NA>
## 352 SummarizedExperiment 2017-10-16 <NA>
## 353 RaggedExperiment 2017-10-10 <NA>
## 354 RaggedExperiment 2017-10-10 <NA>
## 355 RaggedExperiment 2017-10-10 <NA>
## 356 SummarizedExperiment 2017-10-10 <NA>
## 357 RangedSummarizedExperiment 2019-01-09 <NA>
## 358 SummarizedExperiment 2017-10-10 <NA>
## 359 SummarizedExperiment 2019-01-09 <NA>
## 360 RaggedExperiment 2019-01-09 <NA>
## 361 SummarizedExperiment 2019-01-09 <NA>
## 362 SummarizedExperiment 2019-01-09 <NA>
## 363 SummarizedExperiment 2017-10-10 <NA>
## 364 RaggedExperiment 2017-10-10 <NA>
## 365 SummarizedExperiment 2017-10-10 <NA>
## 366 SummarizedExperiment 2017-10-10 <NA>
## 367 SummarizedExperiment 2017-10-10 <NA>
## 368 RaggedExperiment 2017-10-10 <NA>
## 369 RaggedExperiment 2017-10-10 <NA>
## 370 SummarizedExperiment 2017-10-10 <NA>
## 371 RangedSummarizedExperiment 2019-01-09 <NA>
## 372 SummarizedExperiment 2017-10-10 <NA>
## 373 SummarizedExperiment 2019-01-09 <NA>
## 374 RaggedExperiment 2019-01-09 <NA>
## 375 SummarizedExperiment 2017-10-10 <NA>
## 376 RaggedExperiment 2017-10-10 <NA>
## 377 SummarizedExperiment 2017-10-10 <NA>
## 378 SummarizedExperiment 2017-10-10 <NA>
## 379 RaggedExperiment 2017-10-10 <NA>
## 380 RaggedExperiment 2017-10-10 <NA>
## 381 RaggedExperiment 2017-10-10 <NA>
## 382 SummarizedExperiment 2017-10-10 <NA>
## 383 RangedSummarizedExperiment 2019-01-09 <NA>
## 384 SummarizedExperiment 2017-10-10 <NA>
## 385 SummarizedExperiment 2019-01-09 <NA>
## 386 RaggedExperiment 2019-01-09 <NA>
## 387 SummarizedExperiment 2017-10-10 <NA>
## 388 RaggedExperiment 2017-10-10 <NA>
## 389 SummarizedExperiment 2017-10-10 <NA>
## 390 SummarizedExperiment 2017-10-10 <NA>
## 391 SummarizedExperiment 2017-10-10 <NA>
## 392 RaggedExperiment 2017-10-10 <NA>
## 393 RaggedExperiment 2017-10-10 <NA>
## 394 SummarizedExperiment 2017-10-10 <NA>
## 395 RangedSummarizedExperiment 2019-01-09 <NA>
## 396 SummarizedExperiment 2017-10-10 <NA>
## 397 SummarizedExperiment 2019-01-09 <NA>
## 398 RaggedExperiment 2019-01-09 <NA>
## 399 SummarizedExperiment 2017-10-10 <NA>
## 400 RaggedExperiment 2017-10-10 <NA>
## 401 SummarizedExperiment 2017-10-10 <NA>
## 402 SummarizedExperiment 2017-10-10 <NA>
## 403 RaggedExperiment 2017-10-10 <NA>
## 404 RaggedExperiment 2017-10-10 <NA>
## 405 RaggedExperiment 2017-10-10 <NA>
## 406 SummarizedExperiment 2017-10-10 <NA>
## 407 RangedSummarizedExperiment 2019-01-09 <NA>
## 408 SummarizedExperiment 2017-10-10 <NA>
## 409 SummarizedExperiment 2019-01-09 <NA>
## 410 SummarizedExperiment 2019-01-09 <NA>
## 411 RaggedExperiment 2019-01-09 <NA>
## 412 SummarizedExperiment 2019-01-09 <NA>
## 413 SummarizedExperiment 2017-10-10 <NA>
## 414 SummarizedExperiment 2017-10-10 <NA>
## 415 RaggedExperiment 2017-10-10 <NA>
## 416 SummarizedExperiment 2017-10-10 <NA>
## 417 SummarizedExperiment 2017-10-10 <NA>
## 418 SummarizedExperiment 2017-10-10 <NA>
## 419 RaggedExperiment 2017-10-10 <NA>
## 420 RaggedExperiment 2017-10-10 <NA>
## 421 SummarizedExperiment 2017-10-10 <NA>
## 422 RangedSummarizedExperiment 2019-01-09 <NA>
## 423 SummarizedExperiment 2017-10-10 <NA>
## 424 SummarizedExperiment 2019-01-09 <NA>
## 425 RaggedExperiment 2019-01-09 <NA>
## 426 SummarizedExperiment 2017-10-10 <NA>
## 427 RaggedExperiment 2017-10-10 <NA>
## 428 SummarizedExperiment 2017-10-10 <NA>
## 429 SummarizedExperiment 2017-10-10 <NA>
## 430 RaggedExperiment 2017-10-10 <NA>
## 431 RaggedExperiment 2017-10-10 <NA>
## 432 RaggedExperiment 2017-10-10 <NA>
## 433 SummarizedExperiment 2017-10-10 <NA>
## 434 RangedSummarizedExperiment 2019-01-09 <NA>
## 435 SummarizedExperiment 2017-10-10 <NA>
## 436 SummarizedExperiment 2019-01-09 <NA>
## 437 RaggedExperiment 2019-01-09 <NA>
## 438 SummarizedExperiment 2017-10-10 <NA>
## 439 RaggedExperiment 2017-10-10 <NA>
## 440 SummarizedExperiment 2017-10-10 <NA>
## 441 SummarizedExperiment 2017-10-10 <NA>Check potential files to be downloaded:
curatedTCGAData(diseaseCode = "COAD", assays = "RPPA*", dry.run = TRUE)## snapshotDate(): 2020-10-27## See '?curatedTCGAData' for 'diseaseCode' and 'assays' inputs## ah_id title file_size rdataclass rdatadateadded
## 1 EH639 COAD_RPPAArray-20160128 0.6 Mb SummarizedExperiment 2017-10-10
## rdatadateremoved
## 1 <NA>Not all TCGA samples are cancer, there are a mix of samples in each of the
33 cancer types. Use sampleTables on the MultiAssayExperiment object
along with data(sampleTypes, package = "TCGAutils") to see what samples are
present in the data. There may be tumors that were used to create multiple
contributions leading to technical replicates. These should be resolved using
the appropriate helper functions such as mergeReplicates. Primary tumors
should be selected using TCGAutils::TCGAsampleSelect and used as input
to the subsetting mechanisms. See the “Samples in Assays” section of this
vignette.
(accmae <- curatedTCGAData("ACC", c("CN*", "Mutation"), FALSE))## A MultiAssayExperiment object of 3 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 3:
## [1] ACC_CNASNP-20160128: RaggedExperiment with 79861 rows and 180 columns
## [2] ACC_CNVSNP-20160128: RaggedExperiment with 21052 rows and 180 columns
## [3] ACC_Mutation-20160128: RaggedExperiment with 20166 rows and 90 columns
## Functionality:
## experiments() - obtain the ExperimentList instance
## colData() - the primary/phenotype DataFrame
## sampleMap() - the sample coordination DataFrame
## `$`, `[`, `[[` - extract colData columns, subset, or experiment
## *Format() - convert into a long or wide DataFrame
## assays() - convert ExperimentList to a SimpleList of matrices
## exportClass() - save all data to filesNote. For more on how to use a MultiAssayExperiment please see the
MultiAssayExperiment vignette.
Some cancer datasets contain associated subtype information within the
clinical datasets provided. This subtype information is included in the
metadata of colData of the MultiAssayExperiment object. To obtain these
variable names, use the getSubtypeMap function from TCGA utils:
head(getSubtypeMap(accmae))## ACC_annotations ACC_subtype
## 1 Patient_ID patientID
## 2 histological_subtypes Histology
## 3 mrna_subtypes C1A/C1B
## 4 mrna_subtypes mRNA_K4
## 5 cimp MethyLevel
## 6 microrna_subtypes miRNA clusterAnother helper function provided by TCGAutils allows users to obtain a set
of consistent clinical variable names across several cancer types.
Use the getClinicalNames function to obtain a character vector of common
clinical variables such as vital status, years to birth, days to death, etc.
head(getClinicalNames("ACC"))## [1] "years_to_birth" "vital_status" "days_to_death"
## [4] "days_to_last_followup" "tumor_tissue_site" "pathologic_stage"colData(accmae)[, getClinicalNames("ACC")][1:5, 1:5]## DataFrame with 5 rows and 5 columns
## years_to_birth vital_status days_to_death days_to_last_followup
## <integer> <integer> <integer> <integer>
## TCGA-OR-A5J1 58 1 1355 NA
## TCGA-OR-A5J2 44 1 1677 NA
## TCGA-OR-A5J3 23 0 NA 2091
## TCGA-OR-A5J4 23 1 423 NA
## TCGA-OR-A5J5 30 1 365 NA
## tumor_tissue_site
## <character>
## TCGA-OR-A5J1 adrenal
## TCGA-OR-A5J2 adrenal
## TCGA-OR-A5J3 adrenal
## TCGA-OR-A5J4 adrenal
## TCGA-OR-A5J5 adrenalThe sampleTables function gives an overview of sample types / codes
present in the data:
sampleTables(accmae)## $`ACC_CNASNP-20160128`
##
## 01 10 11
## 90 85 5
##
## $`ACC_CNVSNP-20160128`
##
## 01 10 11
## 90 85 5
##
## $`ACC_Mutation-20160128`
##
## 01
## 90Often, an analysis is performed comparing two groups of samples to each other.
To facilitate the separation of samples, the splitAssays TCGAutils function
identifies all sample types in the assays and moves each into its own
assay. By default, all discoverable sample types are separated into
a separate experiment. In this case we requested only solid tumors and blood
derived normal samples as seen in the sampleTypes reference dataset:
data(sampleTypes, package = "TCGAutils")
sampleTypes[sampleTypes[["Code"]] %in% c("01", "10"), ]## Code Definition Short.Letter.Code
## 1 01 Primary Solid Tumor TP
## 10 10 Blood Derived Normal NBsplitAssays(accmae, c("01", "10"))## Warning: Some 'sampleCodes' not found in assays## A MultiAssayExperiment object of 5 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 5:
## [1] 01_ACC_CNASNP-20160128: RaggedExperiment with 79861 rows and 90 columns
## [2] 10_ACC_CNASNP-20160128: RaggedExperiment with 79861 rows and 85 columns
## [3] 01_ACC_CNVSNP-20160128: RaggedExperiment with 21052 rows and 90 columns
## [4] 10_ACC_CNVSNP-20160128: RaggedExperiment with 21052 rows and 85 columns
## [5] 01_ACC_Mutation-20160128: RaggedExperiment with 20166 rows and 90 columns
## Functionality:
## experiments() - obtain the ExperimentList instance
## colData() - the primary/phenotype DataFrame
## sampleMap() - the sample coordination DataFrame
## `$`, `[`, `[[` - extract colData columns, subset, or experiment
## *Format() - convert into a long or wide DataFrame
## assays() - convert ExperimentList to a SimpleList of matrices
## exportClass() - save all data to filesTo obtain a logical vector that could be used for subsetting a
MultiAsssayExperiment, refer to TCGAsampleSelect. To select only primary
tumors, use the function on the colnames of the MultiAssayExperiment:
tums <- TCGAsampleSelect(colnames(accmae), "01")You can subsequently provide this input to the subsetting function to select only primary tumors:
accmae[, tums, ]## harmonizing input:
## removing 180 sampleMap rows with 'colname' not in colnames of experiments## A MultiAssayExperiment object of 3 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 3:
## [1] ACC_CNASNP-20160128: RaggedExperiment with 79861 rows and 90 columns
## [2] ACC_CNVSNP-20160128: RaggedExperiment with 21052 rows and 90 columns
## [3] ACC_Mutation-20160128: RaggedExperiment with 20166 rows and 90 columns
## Functionality:
## experiments() - obtain the ExperimentList instance
## colData() - the primary/phenotype DataFrame
## sampleMap() - the sample coordination DataFrame
## `$`, `[`, `[[` - extract colData columns, subset, or experiment
## *Format() - convert into a long or wide DataFrame
## assays() - convert ExperimentList to a SimpleList of matrices
## exportClass() - save all data to filesMultiAssayExperiment provides users with an integrative representation of multi-omic TCGA data at the convenience of the user. For those users who wish to use alternative environments, we have provided an export function to extract all the data from a MultiAssayExperiment instance and write them to a series of files:
td <- tempdir()
tempd <- file.path(td, "ACCMAE")
if (!dir.exists(tempd))
dir.create(tempd)
exportClass(accmae, dir = tempd, fmt = "csv", ext = ".csv")## Writing about 7 files to disk...## [1] "/tmp/RtmpmOLW7L/ACCMAE/accmae_META_0.csv"
## [2] "/tmp/RtmpmOLW7L/ACCMAE/accmae_META_1.csv"
## [3] "/tmp/RtmpmOLW7L/ACCMAE/accmae_ACC_CNASNP-20160128.csv"
## [4] "/tmp/RtmpmOLW7L/ACCMAE/accmae_ACC_CNVSNP-20160128.csv"
## [5] "/tmp/RtmpmOLW7L/ACCMAE/accmae_ACC_Mutation-20160128.csv"
## [6] "/tmp/RtmpmOLW7L/ACCMAE/accmae_colData.csv"
## [7] "/tmp/RtmpmOLW7L/ACCMAE/accmae_sampleMap.csv"This works for all data classes stored (e.g., RaggedExperiment, HDF5Matrix,
SummarizedExperiment) in the MultiAssayExperiment via the assays method
which converts classes to matrix format (using individual assay methods).
sessionInfo()## R version 4.0.5 (2021-03-31)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.5 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.12-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.12-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] RaggedExperiment_1.14.2 TCGAutils_1.10.1
## [3] curatedTCGAData_1.12.1 MultiAssayExperiment_1.16.0
## [5] SummarizedExperiment_1.20.0 Biobase_2.50.0
## [7] GenomicRanges_1.42.0 GenomeInfoDb_1.26.7
## [9] IRanges_2.24.1 S4Vectors_0.28.1
## [11] BiocGenerics_0.36.1 MatrixGenerics_1.2.1
## [13] matrixStats_0.58.0 BiocStyle_2.18.1
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-7 bit64_4.0.5
## [3] progress_1.2.2 httr_1.4.2
## [5] GenomicDataCommons_1.14.0 tools_4.0.5
## [7] bslib_0.2.4 utf8_1.2.1
## [9] R6_2.5.0 DBI_1.1.1
## [11] withr_2.4.2 tidyselect_1.1.0
## [13] prettyunits_1.1.1 bit_4.0.4
## [15] curl_4.3 compiler_4.0.5
## [17] rvest_1.0.0 xml2_1.3.2
## [19] DelayedArray_0.16.3 rtracklayer_1.50.0
## [21] bookdown_0.22 sass_0.3.1
## [23] readr_1.4.0 askpass_1.1
## [25] rappdirs_0.3.3 stringr_1.4.0
## [27] digest_0.6.27 Rsamtools_2.6.0
## [29] rmarkdown_2.7 XVector_0.30.0
## [31] pkgconfig_2.0.3 htmltools_0.5.1.1
## [33] dbplyr_2.1.1 fastmap_1.1.0
## [35] rlang_0.4.10 RSQLite_2.2.7
## [37] shiny_1.6.0 jquerylib_0.1.3
## [39] generics_0.1.0 jsonlite_1.7.2
## [41] BiocParallel_1.24.1 dplyr_1.0.5
## [43] RCurl_1.98-1.3 magrittr_2.0.1
## [45] GenomeInfoDbData_1.2.4 Matrix_1.3-2
## [47] Rcpp_1.0.6 fansi_0.4.2
## [49] lifecycle_1.0.0 stringi_1.5.3
## [51] yaml_2.2.1 zlibbioc_1.36.0
## [53] BiocFileCache_1.14.0 AnnotationHub_2.22.1
## [55] grid_4.0.5 blob_1.2.1
## [57] promises_1.2.0.1 ExperimentHub_1.16.1
## [59] crayon_1.4.1 lattice_0.20-41
## [61] Biostrings_2.58.0 GenomicFeatures_1.42.3
## [63] hms_1.0.0 knitr_1.33
## [65] pillar_1.6.0 biomaRt_2.46.3
## [67] XML_3.99-0.6 glue_1.4.2
## [69] BiocVersion_3.12.0 evaluate_0.14
## [71] BiocManager_1.30.12 vctrs_0.3.7
## [73] httpuv_1.6.0 openssl_1.4.3
## [75] purrr_0.3.4 assertthat_0.2.1
## [77] cachem_1.0.4 xfun_0.22
## [79] mime_0.10 xtable_1.8-4
## [81] later_1.2.0 tibble_3.1.1
## [83] GenomicAlignments_1.26.0 AnnotationDbi_1.52.0
## [85] memoise_2.0.0 ellipsis_0.3.1
## [87] interactiveDisplayBase_1.28.0