Analyze 5 TCGA datasets using PCAmodel with 10 PCs
2020-11-03
Contents
1 Setup
This vignette is running the same analysis as TCGA_5studies.Rmd but with the PCAmodel
with a fewer clusters.
1.1 Load packages
suppressPackageStartupMessages({
library(PCAGenomicSignatures)
library(dplyr)
library(ggplot2)
})1.2 TCGA datasets
load("data/TCGA_validationDatasets.rda")
datasets <- TCGA_validationDatasets[1:5]1.3 PCAmodel
This PCAmodel used the same 536 studies as the default model, but only top 10 PCs were collected for model building.
PCAmodel
## class: PCAGenomicSignatures
## dim: 13934 2382
## metadata(6): cluster size ... MeSH_freq updateNote
## assays(1): model
## rownames(13934): CASKIN1 DDX3Y ... CTC-457E21.9 AC007966.1
## rowData names(0):
## colnames(2382): RAV1 RAV2 ... RAV2381 RAV2382
## colData names(4): RAV studies silhouetteWidth gsea
## trainingData(2): PCAsummary MeSH
## trainingData names(536): DRP000987 SRP059172 ... SRP166108 SRP188526
updateNote(PCAmodel)
## [1] "536 refine.bio studies/ use top 10 PCs/ top 90% varying genes"2 Multi-comparison (only TCGA)
2.1 heatmapTable (all)
# This process takes little time due to the size of datasets.
val_all <- validate(datasets, PCAmodel)
heatmapTable(val_all, scoreCutoff = 0.68) 
It seems like RAV117 and RAV435 are specific to BRCA while RAV585 is strongly associated with colon/rectal cancers. It’s subtle but potentially RAV345/655 are associated both BRCA and UCEC (uterine cancer).
2.2 heatmapTable - COAD
val_coad <- validate(datasets[["COAD"]], PCAmodel)
heatmapTable(val_coad) 
2.3 heatmapTable - READ
val_read <- validate(datasets[["READ"]], PCAmodel)
heatmapTable(val_read) 
2.4 heatmapTable - BRCA
val_brca <- validate(datasets[["BRCA"]], PCAmodel)
heatmapTable(val_brca)
heatmapTable(val_brca, num.out = 7) 
2.5 MeSH terms and associated studies
2.5.1 BRCA-associated
RAV117 and RAV435 are specific to BRCA.
drawWordcloud(PCAmodel, 117)
drawWordcloud(PCAmodel, 435)
ind <- 117
findStudiesInCluster(PCAmodel, ind, studyTitle = TRUE)
## studyName
## 279 ERP016798
## 977 SRP023262
## 4275 SRP111343
## title
## 279 Whole transcriptome profiling of 63 breast cancer tumours
## 977 A shared transcriptional program in early breast neoplasias despite genetic and clinical distinctions
## 4275 RNAseq analysis of chemotherapy and radiation therapy-naïve breast tumors
subsetEnrichedPathways(PCAmodel, ind) %>% as.data.frame
## RAV117
## Up_1 SMID_BREAST_CANCER_BASAL_DN
## Up_2 SMID_BREAST_CANCER_LUMINAL_B_UP
## Up_3 VANTVEER_BREAST_CANCER_ESR1_UP
## Up_4 DOANE_BREAST_CANCER_ESR1_UP
## Up_5 LIEN_BREAST_CARCINOMA_METAPLASTIC_VS_DUCTAL_DN
## Up_6 CHARAFE_BREAST_CANCER_LUMINAL_VS_BASAL_UP
## Up_7 SMID_BREAST_CANCER_RELAPSE_IN_BONE_UP
## Up_8 CHARAFE_BREAST_CANCER_LUMINAL_VS_MESENCHYMAL_UP
## Up_9 SMID_BREAST_CANCER_RELAPSE_IN_BRAIN_DN
## Up_10 POOLA_INVASIVE_BREAST_CANCER_DN
ind <- 435
findStudiesInCluster(PCAmodel, ind, studyTitle = TRUE)
## studyName
## 773 SRP014428
## 4275 SRP111343
## 5720 SRP158730
## 5843 SRP163173
## 5960 SRP169094
## title
## 773 Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells
## 4275 RNAseq analysis of chemotherapy and radiation therapy-naïve breast tumors
## 5720 Separation of breast cancer and organ microenvironment transcriptomes in metastases
## 5843 Integrative epigenetic taxonomy of primary prostate cancer [RNA-Seq]
## 5960 On-Treatment Biomarkers Improve Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer
subsetEnrichedPathways(PCAmodel, ind) %>% as.data.frame
## RAV435
## Up_1 CHARAFE_BREAST_CANCER_LUMINAL_VS_MESENCHYMAL_UP
## Up_2 LIEN_BREAST_CARCINOMA_METAPLASTIC_VS_DUCTAL_DN
## Up_3 DOANE_BREAST_CANCER_ESR1_UP
## Up_4 CHARAFE_BREAST_CANCER_LUMINAL_VS_BASAL_UP
## Up_5 LIM_MAMMARY_STEM_CELL_DN
## Up_6 ROSTY_CERVICAL_CANCER_PROLIFERATION_CLUSTER
## Up_7 COLDREN_GEFITINIB_RESISTANCE_DN
## Up_8 DOANE_BREAST_CANCER_CLASSES_UP
## Up_9 VANTVEER_BREAST_CANCER_ESR1_UP
## Up_10 SMID_BREAST_CANCER_RELAPSE_IN_BONE_UP2.5.2 COAD/READ-associated
ind <- 585
findStudiesInCluster(PCAmodel, ind, studyTitle = TRUE)## studyName
## 1074 SRP029880
## 3037 SRP077046
## 5411 SRP149847
## title
## 1074 Gene expression profiling study by RNA-seq in colorectal cancer
## 3037 A functional genomics predictive network model identifies regulators of inflammatory bowel disease: Mount Sinai Hospital (MSH) Population Specimen Collection and Profiling of Inflammatory Bowel Disease
## 5411 Differences in tissue immune cell populations following hematopoietic stem cell transplantation in Crohn's disease patientssubsetEnrichedPathways(PCAmodel, ind) %>% as.data.frame## RAV585
## Up_1 SCHUETZ_BREAST_CANCER_DUCTAL_INVASIVE_UP
## Up_2 VECCHI_GASTRIC_CANCER_ADVANCED_VS_EARLY_UP
## Up_3 LIM_MAMMARY_STEM_CELL_UP
## Up_4 CHARAFE_BREAST_CANCER_LUMINAL_VS_MESENCHYMAL_DN
## Up_5 ANASTASSIOU_MULTICANCER_INVASIVENESS_SIGNATURE
## Up_6 LINDGREN_BLADDER_CANCER_CLUSTER_2B
## Up_7 PICCALUGA_ANGIOIMMUNOBLASTIC_LYMPHOMA_UP
## Up_8 SMID_BREAST_CANCER_NORMAL_LIKE_UP
## Up_9 MCLACHLAN_DENTAL_CARIES_UP
## Up_10 TURASHVILI_BREAST_LOBULAR_CARCINOMA_VS_DUCTAL_NORMAL_UPdrawWordcloud(PCAmodel, ind)
3 Multi-comparison (TCGA + others)
3.1 heatmapTable (all)
Here, we added SLE-WB microarray dataset and 4 colon cancer microarray dataasets
to 5 TCGA dataset and scoreCutoff is set to 0.68 instead of the default 0.7.
## Warning: NaNs produced
## Warning: NaNs producednames(new_datasets)
## [1] "COAD" "BRCA" "LUAD" "READ" "UCEC" "SLE"
## [7] "GSE14095" "GSE17536" "GSE2109" "GSE39582"
Based on this multi-datasets validation table,
- RAV13 and RAV765 are SLE-specific
- Different from 20PC-PCAmodels, which has a RAV associated with both COAD and READ,
this 10PC PCAmodel doesn’t seem to have a RAV for both COAD/READ.
3.2 MeSH terms and associated studies
3.2.1 SLE-associated
ind <- 13
findStudiesInCluster(PCAmodel, ind, studyTitle = TRUE)
## studyName
## 16 DRP001953
## 524 ERP114104
## 1749 SRP051848
## 2024 SRP059039
## 3329 SRP089814
## 4244 SRP110609
## 4533 SRP118733
## 4977 SRP132018
## 5135 SRP136057
## 5140 SRP136108
## 5452 SRP150419
## 5462 SRP150595
## title
## 16 Interactive Transcriptome Analysis of Malaria Patients and Infecting Plasmodium falciparum in Indonesia
## 524 Altered Gene Expression in Antipsychotic Induced Weight Gain
## 1749 Gene Networks Specific for Innate Immunity Define Post-traumatic Stress Disorder [RNA-Seq]
## 2024 Elucidating the etiology and molecular pathogenicity of infectious diarrhea by high throughput RNA sequencing
## 3329 Differentially Expressed Gene Transcripts Using RNA Sequencing from the Blood of Immunosuppressed Kidney Allograft Recipients
## 4244 RNA-sequencing analysis of response to P.falciparum infection in Fulani and Mossi ethnic groups, Burkina Faso
## 4533 Transcriptomic analysis of Multiple Myeloma bone marrow microenvironment
## 4977 In-vitro stimulation of healthy donor blood with IL-3 cytokine
## 5135 Whole Blood Transcriptome Profiling in Juvenile Idiopathic Arthritis and Inflammatory Bowel Disease
## 5140 RNA-seq of nine primary human cell types exposed in vitro to methylprednisolone
## 5452 Haemopedia: Human Haematopoietic Gene Expression
## 5462 Homo sapiens Transcriptome or Gene expression
subsetEnrichedPathways(PCAmodel, ind) %>% as.data.frame
## RAV13
## Up_1 KEGG_RIBOSOME
## Up_2 REACTOME_SRP_DEPENDENT_COTRANSLATIONAL_PROTEIN_TARGETING_TO_MEMBRANE
## Up_3 REACTOME_EUKARYOTIC_TRANSLATION_ELONGATION
## Up_4 REACTOME_SELENOAMINO_ACID_METABOLISM
## Up_5 REACTOME_RRNA_PROCESSING
## Up_6 REACTOME_TRANSLATION
## Up_7 MANALO_HYPOXIA_DN
## Up_8 CAIRO_HEPATOBLASTOMA_CLASSES_UP
## Up_9 PUJANA_BRCA2_PCC_NETWORK
## Up_10 WONG_EMBRYONIC_STEM_CELL_CORE
drawWordcloud(PCAmodel, ind)
ind <- 765
findStudiesInCluster(PCAmodel, ind, studyTitle = TRUE)
## studyName
## 1740 SRP051688
## 1904 SRP056840
## 2209 SRP062966
## 2699 SRP071965
## 4031 SRP105369
## 4914 SRP131037
## 5462 SRP150595
## 5481 SRP150872
## title
## 1740 A Cell-based Systems Biology Assessment of Human Blood to Monitor Immune Responses After Influenza Vaccination
## 1904 Renal systems biology of patients with systemic inflammatory response syndrome
## 2209 SLE lupus RNA-seq
## 2699 A blood RNA signature for tuberculosis disease risk: a prospective cohort study
## 4031 Transcriptome analysis of G protein-coupled receptors in distinct genetic subgroups of acute myeloid leukemia: identification of potential disease-specific targets
## 4914 Using Next-Generation Sequencing Transcriptomics to Determine Markers of Post-traumatic Symptoms - preliminary findings from a post-deployment cohort
## 5462 Homo sapiens Transcriptome or Gene expression
## 5481 Discovering in vivo cytokine eQTL interactions from a lupus clinical trial
subsetEnrichedPathways(PCAmodel, ind) %>% as.data.frame
## RAV765
## Up_1 REACTOME_NEUTROPHIL_DEGRANULATION
## Up_2 THEILGAARD_NEUTROPHIL_AT_SKIN_WOUND_DN
## Up_3 ALTEMEIER_RESPONSE_TO_LPS_WITH_MECHANICAL_VENTILATION
## Up_4 VERHAAK_AML_WITH_NPM1_MUTATED_UP
## Up_5 VERHAAK_GLIOBLASTOMA_MESENCHYMAL
## Up_6 HAHTOLA_MYCOSIS_FUNGOIDES_CD4_UP
## Up_7 BROWN_MYELOID_CELL_DEVELOPMENT_UP
## Up_8 TAKEDA_TARGETS_OF_NUP98_HOXA9_FUSION_8D_DN
## Up_9 LENAOUR_DENDRITIC_CELL_MATURATION_DN
## Up_10 SMIRNOV_CIRCULATING_ENDOTHELIOCYTES_IN_CANCER_UP
drawWordcloud(PCAmodel, ind)