├── Finucane2015_ct_names.txt ├── LICENSE ├── README.md ├── input_example ├── input_ct.txt ├── input_rg.txt └── traitlist.txt ├── output ├── network_ct.png ├── network_ct_1518554735.RData ├── network_ct_1518554735.pdf ├── network_ct_1518554735.png └── network_rg.png ├── plot_network_ct.R └── plot_network_rg.R /Finucane2015_ct_names.txt: -------------------------------------------------------------------------------- 1 | category cell_type cell_type_clean file_num mark cell_type_clean2 2 | Other BI.Adipose_Nuclei_peaks.bed Adipose_nuclei 1 H3K4me1 Adipose nuclei 3 | Kidney BI.Adult_Kidney_peaks.bed Kidney 2 H3K4me1 Kidney 4 | Liver BI.Adult_Liver_peaks.bed Liver_(BI) 3 H3K4me1 Liver 5 | CNS BI.Brain_Angular_Gyrus_peaks.bed Angular_gyrus 4 H3K4me1 Angular gyrus 6 | CNS BI.Brain_Anterior_Caudate_peaks.bed Anterior_caudate 5 H3K4me1 Anterior caudate 7 | CNS BI.Brain_Cingulate_Gyrus_peaks.bed Cingulate_gyrus 6 H3K4me1 Cingulate gyrus 8 | CNS BI.Brain_Hippocampus_Middle_peaks.bed Hippocampus_middle 7 H3K4me1 Hippocampus middle 9 | CNS BI.Brain_Inferior_Temporal_Lobe_peaks.bed Inferior_temporal_lobe 8 H3K4me1 Inferior temporal lobe 10 | CNS BI.Brain_Mid_Frontal_Lobe_peaks.bed Mid_frontal_lobe 9 H3K4me1 Mid frontal lobe 11 | CNS BI.Brain_Substantia_Nigra_peaks.bed Substantia_nigra 10 H3K4me1 Substantia nigra 12 | Hematopoietic BI.CD15_Primary_Cells_peaks.bed CD15_primary 11 H3K4me1 CD15 13 | Hematopoietic BI.CD19_Primary_Cells_peaks.bed CD19_primary_(BI) 12 H3K4me1 CD19 14 | Hematopoietic BI.CD34_Primary_Cells_peaks.bed CD34_primary 13 H3K4me1 CD34 15 | Hematopoietic BI.CD3_Primary_Cells_peaks.bed CD3_primary_(BI) 14 H3K4me1 CD3 16 | Hematopoietic BI.CD4+_CD25+_CD127-_Treg_Primary_Cells_peaks.bed CD4+_CD25+_CD127-_Treg_primary 15 H3K4me1 CD4+CD25+CD127- 17 | Hematopoietic BI.CD4+_CD25-_CD45RA+_Naive_Primary_Cells_peaks.bed CD4+_CD25-_CD45RA+_naive_primary 16 H3K4me1 CD4+CD25-CD45RA+ naive 18 | Hematopoietic BI.CD4+_CD25-_CD45RO+_Memory_Primary_Cells_peaks.bed CD4+_CD25-_CD45R0+_memory_primary 17 H3K4me1 CD4+CD25-CD45RO+ memory 19 | Hematopoietic BI.CD4+_CD25-_IL17+_PMA-Ionomcyin_stimulated_Th17_Primary_Cells_peaks.bed CD4+_CD25-_IL17+_PMA_Ionomycin_stim_Th17_primary 18 H3K4me1 CD4+CD25-IL17+ stim Th17 20 | Hematopoietic BI.CD4+_CD25-_IL17-_PMA-Ionomycin_stimulated_MACS_purified_Th_Primary_Cells_peaks.bed CD4+_CD25-_IL17-_PMA_Ionomycin_stim_MACS_Th_sprimary 19 H3K4me1 CD4+CD25-IL17- stim Th 21 | Hematopoietic BI.CD4+_CD25int_CD127+_Tmem_Primary_Cells_peaks.bed CD4+_CD25int_CD127+_Tmem_primary 20 H3K4me1 CD4+CD25intCD127+ Tmem 22 | Hematopoietic BI.CD4+_CD25-_Th_Primary_Cells_peaks.bed CD4+_CD25-_Th_primary 21 H3K4me1 CD4+CD25- Th 23 | Hematopoietic BI.CD4_Memory_Primary_Cells_peaks.bed CD4_memory_primary 22 H3K4me1 CD4 memory 24 | Hematopoietic BI.CD4_Naive_Primary_Cells_peaks.bed CD4_naive_primary 23 H3K4me1 CD4 naive 25 | Hematopoietic BI.CD8_Memory_Primary_Cells_peaks.bed CD8_memory_primary 24 H3K4me1 CD8 memory 26 | Hematopoietic BI.CD8_Naive_Primary_Cells_peaks.bed CD8_naive_primary_(BI) 25 H3K4me1 CD8 naive 27 | GI BI.Colonic_Mucosa_peaks.bed Colonic_mucosa 26 H3K4me1 Colonic mucosa 28 | GI BI.Colon_Smooth_Muscle_peaks.bed Colon_smooth_muscle 27 H3K4me1 Colon smooth muscle 29 | GI BI.Duodenum_Mucosa_peaks.bed Duodenum_Mucosa 28 H3K4me1 Duodenum Mucosa 30 | GI BI.Duodenum_Smooth_Muscle_peaks.bed Duodenum_smooth_muscle 29 H3K4me1 Duodenum smooth muscle 31 | CNS BI.Fetal_Brain_peaks.bed Fetal_brain 30 H3K4me1 Fetal brain 32 | Cardiovascular BI.Fetal_Heart_peaks.bed Fetal_heart 31 H3K4me1 Fetal heart 33 | Cardiovascular BI.Fetal_Lung_peaks.bed Fetal_lung 32 H3K4me1 Fetal lung 34 | Hematopoietic BI.Mobilized_CD34_Primary_Cells_peaks.bed Mobilized_CD34_primary 33 H3K4me1 Mobilized CD34 35 | Adrenal_Pancreas BI.Pancreatic_Islets_peaks.bed Pancreatic_islets 34 H3K4me1 Pancreatic islets 36 | GI BI.Rectal_Mucosa_peaks.bed Rectal_mucosa 35 H3K4me1 Rectal mucosa 37 | GI BI.Rectal_Smooth_Muscle_peaks.bed Rectal_smooth_muscle 36 H3K4me1 Rectal smooth muscle 38 | SkeletalMuscle BI.Skeletal_Muscle_peaks.bed Skeletal_muscle 37 H3K4me1 Skeletal muscle 39 | GI BI.Stomach_Mucosa_peaks.bed Stomach_mucosa 38 H3K4me1 Stomach mucosa 40 | GI BI.Stomach_Smooth_Muscle_peaks.bed Stomach_smooth_muscle 39 H3K4me1 Stomach smooth muscle 41 | Liver UCSD.Adult_Liver_peaks.bed Liver_(UCSD) 40 H3K4me1 Liver 42 | GI UCSD.Esophagus_peaks.bed Esophagus 41 H3K4me1 Esophagus 43 | GI UCSD.Gastric_peaks.bed Gastric 42 H3K4me1 Gastric 44 | Cardiovascular UCSD.Left_Ventricle_peaks.bed Left_Ventricle 43 H3K4me1 Left Ventricle 45 | Cardiovascular UCSD.Lung_peaks.bed Lung 44 H3K4me1 Lung 46 | Other UCSD.Ovary_peaks.bed Ovary 45 H3K4me1 Ovary 47 | Adrenal_Pancreas UCSD.Pancreas_peaks.bed Pancreas 46 H3K4me1 Pancreas 48 | SkeletalMuscle UCSD.Psoas_Muscle_peaks.bed Psoas_muscle 47 H3K4me1 Psoas muscle 49 | Cardiovascular UCSD.Right_Atrium_peaks.bed Right_atrium 48 H3K4me1 Right atrium 50 | Cardiovascular UCSD.Right_Ventricle_peaks.bed Right_ventricle 49 H3K4me1 Right ventricle 51 | GI UCSD.Sigmoid_Colon_peaks.bed Sigmoid_colon 50 H3K4me1 Sigmoid colon 52 | GI UCSD.Small_Intestine_peaks.bed Small_intestine 51 H3K4me1 Small intestine 53 | Hematopoietic UCSD.Spleen_peaks.bed Spleen 52 H3K4me1 Spleen 54 | Hematopoietic UCSD.Thymus_peaks.bed Thymus 53 H3K4me1 Thymus 55 | Hematopoietic UW.Fetal_Thymus_peaks.bed Fetal_thymus 54 H3K4me1 Fetal thymus 56 | Connective_Bone UCSF-UBC.Breast_Fibroblast_Primary_Cells_peaks.bed Breast_fibroblast_primary 55 H3K4me1 Breast fibroblast 57 | Other UCSF-UBC.Breast_Luminal_Epithelial_Cells_peaks.bed Breast_luminal_epithelial 56 H3K4me1 Breast luminal epithelial 58 | Other UCSF-UBC.Breast_Myoepithelial_Cells_peaks.bed Breast_myoepithelial 57 H3K4me1 Breast myoepithelial 59 | Other UCSF-UBC.Breast_vHMEC_peaks.bed Breast_vHMEC 58 H3K4me1 Breast vHMEC 60 | Hematopoietic UCSF-UBC.CD8_Naive_Primary_Cells_peaks.bed CD8_naive_primary_(UCSF-UBC) 59 H3K4me1 CD8 naive 61 | Adrenal_Pancreas UCSF-UBC.Pancreatic_Islets_peaks.bed Pancreatic_islets 60 H3K4me1 Pancreatic islets 62 | Connective_Bone UCSF-UBC.Penis_Foreskin_Fibroblast_Primary_Cells_peaks.bed Penis_foreskin_fibroblast_primary 61 H3K4me1 Penis foreskin fibroblast 63 | Other UCSF-UBC.Penis_Foreskin_Keratinocyte_Primary_Cells_peaks.bed Penis_foreskin_keratinocyte_primary 62 H3K4me1 Penis foreskin keratinocyte 64 | Other UCSF-UBC.Penis_Foreskin_Melanocyte_Primary_Cells_peaks.bed Penis_foreskin_melanocyte_primary 63 H3K4me1 Penis foreskin melanocyte 65 | Hematopoietic UCSF-UBC.Peripheral_Blood_Mononuclear_Primary_Cells_peaks.bed Peripheralblood_mononuclear_primary 64 H3K4me1 PBMC 66 | Other UCSF-UBC.Placenta_Amnion_peaks.bed Placenta_amnion 65 H3K4me1 Placenta amnion 67 | Other UCSF-UBC.Placenta_Chorion_Smooth_peaks.bed Placenta_chorion 66 H3K4me1 Placenta chorion 68 | Hematopoietic UW.CD14_Primary_Cells_peaks.bed CD14_primary 67 H3K4me1 CD14 69 | Hematopoietic UW.CD19_Primary_Cells_peaks.bed CD19_primary_(UW) 68 H3K4me1 CD19 70 | Hematopoietic UW.CD3_Primary_Cells_peaks.bed CD3_primary_(UW) 69 H3K4me1 CD3 71 | Hematopoietic UW.CD56_Primary_Cells_peaks.bed CD56_primary 70 H3K4me1 CD56 72 | Adrenal_Pancreas UW.Fetal_Adrenal_Gland_peaks.bed Fetal_adrenal 71 H3K4me1 Fetal adrenal 73 | GI UW.Fetal_Intestine_Large_peaks.bed Fetal_large_intestine 72 H3K4me1 Fetal large intestine 74 | GI UW.Fetal_Intestine_Small_peaks.bed Fetal_small_intestine 73 H3K4me1 Fetal small intestine 75 | SkeletalMuscle UW.Fetal_Muscle_Leg_peaks.bed Fetal_leg_muscle 74 H3K4me1 Fetal leg muscle 76 | SkeletalMuscle UW.Fetal_Muscle_Trunk_peaks.bed Fetal_trunk_muscle 75 H3K4me1 Fetal trunk muscle 77 | Other UW.Fetal_Placenta_peaks.bed Fetal_placenta 76 H3K4me1 Fetal placenta 78 | GI UW.Fetal_Stomach_peaks.bed Fetal_stomach 77 H3K4me1 Fetal stomach 79 | Other BI.Adipose_Nuclei_peaks.bed Adipose_nuclei 78 H3K4me3 Adipose nuclei 80 | Kidney BI.Adult_Kidney_peaks.bed Kidney 79 H3K4me3 Kidney 81 | Liver BI.Adult_Liver_peaks.bed Liver_(BI) 80 H3K4me3 Liver 82 | CNS BI.Brain_Angular_Gyrus_peaks.bed Angular_gyrus 81 H3K4me3 Angular gyrus 83 | CNS BI.Brain_Anterior_Caudate_peaks.bed Anterior_caudate 82 H3K4me3 Anterior caudate 84 | CNS BI.Brain_Cingulate_Gyrus_peaks.bed Cingulate_gyrus 83 H3K4me3 Cingulate gyrus 85 | CNS BI.Brain_Hippocampus_Middle_peaks.bed Hippocampus_middle 84 H3K4me3 Hippocampus middle 86 | CNS BI.Brain_Inferior_Temporal_Lobe_peaks.bed Inferior_temporal_lobe 85 H3K4me3 Inferior temporal lobe 87 | CNS BI.Brain_Mid_Frontal_Lobe_peaks.bed Mid_frontal_lobe 86 H3K4me3 Mid frontal lobe 88 | CNS BI.Brain_Substantia_Nigra_peaks.bed Substantia_nigra 87 H3K4me3 Substantia nigra 89 | Hematopoietic BI.CD15_Primary_Cells_peaks.bed CD15_primary 88 H3K4me3 CD15 90 | Hematopoietic BI.CD19_Primary_Cells_peaks.bed CD19_primary_(BI) 89 H3K4me3 CD19 91 | Hematopoietic BI.CD34_Primary_Cells_peaks.bed CD34_primary 90 H3K4me3 CD34 92 | Hematopoietic BI.CD3_Primary_Cells_peaks.bed CD3_primary_(BI) 91 H3K4me3 CD3 93 | Hematopoietic BI.CD4+_CD25+_CD127-_Treg_Primary_Cells_peaks.bed CD4+_CD25+_CD127-_Treg_primary 92 H3K4me3 CD4+CD25+CD127- Treg 94 | Hematopoietic BI.CD4+_CD25-_CD45RA+_Naive_Primary_Cells_peaks.bed CD4+_CD25-_CD45RA+_naive_primary 93 H3K4me3 CD4+CD25-CD45RA+ naive 95 | Hematopoietic BI.CD4+_CD25-_CD45RO+_Memory_Primary_Cells_peaks.bed CD4+_CD25-_CD45R0+_memory_primary 94 H3K4me3 CD4+CD25-CD45RO+ memory 96 | Hematopoietic BI.CD4+_CD25-_IL17+_PMA-Ionomcyin_stimulated_Th17_Primary_Cells_peaks.bed CD4+_CD25-_IL17+_PMA_Ionomycin_stim_Th17_primary 95 H3K4me3 CD4+CD25-IL17+ stim Th17 97 | Hematopoietic BI.CD4+_CD25-_IL17-_PMA-Ionomycin_stimulated_MACS_purified_Th_Primary_Cells_peaks.bed CD4+_CD25-_IL17-_PMA_Ionomycin_stim_MACS_Th_sprimary 96 H3K4me3 CD4+CD25-IL17- stim Th 98 | Hematopoietic BI.CD4+_CD25int_CD127+_Tmem_Primary_Cells_peaks.bed CD4+_CD25int_CD127+_Tmem_primary 97 H3K4me3 CD4+CD25intCD127+ Tmem 99 | Hematopoietic BI.CD4+_CD25-_Th_Primary_Cells_peaks.bed CD4+_CD25-_Th_primary 98 H3K4me3 CD4+CD25- Th 100 | Hematopoietic BI.CD4_Memory_Primary_Cells_peaks.bed CD4_memory_primary 99 H3K4me3 CD4 memory 101 | Hematopoietic BI.CD4_Naive_Primary_Cells_peaks.bed CD4_naive_primary 100 H3K4me3 CD4 naive 102 | Hematopoietic BI.CD8_Memory_Primary_Cells_peaks.bed CD8_memory_primary 101 H3K4me3 CD8 memory 103 | Hematopoietic BI.CD8_Naive_Primary_Cells_peaks.bed CD8_naive_primary_(BI) 102 H3K4me3 CD8 naive 104 | GI BI.Colonic_Mucosa_peaks.bed Colonic_mucosa 103 H3K4me3 Colonic mucosa 105 | GI BI.Colon_Smooth_Muscle_peaks.bed Colon_smooth_muscle 104 H3K4me3 Colon smooth muscle 106 | GI BI.Duodenum_Mucosa_peaks.bed Duodenum_Mucosa 105 H3K4me3 Duodenum Mucosa 107 | GI BI.Duodenum_Smooth_Muscle_peaks.bed Duodenum_smooth_muscle 106 H3K4me3 Duodenum smooth muscle 108 | CNS BI.Fetal_Brain_peaks.bed Fetal_brain 107 H3K4me3 Fetal brain 109 | Cardiovascular BI.Fetal_Heart_peaks.bed Fetal_heart 108 H3K4me3 Fetal heart 110 | Cardiovascular BI.Fetal_Lung_peaks.bed Fetal_lung 109 H3K4me3 Fetal lung 111 | Hematopoietic BI.Mobilized_CD34_Primary_Cells_peaks.bed Mobilized_CD34_primary 110 H3K4me3 Mobilized CD34 112 | Adrenal_Pancreas BI.Pancreatic_Islets_peaks.bed Pancreatic_islets 111 H3K4me3 Pancreatic islets 113 | GI BI.Rectal_Mucosa_peaks.bed Rectal_mucosa 112 H3K4me3 Rectal mucosa 114 | GI BI.Rectal_Smooth_Muscle_peaks.bed Rectal_smooth_muscle 113 H3K4me3 Rectal smooth muscle 115 | SkeletalMuscle BI.Skeletal_Muscle_peaks.bed Skeletal_muscle 114 H3K4me3 Skeletal muscle 116 | GI BI.Stomach_Mucosa_peaks.bed Stomach_mucosa 115 H3K4me3 Stomach mucosa 117 | GI BI.Stomach_Smooth_Muscle_peaks.bed Stomach_smooth_muscle 116 H3K4me3 Stomach smooth muscle 118 | Hematopoietic BI.Treg_Primary_Cells_peaks.bed Treg_primary 117 H3K4me3 Treg 119 | Liver UCSD.Adult_Liver_peaks.bed Liver_(UCSD) 118 H3K4me3 Liver 120 | Cardiovascular UCSD.Aorta_peaks.bed Aorta 119 H3K4me3 Aorta 121 | GI UCSD.Esophagus_peaks.bed Esophagus 120 H3K4me3 Esophagus 122 | GI UCSD.Gastric_peaks.bed Gastric 121 H3K4me3 Gastric 123 | Cardiovascular UCSD.Left_Ventricle_peaks.bed Left_Ventricle 122 H3K4me3 Left Ventricle 124 | Cardiovascular UCSD.Lung_peaks.bed Lung 123 H3K4me3 Lung 125 | Other UCSD.Ovary_peaks.bed Ovary 124 H3K4me3 Ovary 126 | Adrenal_Pancreas UCSD.Pancreas_peaks.bed Pancreas 125 H3K4me3 Pancreas 127 | SkeletalMuscle UCSD.Psoas_Muscle_peaks.bed Psoas_muscle 126 H3K4me3 Psoas muscle 128 | Cardiovascular UCSD.Right_Atrium_peaks.bed Right_atrium 127 H3K4me3 Right atrium 129 | Cardiovascular UCSD.Right_Ventricle_peaks.bed Right_ventricle 128 H3K4me3 Right ventricle 130 | GI UCSD.Sigmoid_Colon_peaks.bed Sigmoid_colon 129 H3K4me3 Sigmoid colon 131 | GI UCSD.Small_Intestine_peaks.bed Small_intestine 130 H3K4me3 Small intestine 132 | Hematopoietic UCSD.Spleen_peaks.bed Spleen 131 H3K4me3 Spleen 133 | CNS UCSF-UBC.Brain_Germinal_Matrix_peaks.bed Germinal_matrix 132 H3K4me3 Germinal matrix 134 | Connective_Bone UCSF-UBC.Breast_Fibroblast_Primary_Cells_peaks.bed Breast_fibroblast_primary 133 H3K4me3 Breast fibroblast 135 | Other UCSF-UBC.Breast_Myoepithelial_Cells_peaks.bed Breast_myoepithelial 134 H3K4me3 Breast myoepithelial 136 | Other UCSF-UBC.Breast_vHMEC_peaks.bed Breast_vHMEC 135 H3K4me3 Breast vHMEC 137 | Hematopoietic UCSF-UBC.CD8_Naive_Primary_Cells_peaks.bed CD8_naive_primary_(UCSF-UBC) 136 H3K4me3 CD8 naive 138 | CNS UCSF-UBC.Fetal_Brain_peaks.bed Fetal_brain 137 H3K4me3 Fetal brain 139 | Adrenal_Pancreas UCSF-UBC.Pancreatic_Islets_peaks.bed Pancreatic_islets 138 H3K4me3 Pancreatic islets 140 | Connective_Bone UCSF-UBC.Penis_Foreskin_Fibroblast_Primary_Cells_peaks.bed Penis_foreskin_fibroblast_primary 139 H3K4me3 Penis foreskin fibroblast 141 | Other UCSF-UBC.Penis_Foreskin_Keratinocyte_Primary_Cells_peaks.bed Penis_foreskin_keratinocyte_primary 140 H3K4me3 Penis foreskin keratinocyte 142 | Other UCSF-UBC.Penis_Foreskin_Melanocyte_Primary_Cells_peaks.bed Penis_foreskin_melanocyte_primary 141 H3K4me3 Penis foreskin melanocyte 143 | Hematopoietic UCSF-UBC.Peripheral_Blood_Mononuclear_Primary_Cells_peaks.bed Peripheralblood_mononuclear_primary 142 H3K4me3 PBMC 144 | Other UCSF-UBC.Placenta_Amnion_peaks.bed Placenta_amnion 143 H3K4me3 Placenta amnion 145 | Other UCSF-UBC.Placenta_Chorion_Smooth_peaks.bed Placenta_chorion 144 H3K4me3 Placenta chorion 146 | Hematopoietic UW.CD14_Primary_Cells_peaks.bed CD14_primary 145 H3K4me3 CD14 147 | Hematopoietic UW.CD19_Primary_Cells_peaks.bed CD19_primary_(UW) 146 H3K4me3 CD19 148 | Hematopoietic UW.CD3_Primary_Cells_peaks.bed CD3_primary_(UW) 147 H3K4me3 CD3 149 | Hematopoietic UW.CD4_Primary_Cells_peaks.bed CD4_primary 148 H3K4me3 CD4 150 | Hematopoietic UW.CD56_Primary_Cells_peaks.bed CD56_primary 149 H3K4me3 CD56 151 | Hematopoietic UW.CD8_Primary_Cells_peaks.bed CD8_primary 150 H3K4me3 CD8 152 | Adrenal_Pancreas UW.Fetal_Adrenal_Gland_peaks.bed Fetal_adrenal 151 H3K4me3 Fetal adrenal 153 | GI UW.Fetal_Intestine_Large_peaks.bed Fetal_large_intestine 152 H3K4me3 Fetal large intestine 154 | GI UW.Fetal_Intestine_Small_peaks.bed Fetal_small_intestine 153 H3K4me3 Fetal small intestine 155 | SkeletalMuscle UW.Fetal_Muscle_Leg_peaks.bed Fetal_leg_muscle 154 H3K4me3 Fetal leg muscle 156 | SkeletalMuscle UW.Fetal_Muscle_Trunk_peaks.bed Fetal_trunk_muscle 155 H3K4me3 Fetal trunk muscle 157 | Other UW.Fetal_Placenta_peaks.bed Fetal_placenta 156 H3K4me3 Fetal placenta 158 | GI UW.Fetal_Stomach_peaks.bed Fetal_stomach 157 H3K4me3 Fetal stomach 159 | Hematopoietic UW.Fetal_Thymus_peaks.bed Fetal_thymus 158 H3K4me3 Fetal thymus 160 | Other BI.Adipose_Nuclei_peaks.bed Adipose_nuclei 159 H3K9ac Adipose nuclei 161 | Kidney BI.Adult_Kidney_peaks.bed Kidney 160 H3K9ac Kidney 162 | Liver BI.Adult_Liver_peaks.bed Liver_(BI) 161 H3K9ac Liver 163 | CNS BI.Brain_Angular_Gyrus_peaks.bed Angular_gyrus 162 H3K9ac Angular gyrus 164 | CNS BI.Brain_Anterior_Caudate_peaks.bed Anterior_caudate 163 H3K9ac Anterior caudate 165 | CNS BI.Brain_Cingulate_Gyrus_peaks.bed Cingulate_gyrus 164 H3K9ac Cingulate gyrus 166 | CNS BI.Brain_Hippocampus_Middle_peaks.bed Hippocampus_middle 165 H3K9ac Hippocampus middle 167 | CNS BI.Brain_Inferior_Temporal_Lobe_peaks.bed Inferior_temporal_lobe 166 H3K9ac Inferior temporal lobe 168 | CNS BI.Brain_Mid_Frontal_Lobe_peaks.bed Mid_frontal_lobe 167 H3K9ac Mid frontal lobe 169 | CNS BI.Brain_Substantia_Nigra_peaks.bed Substantia_nigra 168 H3K9ac Substantia nigra 170 | GI BI.Colonic_Mucosa_peaks.bed Colonic_mucosa 169 H3K9ac Colonic mucosa 171 | GI BI.Colon_Smooth_Muscle_peaks.bed Colon_smooth_muscle 170 H3K9ac Colon smooth muscle 172 | GI BI.Duodenum_Mucosa_peaks.bed Duodenum_Mucosa 171 H3K9ac Duodenum Mucosa 173 | Cardiovascular BI.Fetal_Heart_peaks.bed Fetal_heart 172 H3K9ac Fetal heart 174 | Kidney BI.Fetal_Kidney_peaks.bed Fetal_kidney 173 H3K9ac Fetal kidney 175 | Cardiovascular BI.Fetal_Lung_peaks.bed Fetal_lung 174 H3K9ac Fetal lung 176 | Adrenal_Pancreas BI.Pancreatic_Islets_peaks.bed Pancreatic_islets 175 H3K9ac Pancreatic islets 177 | GI BI.Rectal_Mucosa_peaks.bed Rectal_mucosa 176 H3K9ac Rectal mucosa 178 | GI BI.Rectal_Smooth_Muscle_peaks.bed Rectal_smooth_muscle 177 H3K9ac Rectal smooth muscle 179 | SkeletalMuscle BI.Skeletal_Muscle_peaks.bed Skeletal_muscle 178 H3K9ac Skeletal muscle 180 | GI BI.Stomach_Mucosa_peaks.bed Stomach_mucosa 179 H3K9ac Stomach mucosa 181 | GI BI.Stomach_Smooth_Muscle_peaks.bed Stomach_smooth_muscle 180 H3K9ac Stomach smooth muscle 182 | Other UCSF-UBC.Breast_Myoepithelial_Cells_peaks.bed Breast_myoepithelial 181 H3K9ac Breast myoepithelial 183 | Hematopoietic UCSF-UBC.CD8_Naive_Primary_Cells_peaks.bed CD8_naive_primary_(UCSF-UBC) 182 H3K9ac CD8 naive 184 | CNS UCSF-UBC.Fetal_Brain_peaks.bed Fetal_brain 183 H3K9ac Fetal brain 185 | Other UCSF-UBC.Penis_Foreskin_Keratinocyte_Primary_Cells_peaks.bed Penis_foreskin_keratinocyte_primary 184 H3K9ac Penis foreskin keratinocyte 186 | Hematopoietic UCSF-UBC.Peripheral_Blood_Mononuclear_Primary_Cells_peaks.bed Peripheralblood_mononuclear_primary 185 H3K9ac PBMC 187 | Hematopoietic CD3_Primary_Cells CD3_primary 186 H3K27ac CD3 188 | Hematopoietic CD25-_CD45RA+_naive CD25-_CD45RA+_naive 187 H3K27ac CD4+CD25-CD45RA+ naïve 189 | Hematopoietic CD25+_CD127-_Treg CD25+_CD127-_Treg 188 H3K27ac CD4+CD25+CD127- Treg 190 | Hematopoietic CD25int_CD127+_Tmem CD25int_CD127+_Tmem 189 H3K27ac CD4+CD25intCD127+ Tmem 191 | Hematopoietic CD25-_IL17+_Th17_stim CD25-_IL17+_Th17_stim 190 H3K27ac CD4+CD25-IL17+ stim Th17 192 | Hematopoietic CD25-_IL17-_Th_stim_MACS CD25-_IL17-_Th_stim_MACS 191 H3K27ac CD4+CD25-IL17- stim Th 193 | Hematopoietic Th0 Th0 192 H3K27ac Th0 194 | Hematopoietic Th1 Th1 193 H3K27ac Th1 195 | Hematopoietic Th2 Th2 194 H3K27ac Th2 196 | Hematopoietic CD19 CD19 195 H3K27ac CD19 197 | Hematopoietic adult_CD20 CD20 196 H3K27ac CD20 198 | Hematopoietic adult_CD14 CD14 197 H3K27ac CD14 199 | Hematopoietic Mobilized_CD34 Mobilized_CD34 198 H3K27ac Mobilized CD34 200 | Connective_Bone NH-Osteoblast Osteoblast 199 H3K27ac Osteoblast 201 | Connective_Bone chondrogenic_dif_cells Chondrogenic_dif 200 H3K27ac Chondrogenic dif 202 | GI colon_smooth_muscle Colon_smooth_muscle 201 H3K27ac Colon smooth muscle 203 | GI stomach_smooth_muscle Stomach_smooth_muscle 202 H3K27ac Stomach smooth muscle 204 | GI rectal_smooth_muscle Rectal_smooth_muscle 203 H3K27ac Rectal smooth muscle 205 | GI Duodenum_smooth_muscle Duodenum_smooth_muscle 204 H3K27ac Duodenum smooth muscle 206 | SkeletalMuscle Skeletal_muscle Skeletal_muscle 205 H3K27ac Skeletal muscle 207 | Other AdiposeNuclei Adipose_nuclei 206 H3K27ac Adipose nuclei 208 | GI rectal_mucosa Rectal_mucosa 207 H3K27ac Rectal mucosa 209 | GI colonic_mucosa Colonic_mucosa 208 H3K27ac Colonic mucosa 210 | GI Duodenum_mucosa Duodenum_mucosa 209 H3K27ac Duodenum mucosa 211 | Liver Liver1 Liver 210 H3K27ac Liver 212 | Adrenal_Pancreas Pancreatic_Islets Pancreatic_islets 211 H3K27ac Pancreatic islets 213 | Kidney kidney Kidney 212 H3K27ac Kidney 214 | CNS Neurosphere Neurosphere 213 H3K27ac Neurosphere 215 | CNS inferior_temporal_lobe Inferior_temporal_lobe 214 H3K27ac Inferior temporal lobe 216 | CNS angular_gyrus Angular_gyrus 215 H3K27ac Angular gyrus 217 | CNS mid_frontal_lobe Mid_frontal_lobe 216 H3K27ac Mid frontal lobe 218 | CNS cingulate_gyrus Cingulate_gyrus 217 H3K27ac Cingulate gyrus 219 | CNS substantia_nigra Substantia_nigra 218 H3K27ac Substantia nigra 220 | CNS anterior_caudate Anterior_caudate 219 H3K27ac Anterior caudate 221 | CNS hippocampus_middle Hippocampus_middle 220 H3K27ac Hippocampus middle 222 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Network representation of LDSC resuls 2 | 3 | ## Overview 4 | 5 | This repo provides our scripts to plot genetic correlation network (Fig. 3) and cell-type specificity network (Fig. 6) based on bivariate/stratified LD score regression results ([Kanai, M. _et al_., _Nat. Genet._ 2018](http://dx.doi.org/10.1038/s41588-018-0047-6)). 6 | 7 | ## Requirements 8 | 9 | - [R](https://www.r-project.org/) with [igraph](https://github.com/igraph/rigraph), [dplyr](https://github.com/tidyverse/dplyr), [stringr](https://github.com/tidyverse/stringr) and [Hmisc](https://github.com/harrelfe/Hmisc) packages. 10 | - To install these packages, type `install.packages(c("igraph", "tidyverse", "Hmisc"))` in R console. 11 | 12 | ## Genetic correlation network (Fig. 3) 13 | 14 | Genetic correlation network was designed to visualize cross-phenotype relationships (pairwise genetic correlation matrix) across dozens of traits estimated via [bivariate LD score regression](https://www.nature.com/articles/ng.3406). Each circle represents a trait and each edge represents a significant genetic correlation (FDR < 0.05). Positive and negative genetic correlations are indicated by color. Thicker edges correspond to more significant FDRs. 15 | 16 |

17 | 18 | ### Usage 19 | 20 | ```{sh} 21 | Rscript plot_network_rg.R input_example/input_rg.txt input_example/traitlist.txt 22 | ``` 23 | 24 | ### Input: Genetic correlations (`input_rg.txt`) 25 | 26 | This file provides a list of all pairwise genetic correlations estimated via [ldsc](https://github.com/bulik/ldsc) software. The script expects all rows are unique (_i.e.,_ one row per each pair of traits). The required fields are as follows: 27 | 28 | - `p1_category`: Trait category of trait 1 29 | - `p1`: Trait 1 30 | - `p2_category`: Trait category of trait 2 31 | - `p2`: Trait 2 32 | - `rg`: Genetic correlation 33 | - `p`: P-value 34 | - `q`: FDR q-value 35 | 36 | ### Input: Trait list (`traitlist.txt`) 37 | 38 | This file provides a list of traits and their categories. It defines a color of each category in a figure. The required fields are as follows: 39 | 40 | - `CATEGORY`: Trait category 41 | - `TRAIT`: Trait name 42 | - `COLOR`: Category color 43 | 44 | ### Output 45 | 46 | An example output is shown below. Since layouts are determined by the Fruchterman–Reingold algorithm, they are slightly different at each runtime. To get the final figure, we edited a pdf output using Adobe Illustrator. 47 | 48 |

49 | 50 | ## Cell-type specificity network (Fig. 6) 51 | 52 | Cell-type specificity network was designed to summarize cell-type specific enrichments estimated via [stratified LD score regression](https://www.nature.com/articles/ng.3404). Each circle represents a trait, and each square represents a cell type. Arrows denote significant heritability enrichments for the indicated traits (FDR < 0.01). Thicker arrows correspond to more significant FDRs. 53 | 54 |

55 | 56 | ### Usage 57 | 58 | ```{sh} 59 | Rscript plot_network_ct.R input_example/input_ct.txt input_example/traitlist.txt 60 | ``` 61 | 62 | ### Input: Cell-type specific enrichments (`input_ct.txt`) 63 | 64 | This file provides a concatenated list of all cell-type specific enrichments estimated via [ldsc](https://github.com/bulik/ldsc) software. We ran the analysis using the 220 cell-type specific annotations based on the Roadmap Project ([Finucane, H. _et al_., _Nat. Genet._ 2015](https://www.nature.com/articles/ng.3404)). The required fields are as follows: 65 | 66 | - `trait_category`: Trait category 67 | - `trait`: Trait 68 | - `category`: Cell-type category 69 | - `cell_type_clean`: Cell type 70 | - `Coefficient`: Estimated coefficient 71 | - `Coefficient_p`: P-value 72 | - `Coefficient_q`: FDR q-value 73 | 74 | ### Input: Traitlist (`traitlist.txt`) 75 | 76 | This file is exactly the same as the aforementioned trait list (for genetic correlation network). 77 | 78 | ### Output 79 | 80 | An example output is shown below. Again, layouts are slightly different at each runtime and we edited a pdf output using Adobe Illustrator to get the final figure. 81 | 82 |

83 | 84 | ## Citation 85 | 86 | When using these scripts, please cite the following paper. 87 | 88 | - Kanai, M., _et al_. Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases. _Nat. Genet._ (2018) [doi:10.1038/s41588-018-0047-6](http://dx.doi.org/10.1038/s41588-018-0047-6) 89 | 90 | ## Contact 91 | 92 | Masahiro Kanai (mkanai@g.harvard.edu) 93 | 94 | http://mkanai.github.io/ 95 | 96 | ## Links 97 | 98 | - [JENGER](http://jenger.riken.jp/en/) (the lab website) 99 | - [The BioBank Japan Project](https://biobankjp.org/english/index.html) 100 | - [RIKEN Center for Integrative Medical Sciences](http://www.ims.riken.jp/english/) 101 | - [National Bioscience Database Center Human Database](https://humandbs.biosciencedbc.jp/en/) 102 | - [ldsc](https://github.com/bulik/ldsc) 103 | -------------------------------------------------------------------------------- /input_example/traitlist.txt: -------------------------------------------------------------------------------- 1 | CATEGORY TRAIT COLOR 2 | Anthropometric Height #4C72AE 3 | Anthropometric BMI #4C72AE 4 | Metabolic TC #E4812F 5 | Metabolic HDL-C #E4812F 6 | Metabolic LDL-C #E4812F 7 | Metabolic TG #E4812F 8 | Metabolic BS #E4812F 9 | Metabolic HbA1c #E4812F 10 | Protein TP #8C61B6 11 | Protein Alb #8C61B6 12 | Protein NAP #8C61B6 13 | Protein A/G #8C61B6 14 | Kidney-related BUN #80584E 15 | Kidney-related sCr #80584E 16 | Kidney-related eGFR #80584E 17 | Kidney-related UA #80584E 18 | Electrolyte Na #6BBCCC 19 | Electrolyte K #6BBCCC 20 | Electrolyte Cl #6BBCCC 21 | Electrolyte Ca #6BBCCC 22 | Electrolyte P #6BBCCC 23 | Liver-related TBil #BA2E2C 24 | Liver-related ZTT #BA2E2C 25 | Liver-related AST #BA2E2C 26 | Liver-related ALT #BA2E2C 27 | Liver-related ALP #BA2E2C 28 | Liver-related GGT #BA2E2C 29 | Other biochemical APTT #7F7F7F 30 | Other biochemical PT #7F7F7F 31 | Other biochemical Fbg #7F7F7F 32 | Other biochemical CK #7F7F7F 33 | Other biochemical LDH #7F7F7F 34 | Other biochemical CRP #7F7F7F 35 | Hematological WBC #5AA245 36 | Hematological Neutro #5AA245 37 | Hematological Eosino #5AA245 38 | Hematological Baso #5AA245 39 | Hematological Mono #5AA245 40 | Hematological Lym #5AA245 41 | Hematological RBC #5AA245 42 | Hematological Hb #5AA245 43 | Hematological Ht #5AA245 44 | Hematological MCV #5AA245 45 | Hematological MCH #5AA245 46 | Hematological MCHC #5AA245 47 | Hematological Plt #5AA245 48 | Blood pressure SBP #F3DE67 49 | Blood pressure DBP #F3DE67 50 | Blood pressure MAP #F3DE67 51 | Blood pressure PP #F3DE67 52 | Echocardiographic IVS #CE72BB 53 | Echocardiographic PW #CE72BB 54 | Echocardiographic LVDd #CE72BB 55 | Echocardiographic LVDs #CE72BB 56 | Echocardiographic LVM #CE72BB 57 | Echocardiographic LVMI #CE72BB 58 | Echocardiographic RWT #CE72BB 59 | Echocardiographic FS #CE72BB 60 | Echocardiographic EF #CE72BB 61 | Metabolic disease T2D #FFFFFF 62 | Cardiovascular disease IS #FFFFFF 63 | Cardiovascular disease CeAn #FFFFFF 64 | Cardiovascular disease MI #FFFFFF 65 | Cardiovascular disease PAD #FFFFFF 66 | Cardiovascular disease AF #FFFFFF 67 | Allergic disease Asthma #FFFFFF 68 | Allergic disease AD #FFFFFF 69 | Autoimmune disease GD #FFFFFF 70 | Autoimmune disease RA #FFFFFF 71 | Infectious disease CHB #FFFFFF 72 | Infectious disease CHC #FFFFFF 73 | Hematologic disease Anemia #FFFFFF 74 | Psychiatric disease BD #FFFFFF 75 | Psychiatric disease SCZ #FFFFFF 76 | Musculoskeletal disease AIS #FFFFFF 77 | Musculoskeletal disease Osteoporosis #FFFFFF 78 | Tumor LuCa #FFFFFF 79 | Tumor GaCa #FFFFFF 80 | Tumor EsCa #FFFFFF 81 | Tumor CoCa #FFFFFF 82 | Tumor PrCa #FFFFFF 83 | Tumor BrCa #FFFFFF 84 | Tumor EnCa #FFFFFF 85 | Other UF #FFFFFF 86 | Other Glaucoma #FFFFFF 87 | Other COPD #FFFFFF 88 | Other Epilepsy #FFFFFF 89 | Other Pollinosis #FFFFFF 90 | Other Urolithiasis #FFFFFF 91 | -------------------------------------------------------------------------------- /output/network_ct.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mkanai/ldsc-network-plot/d1dbf84064cffe335ac55042f5db04ce87df91cb/output/network_ct.png -------------------------------------------------------------------------------- /output/network_ct_1518554735.RData: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mkanai/ldsc-network-plot/d1dbf84064cffe335ac55042f5db04ce87df91cb/output/network_ct_1518554735.RData -------------------------------------------------------------------------------- /output/network_ct_1518554735.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mkanai/ldsc-network-plot/d1dbf84064cffe335ac55042f5db04ce87df91cb/output/network_ct_1518554735.pdf -------------------------------------------------------------------------------- /output/network_ct_1518554735.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mkanai/ldsc-network-plot/d1dbf84064cffe335ac55042f5db04ce87df91cb/output/network_ct_1518554735.png -------------------------------------------------------------------------------- /output/network_rg.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mkanai/ldsc-network-plot/d1dbf84064cffe335ac55042f5db04ce87df91cb/output/network_rg.png -------------------------------------------------------------------------------- /plot_network_ct.R: -------------------------------------------------------------------------------- 1 | library(dplyr) 2 | library(Hmisc) 3 | library(igraph) 4 | library(RColorBrewer) 5 | library(stringr) 6 | 7 | # constants 8 | CT_NAMES = "./Finucane2015_ct_names.txt" 9 | CT_Q_THRESHOLD = 0.01 10 | MIN_VERTEX_SIZE = 6 11 | MAX_VERTEX_SIZE = 20 12 | MAX_EDGE_WIDTH = 10 13 | 14 | ################################################################################ 15 | args = commandArgs(trailingOnly=T) 16 | if (identical(args, character(0))) { 17 | args = str_c("./input_example/", c("input_ct.txt", "traitlist.txt")) 18 | } 19 | ct_fname = args[1] 20 | traitlist_fname = args[2] 21 | 22 | ################################################################################ 23 | # load data 24 | ct = read.table(ct_fname, T, sep='\t', as.is = T) 25 | traitlist = read.table(traitlist_fname, T, sep = '\t', as.is = T, quote = '', fileEncoding='utf-8', comment.char="") 26 | 27 | ct_names = read.table(CT_NAMES, header = T, as.is = T, sep = '\t') 28 | cols = data.frame(category = c("Adrenal_Pancreas","CNS","Cardiovascular","Connective_Bone","GI","Hematopoietic","Kidney","Liver","SkeletalMuscle","Other"), 29 | color = c("#FFE014","#3B37C2","#BE5BC7","#D3FF5B","#80B5FF","#31B54A","#8A6F43","#F8480C","#FF80AE","#7F7F7F")) 30 | ct_names = merge(ct_names, cols, by = "category", all.x = T) 31 | ct_names$color = as.character(ct_names$color) 32 | 33 | ct = merge(ct, ct_names, by = "cell_type_clean") 34 | 35 | ################################################################################ 36 | # construct ct graph 37 | ct_sig = ct %>% filter(Coefficient_q < CT_Q_THRESHOLD) %>% 38 | group_by(cell_type_clean2, trait) %>% 39 | summarise(n = n(), q = min(Coefficient_q)) 40 | 41 | rel_ct = data.frame(from = ct_sig$cell_type_clean2, 42 | to = ct_sig$trait, 43 | directed = T, 44 | weight = -log10(ct_sig$q), 45 | q = ct_sig$q, 46 | type = "ct") 47 | rel_ct$weight = rel_ct$weight / max(rel_ct$weight) 48 | g = graph.data.frame(rel_ct, directed=T) 49 | 50 | ################################################################################ 51 | # layout 52 | l = layout_with_fr(g) 53 | l = norm_coords(l) 54 | V(g)$x = l[,1] 55 | V(g)$y = l[,2] 56 | 57 | 58 | ################################################################################ 59 | # vertex visual 60 | is_trait_v = V(g)$name %in% traitlist$TRAIT 61 | V(g)$color = "grey90" 62 | V(g)$color[is_trait_v] = traitlist$COLOR[match(V(g)$name[is_trait_v], traitlist$TRAIT)] 63 | V(g)$color[!is_trait_v] = ct_names$color[match(V(g)$name[!is_trait_v], ct_names$cell_type_clean2)] 64 | V(g)$shape = ifelse(is_trait_v, "circle", "square") 65 | V(g)$label = V(g)$name 66 | V(g)$label.color = "black" 67 | V(g)$label.font = ifelse(is_trait_v, 2, 4) 68 | V(g)$label.family = "sans" 69 | V(g)$label.cex = 0.7 70 | V(g)$label.degree = pi/2 71 | V(g)$size = 6 72 | 73 | ct_sig_trait_n = ct %>% filter(Coefficient_q < CT_Q_THRESHOLD) %>% group_by(trait) %>% count() 74 | n_v_cls = 16; 75 | v_cls_ct = cut2(ct_sig_trait_n$n[match(V(g)$name[is_trait_v], ct_sig_trait_n$trait)], g = n_v_cls) 76 | v_size_ct = seq(MIN_VERTEX_SIZE, MAX_VERTEX_SIZE, length.out = n_v_cls)[match(v_cls_ct, levels(v_cls_ct))] 77 | V(g)$size[is_trait_v] = v_size_ct 78 | 79 | ct_sig_cell_n = ct %>% filter(Coefficient_q < CT_Q_THRESHOLD) %>% group_by(cell_type_clean2) %>% summarise(n = length(unique(trait))) 80 | n_v_cls = 16; 81 | v_cls_ct = cut2(ct_sig_cell_n$n[match(V(g)$name[!is_trait_v], ct_sig_cell_n$cell_type_clean2)], g = n_v_cls) 82 | v_size_ct = seq(MIN_VERTEX_SIZE, MAX_VERTEX_SIZE, length.out = n_v_cls)[match(v_cls_ct, levels(v_cls_ct))] 83 | V(g)$size[!is_trait_v] = v_size_ct 84 | 85 | 86 | ################################################################################ 87 | # edge visual 88 | ct_cols = c("#F7F7F7", brewer.pal(5, "Blues"), c("seagreen", "gold", "deeppink")) 89 | edge_cls_ct = cut2(-log10(ct_sig$q), cuts = c(seq(0, -log10(0.05), length=6), seq(2, ceiling(max(-log10(ct_sig$q))), by=1))) 90 | edge_color_ct = ct_cols[match(edge_cls_ct, levels(edge_cls_ct))] 91 | 92 | edge_width = -log10(ct_sig$q) 93 | edge_width[edge_width > MAX_EDGE_WIDTH] = MAX_EDGE_WIDTH 94 | 95 | E(g)$color = edge_color_ct 96 | E(g)$width = edge_width 97 | E(g)$curved = .2 98 | E(g)$arrow.size = -log10(ct_sig$q)/4 99 | 100 | 101 | ################################################################################ 102 | # save images & graph 103 | ts = as.numeric(Sys.time()) 104 | out_fname = sprintf("./output/network_ct_%.0f", ts) 105 | png(str_c(out_fname, ".png"), width = 9, height = 9, units = "in", res = 300) 106 | plot(g, rescale = F) 107 | dev.off() 108 | 109 | pdf(str_c(out_fname, ".pdf"), width = 9, height = 9) 110 | plot(g, rescale = F) 111 | dev.off() 112 | 113 | save(list = c("g", "l"), file = str_c(out_fname, ".RData")) 114 | -------------------------------------------------------------------------------- /plot_network_rg.R: -------------------------------------------------------------------------------- 1 | library(dplyr) 2 | library(Hmisc) 3 | library(igraph) 4 | library(RColorBrewer) 5 | library(stringr) 6 | 7 | # constants 8 | RG_Q_THRESHOLD = 0.05 9 | MIN_VERTEX_SIZE = 6 10 | MAX_VERTEX_SIZE = 20 11 | MAX_EDGE_WIDTH = 10 12 | 13 | ################################################################################ 14 | args = commandArgs(trailingOnly=T) 15 | if (identical(args, character(0))) { 16 | args = str_c("./input_example/", c("input_rg.txt", "traitlist.txt")) 17 | } 18 | rg_fname = args[1] 19 | traitlist_fname = args[2] 20 | 21 | 22 | ################################################################################ 23 | # load data 24 | rg = read.table(rg_fname, T, sep='\t', as.is = T) 25 | traitlist = read.table(traitlist_fname, T, sep = '\t', as.is = T, quote = '', fileEncoding='utf-8', comment.char="") 26 | 27 | 28 | ################################################################################ 29 | # construct rg graph 30 | rg_sig = rg %>% filter(q < RG_Q_THRESHOLD) 31 | rel_rg = data.frame(from = rg_sig$p1, 32 | to = rg_sig$p2, 33 | directed = F, 34 | weight = abs(rg_sig$rg), 35 | q = rg_sig$q, 36 | type = "rg") 37 | g = graph.data.frame(rel_rg, directed=F) 38 | 39 | 40 | ################################################################################ 41 | # layout 42 | l = layout_with_fr(g) 43 | l = norm_coords(l) 44 | V(g)$x = l[,1] 45 | V(g)$y = l[,2] 46 | 47 | 48 | ################################################################################ 49 | # vertex visual 50 | V(g)$color = traitlist$COLOR[match(V(g)$name, traitlist$TRAIT)] 51 | V(g)$shape = "circle" 52 | V(g)$label = V(g)$name 53 | V(g)$label.color = "black" 54 | V(g)$label.font = 2 55 | V(g)$label.family = "sans" 56 | V(g)$label.cex = 0.7 57 | V(g)$label.degree = pi/2 58 | 59 | # duplicate lines 60 | tmp = rg 61 | tmp$p1 = rg$p2 62 | tmp$p2 = rg$p1 63 | tmp$p1_category = rg$p2_category 64 | tmp$p2_category = rg$p1_category 65 | rg_dup = rbind(rg, tmp) 66 | 67 | rg_sig_n = rg_dup %>% filter(q < RG_Q_THRESHOLD) %>% group_by(p1) %>% count() 68 | n_v_cls = diff(range(rg_sig_n$n))+1; 69 | v_cls_rg = cut2(rg_sig_n$n[match(V(g)$name, rg_sig_n$p1)], g = n_v_cls) 70 | v_size_rg = seq(MIN_VERTEX_SIZE, MAX_VERTEX_SIZE, length.out = n_v_cls)[match(v_cls_rg, levels(v_cls_rg))] 71 | V(g)$size = v_size_rg 72 | 73 | ################################################################################ 74 | # edge visual 75 | rg_cols = colorRampPalette(c("#67001F", "#B2182B", "#D6604D", "#F4A582", 76 | "#FDDBC7", "#FFFFFF", "#D1E5F0", "#92C5DE", 77 | "#4393C3", "#2166AC", "#053061"))(200) 78 | edge_cls_rg = cut2(rg_sig$rg, cuts = seq(-1, 1, length.out = 201)) 79 | edge_color_rg = rg_cols[match(edge_cls_rg, levels(edge_cls_rg))] 80 | 81 | edge_width = -log10(rg_sig$q) 82 | edge_width[edge_width > MAX_EDGE_WIDTH] = MAX_EDGE_WIDTH 83 | 84 | E(g)$color = edge_color_rg 85 | E(g)$width = edge_width 86 | E(g)$curved = .2 87 | E(g)$arrow.size = .5 88 | 89 | 90 | ################################################################################ 91 | # save images & graph 92 | 93 | ts = as.numeric(Sys.time()) 94 | out_fname = sprintf("./output/network_rg_%.0f", ts) 95 | png(str_c(out_fname, ".png"), width = 9, height = 9, units = "in", res = 300) 96 | plot(g, rescale = F) 97 | dev.off() 98 | 99 | pdf(str_c(out_fname, ".pdf"), width = 9, height = 9) 100 | plot(g, rescale = F) 101 | dev.off() 102 | 103 | save(list = c("g", "l"), file = str_c(out_fname, ".RData")) 104 | 105 | --------------------------------------------------------------------------------