Contents
RAV184
ind <- 184
getRAVInfo(RAVmodel_C2, ind)
## $clusterSize
## [1] 22
##
## $silhouetteWidth
## [1] 0.01
##
## $enrichedPathways
## [1] 15
##
## $members
## studyName PC Variance explained (%)
## 1 ERP013206 1 45.63
## 2 ERP022034 1 12.46
## 3 ERP023424 1 18.07
## 4 ERP106487 1 29.73
## 5 ERP111913 3 5.21
## 6 SRP001540 1 15.28
## 7 SRP015640 1 44.23
## 8 SRP059057 1 22.50
## 9 SRP059557 1 21.22
## 10 SRP071965 1 12.20
## 11 SRP073061 1 43.22
## 12 SRP073813 1 22.14
## 13 SRP074198 1 23.64
## 14 SRP075449 1 41.47
## 15 SRP076944 1 28.55
## 16 SRP099844 1 17.28
## 17 SRP115956 1 36.78
## 18 SRP140795 1 44.72
## 19 SRP144003 1 44.49
## 20 SRP149978 1 11.87
## 21 SRP174668 1 61.09
## 22 SRP182096 1 51.58
findStudiesInCluster(RAVmodel_C2, ind, studyTitle = TRUE)
## studyName PC Variance explained (%)
## 1 ERP013206 1 45.63
## 2 ERP022034 1 12.46
## 3 ERP023424 1 18.07
## 4 ERP106487 1 29.73
## 5 ERP111913 3 5.21
## 6 SRP001540 1 15.28
## 7 SRP015640 1 44.23
## 8 SRP059057 1 22.50
## 9 SRP059557 1 21.22
## 10 SRP071965 1 12.20
## 11 SRP073061 1 43.22
## 12 SRP073813 1 22.14
## 13 SRP074198 1 23.64
## 14 SRP075449 1 41.47
## 15 SRP076944 1 28.55
## 16 SRP099844 1 17.28
## 17 SRP115956 1 36.78
## 18 SRP140795 1 44.72
## 19 SRP144003 1 44.49
## 20 SRP149978 1 11.87
## 21 SRP174668 1 61.09
## 22 SRP182096 1 51.58
## title
## 1 Whole transcriptome profiling of Esophageal adenocarcinoma and Barrett's
## 2 RNA sequencing of pancreatic adenocarcinoma (PDAC) xenograft samples
## 3 Transcriptional profiling of Th1, Th2, Th9, and Th17 T cell clones
## 4 RNA sequencing of purified intestinal epithelial cells from paediatric biopsies including Inflammatory Bowel Disease and healthy controls
## 5 RNA-seq from knee and hip osteoarthritis cartilage
## 6 Understaning mechanisms underlying human gene expression variation with RNA sequencing
## 7 Comprehensive comparative analysis of RNA sequencing methods for degraded or low input samples
## 8 Transcriptome analysis of CD4+ T cells reveals imprint of BACH2 and IFN? regulation
## 9 Salivary adenoid cystic carcinoma RNA-seq
## 10 A blood RNA signature for tuberculosis disease risk: a prospective cohort study
## 11 GEO accession GSE80098 is currently private and is scheduled to be released on Jun 01, 2016.
## 12 RNA-sequencing of human post-mortem brain tissues
## 13 Gene expression profiling of melanoma cell lines by RNASeq
## 14 Nuclear Surveillance of long intervening noncoding RNA
## 15 RNA-seq transcriptome analysis of epidermal CD8+CD103+CD49a+ and CD8+CD103+CD49- T cells from healthy human skin
## 16 Chemoprevention with COX2 and EGFR inhibition in FAP patients: mRNA signatures of duodenal neoplasia
## 17 Sex-specific Transcriptional Signatures in Human Depression
## 18 Using RNA sequencing to examine age-dependent skeletal muscle transcriptome response to bed rest-induced atrophy, and age independent disuse-induced insulin resistance
## 19 RNA-seq from HNSCC and melanoma populations
## 20 RNA-seq of HIV bnAb and control individuals PBMC
## 21 NASH
## 22 Transcriptomic profiling of peripheral blood NK cells of chronic HBV, HCV and HIV patients
subsetEnrichedPathways(RAVmodel_C2, ind, n = 20, both = TRUE, include_nes = TRUE) %>% as.data.frame
## RAV184.Description RAV184.NES
## Up_1 KEGG_PROTEASOME 4.460750
## Up_2 PARK_APL_PATHOGENESIS_DN 4.446250
## Up_3 MALONEY_RESPONSE_TO_17AAG_DN 4.426264
## Up_4 SCHLOSSER_MYC_TARGETS_AND_SERUM_RESPONSE_DN 4.270178
## Up_5 TIEN_INTESTINE_PROBIOTICS_6HR_UP 4.149628
## Up_6 CROONQUIST_STROMAL_STIMULATION_UP 4.100296
## Up_7 YAO_TEMPORAL_RESPONSE_TO_PROGESTERONE_CLUSTER_8 4.098125
## Up_8 MODY_HIPPOCAMPUS_PRENATAL 4.075175
## Up_9 YU_MYC_TARGETS_UP 4.060458
## Up_10 ZHAN_V2_LATE_DIFFERENTIATION_GENES 4.040911
## Up_11 JAZAERI_BREAST_CANCER_BRCA1_VS_BRCA2_UP 4.030009
## Up_12 PID_IL2_1PATHWAY 3.889647
## Up_13 LUI_THYROID_CANCER_PAX8_PPARG_DN 3.824361
## Up_14 LUI_THYROID_CANCER_CLUSTER_3 3.731501
## Up_15 ZHAN_LATE_DIFFERENTIATION_GENES_UP 3.666224
## Up_16 <NA> NA
## Up_17 <NA> NA
## Up_18 <NA> NA
## Up_19 <NA> NA
## Up_20 <NA> NA
## Down_1 ZHAN_LATE_DIFFERENTIATION_GENES_UP 3.666224
## Down_2 LUI_THYROID_CANCER_CLUSTER_3 3.731501
## Down_3 LUI_THYROID_CANCER_PAX8_PPARG_DN 3.824361
## Down_4 PID_IL2_1PATHWAY 3.889647
## Down_5 JAZAERI_BREAST_CANCER_BRCA1_VS_BRCA2_UP 4.030009
## Down_6 ZHAN_V2_LATE_DIFFERENTIATION_GENES 4.040911
## Down_7 YU_MYC_TARGETS_UP 4.060458
## Down_8 MODY_HIPPOCAMPUS_PRENATAL 4.075175
## Down_9 YAO_TEMPORAL_RESPONSE_TO_PROGESTERONE_CLUSTER_8 4.098125
## Down_10 CROONQUIST_STROMAL_STIMULATION_UP 4.100296
## Down_11 TIEN_INTESTINE_PROBIOTICS_6HR_UP 4.149628
## Down_12 SCHLOSSER_MYC_TARGETS_AND_SERUM_RESPONSE_DN 4.270178
## Down_13 MALONEY_RESPONSE_TO_17AAG_DN 4.426264
## Down_14 PARK_APL_PATHOGENESIS_DN 4.446250
## Down_15 KEGG_PROTEASOME 4.460750
## Down_16 <NA> NA
## Down_17 <NA> NA
## Down_18 <NA> NA
## Down_19 <NA> NA
## Down_20 <NA> NA
subsetEnrichedPathways(RAVmodel_PLIERpriors, ind, n = 20, both = TRUE, include_nes = TRUE) %>% as.data.frame
## RAV184.Description
## Up_1 REACTOME_AUTODEGRADATION_OF_THE_E3_UBIQUITIN_LIGASE_COP1
## Up_2 REACTOME_CDK_MEDIATED_PHOSPHORYLATION_AND_REMOVAL_OF_CDC6
## Up_3 REACTOME_FORMATION_OF_THE_TERNARY_COMPLEX_AND_SUBSEQUENTLY_THE_43S_COMPLEX
## Up_4 MIPS_60S_RIBOSOMAL_SUBUNIT_CYTOPLASMIC
## Up_5 MIPS_PA700_20S_PA28_COMPLEX
## Up_6 KEGG_PROTEASOME
## Up_7 PID_TGFBRPATHWAY
## Up_8 PID_IL2_1PATHWAY
## Up_9 MIPS_40S_RIBOSOMAL_SUBUNIT_CYTOPLASMIC
## Up_10 REACTOME_DEADENYLATION_DEPENDENT_MRNA_DECAY
## Up_11 <NA>
## Up_12 <NA>
## Up_13 <NA>
## Up_14 <NA>
## Up_15 <NA>
## Up_16 <NA>
## Up_17 <NA>
## Up_18 <NA>
## Up_19 <NA>
## Up_20 <NA>
## Down_1 REACTOME_DEADENYLATION_DEPENDENT_MRNA_DECAY
## Down_2 MIPS_40S_RIBOSOMAL_SUBUNIT_CYTOPLASMIC
## Down_3 PID_IL2_1PATHWAY
## Down_4 PID_TGFBRPATHWAY
## Down_5 KEGG_PROTEASOME
## Down_6 MIPS_PA700_20S_PA28_COMPLEX
## Down_7 MIPS_60S_RIBOSOMAL_SUBUNIT_CYTOPLASMIC
## Down_8 REACTOME_FORMATION_OF_THE_TERNARY_COMPLEX_AND_SUBSEQUENTLY_THE_43S_COMPLEX
## Down_9 REACTOME_AUTODEGRADATION_OF_THE_E3_UBIQUITIN_LIGASE_COP1
## Down_10 REACTOME_CDK_MEDIATED_PHOSPHORYLATION_AND_REMOVAL_OF_CDC6
## Down_11 <NA>
## Down_12 <NA>
## Down_13 <NA>
## Down_14 <NA>
## Down_15 <NA>
## Down_16 <NA>
## Down_17 <NA>
## Down_18 <NA>
## Down_19 <NA>
## Down_20 <NA>
## RAV184.NES
## Up_1 4.604385
## Up_2 4.604385
## Up_3 4.432545
## Up_4 4.369468
## Up_5 4.368422
## Up_6 4.182978
## Up_7 3.796751
## Up_8 3.768644
## Up_9 3.720400
## Up_10 3.578090
## Up_11 NA
## Up_12 NA
## Up_13 NA
## Up_14 NA
## Up_15 NA
## Up_16 NA
## Up_17 NA
## Up_18 NA
## Up_19 NA
## Up_20 NA
## Down_1 3.578090
## Down_2 3.720400
## Down_3 3.768644
## Down_4 3.796751
## Down_5 4.182978
## Down_6 4.368422
## Down_7 4.369468
## Down_8 4.432545
## Down_9 4.604385
## Down_10 4.604385
## Down_11 NA
## Down_12 NA
## Down_13 NA
## Down_14 NA
## Down_15 NA
## Down_16 NA
## Down_17 NA
## Down_18 NA
## Down_19 NA
## Down_20 NA
drawWordcloud(RAVmodel_C2, ind)
RAV312
ind <- 312
getRAVInfo(RAVmodel_C2, ind)
## $clusterSize
## [1] 24
##
## $silhouetteWidth
## [1] -0.07
##
## $enrichedPathways
## [1] 121
##
## $members
## studyName PC Variance explained (%)
## 1 ERP021686 5 1.17
## 2 ERP105501 7 1.68
## 3 ERP109255 6 1.34
## 4 SRP010181 4 2.45
## 5 SRP014320 14 1.18
## 6 SRP026042 4 3.78
## 7 SRP041036 12 0.95
## 8 SRP051472 5 1.90
## 9 SRP051848 3 2.12
## 10 SRP086078 7 1.43
## 11 SRP094424 9 0.67
## 12 SRP095272 3 1.64
## 13 SRP096016 1 26.30
## 14 SRP100928 5 1.46
## 15 SRP108121 8 1.82
## 16 SRP110609 2 3.87
## 17 SRP125001 4 4.08
## 18 SRP126485 18 0.72
## 19 SRP151763 2 4.93
## 20 SRP162023 4 2.26
## 21 SRP163027 5 2.58
## 22 SRP173388 7 0.71
## 23 SRP174638 5 1.80
## 24 SRP182694 4 1.46
findStudiesInCluster(RAVmodel_C2, ind, studyTitle = TRUE)
## studyName PC Variance explained (%)
## 1 ERP021686 5 1.17
## 2 ERP105501 7 1.68
## 3 ERP109255 6 1.34
## 4 SRP010181 4 2.45
## 5 SRP014320 14 1.18
## 6 SRP026042 4 3.78
## 7 SRP041036 12 0.95
## 8 SRP051472 5 1.90
## 9 SRP051848 3 2.12
## 10 SRP086078 7 1.43
## 11 SRP094424 9 0.67
## 12 SRP095272 3 1.64
## 13 SRP096016 1 26.30
## 14 SRP100928 5 1.46
## 15 SRP108121 8 1.82
## 16 SRP110609 2 3.87
## 17 SRP125001 4 4.08
## 18 SRP126485 18 0.72
## 19 SRP151763 2 4.93
## 20 SRP162023 4 2.26
## 21 SRP163027 5 2.58
## 22 SRP173388 7 0.71
## 23 SRP174638 5 1.80
## 24 SRP182694 4 1.46
## title
## 1 Mammary fat pad and intraductal xenografts with both DCIS.COM-lacZ and DCIS.COM-SOX11 cell lines to characterize the changes in mRNA expression in invasive cancers.
## 2 RNA-Seq analysis of human osteoarthritic intact cartilage following total knee replacement
## 3 RNA-seq of hepatic biopsies taken from patients with chronic liver disease presenting with different aetiologies (HCV, FLD) and fibrosis stages.
## 4 Derivation of HLA types from shotgun sequence datasets
## 5 GSE33480: RNA-seq from ENCODE/Caltech
## 6 Environmental factors transmitted by aryl hydrocarbon receptor influence severity of psoriatic skin inflammation [RNA-Seq]
## 7 RNAseq to investigate transcriptional changes in human MM cell lines due to panobinostat, 5-Azacytidine, panobinostat+5-Azacytidine or n-methyl-2-pyrroldine (NMP) treatments.
## 8 Induction of circular RNA in fetal heart development recapitulated in in vitro differentiation
## 9 Gene Networks Specific for Innate Immunity Define Post-traumatic Stress Disorder [RNA-Seq]
## 10 Airway epithelial cells from smokers treated with myo-inositol or placebo
## 11 Induced Pluripotent Stem Cell Differentiation Provides an Approach for Functional Validation of GWAS Variants in Metabolic Disease
## 12 Analysis of parent-of-origin bias in gene expression levels
## 13 Germline competency of human embryonic stem cells depends on eomesodermin
## 14 Lineage specific differentiation is influenced by state of human pluripotency [RNA-seq]
## 15 A Signaling and Transcription Architecture for the Specification of Human Germ Cell Lineage
## 16 RNA-sequencing analysis of response to P.falciparum infection in Fulani and Mossi ethnic groups, Burkina Faso
## 17 Multi-Omic Molecular Profiling of Lung Cancer Risk in Chronic Obstructive Pulmonary Disease
## 18 Metformin Regulates Metabolic and Non-Metabolic Pathways in Skeletal Muscle and Subcutaneous Adipose Tissues of Older Adults
## 19 Integrated analysis of genetic variants regulating retinal transcriptome (GREx) identifies genes underlying age-related macular degeneration
## 20 HantavaxTM vaccinated peripheral blood mononuclear cells (PBMCs) and sera analyses by transcriptomic and metabolomic profilings
## 21 Differential analysis of gene expression profiles in peripheral blood cells of patients with cervical lesions
## 22 Single-cell RNA-sequencing of Herpes simplex virus 1-infected cells identifies NRF2 activation as an antiviral program
## 23 Cell type-specific immune phenotypes predict loss of insulin secretion in new-onset type 1 diabetes
## 24 Point mutations in the PDX1 transactivation domain impair human ß-cell development and function (RNA-Seq)
subsetEnrichedPathways(RAVmodel_C2, ind, n = 20, both = TRUE, include_nes = TRUE) %>% as.data.frame
## RAV312.Description RAV312.NES
## Up_1 HAMAI_APOPTOSIS_VIA_TRAIL_UP 4.116840
## Up_2 MILI_PSEUDOPODIA_HAPTOTAXIS_UP 3.928017
## Up_3 RODRIGUES_THYROID_CARCINOMA_ANAPLASTIC_UP 3.685763
## Up_4 RODRIGUES_THYROID_CARCINOMA_POORLY_DIFFERENTIATED_UP 3.648490
## Up_5 SHEN_SMARCA2_TARGETS_UP 3.646211
## Up_6 ZHANG_BREAST_CANCER_PROGENITORS_UP 3.588981
## Up_7 PYEON_CANCER_HEAD_AND_NECK_VS_CERVICAL_UP 3.581567
## Up_8 FLORIO_NEOCORTEX_BASAL_RADIAL_GLIA_DN 3.580746
## Up_9 SENGUPTA_NASOPHARYNGEAL_CARCINOMA_WITH_LMP1_UP 3.536564
## Up_10 GABRIELY_MIR21_TARGETS 3.499799
## Up_11 SENGUPTA_NASOPHARYNGEAL_CARCINOMA_UP 3.451462
## Up_12 BIDUS_METASTASIS_UP 3.365283
## Up_13 DACOSTA_UV_RESPONSE_VIA_ERCC3_COMMON_DN 3.339897
## Up_14 LEE_EARLY_T_LYMPHOCYTE_UP 3.315436
## Up_15 FISCHER_G2_M_CELL_CYCLE 3.292662
## Up_16 DACOSTA_UV_RESPONSE_VIA_ERCC3_DN 3.281532
## Up_17 SOTIRIOU_BREAST_CANCER_GRADE_1_VS_3_UP 3.229319
## Up_18 ROSTY_CERVICAL_CANCER_PROLIFERATION_CLUSTER 3.221414
## Up_19 FERREIRA_EWINGS_SARCOMA_UNSTABLE_VS_STABLE_UP 3.208904
## Up_20 KOBAYASHI_EGFR_SIGNALING_24HR_DN 3.184196
## Down_1 NIKOLSKY_BREAST_CANCER_16P13_AMPLICON -2.964416
## Down_2 MARTENS_TRETINOIN_RESPONSE_UP -2.716566
## Down_3 LU_EZH2_TARGETS_UP -2.641859
## Down_4 GINESTIER_BREAST_CANCER_ZNF217_AMPLIFIED_DN -2.488707
## Down_5 KIM_ALL_DISORDERS_DURATION_CORR_DN -2.480843
## Down_6 HAMAI_APOPTOSIS_VIA_TRAIL_DN -2.468456
## Down_7 MIKKELSEN_IPS_LCP_WITH_H3K4ME3 -2.384463
## Down_8 ENK_UV_RESPONSE_KERATINOCYTE_UP -2.354719
## Down_9 BANDRES_RESPONSE_TO_CARMUSTIN_MGMT_48HR_DN -2.352448
## Down_10 MIKKELSEN_MCV6_HCP_WITH_H3K27ME3 -2.250054
## Down_11 DACOSTA_UV_RESPONSE_VIA_ERCC3_UP -2.182229
## Down_12 KIM_ALL_DISORDERS_OLIGODENDROCYTE_NUMBER_CORR_UP -2.175614
## Down_13 GRAESSMANN_RESPONSE_TO_MC_AND_DOXORUBICIN_UP -2.102170
## Down_14 MARTENS_BOUND_BY_PML_RARA_FUSION -2.053189
## Down_15 MIKKELSEN_MEF_HCP_WITH_H3K27ME3 -1.973955
## Down_16 MILI_PSEUDOPODIA_HAPTOTAXIS_DN -1.920583
## Down_17 MARTENS_TRETINOIN_RESPONSE_DN -1.860731
## Down_18 CAIRO_HEPATOBLASTOMA_CLASSES_UP 1.895524
## Down_19 CHARAFE_BREAST_CANCER_LUMINAL_VS_BASAL_DN 1.978676
## Down_20 CHICAS_RB1_TARGETS_SENESCENT 2.046994
subsetEnrichedPathways(RAVmodel_PLIERpriors, ind, n = 20, both = TRUE, include_nes = TRUE) %>% as.data.frame
## RAV312.Description RAV312.NES
## Up_1 REACTOME_CELL_CYCLE_MITOTIC 2.578839
## Up_2 REACTOME_MITOTIC_M_M_G1_PHASES 2.473870
## Up_3 REACTOME_DNA_REPLICATION 2.443871
## Up_4 REACTOME_CELL_CYCLE 2.296212
## Up_5 <NA> NA
## Up_6 <NA> NA
## Up_7 <NA> NA
## Up_8 <NA> NA
## Up_9 <NA> NA
## Up_10 <NA> NA
## Up_11 <NA> NA
## Up_12 <NA> NA
## Up_13 <NA> NA
## Up_14 <NA> NA
## Up_15 <NA> NA
## Up_16 <NA> NA
## Up_17 <NA> NA
## Up_18 <NA> NA
## Up_19 <NA> NA
## Up_20 <NA> NA
## Down_1 REACTOME_CELL_CYCLE 2.296212
## Down_2 REACTOME_DNA_REPLICATION 2.443871
## Down_3 REACTOME_MITOTIC_M_M_G1_PHASES 2.473870
## Down_4 REACTOME_CELL_CYCLE_MITOTIC 2.578839
## Down_5 <NA> NA
## Down_6 <NA> NA
## Down_7 <NA> NA
## Down_8 <NA> NA
## Down_9 <NA> NA
## Down_10 <NA> NA
## Down_11 <NA> NA
## Down_12 <NA> NA
## Down_13 <NA> NA
## Down_14 <NA> NA
## Down_15 <NA> NA
## Down_16 <NA> NA
## Down_17 <NA> NA
## Down_18 <NA> NA
## Down_19 <NA> NA
## Down_20 <NA> NA
drawWordcloud(RAVmodel_C2, ind)
