Latest revision as of 13:55, 13 April 2023

Resources

TCGA to Complete its Final Analysis: the PanCanAtlas, Data download from gdc portal.
- TCGA pan-cancer 分析, 文献阅读 The Cancer Genome Atlas Pan-Cancer analysis project
- TCGA数据库(GDC Data User's Guide)学习
- Googole: pancanatlas batch effect correction. TCGA Batch Effects Viewer, Batch Effects Normalized miRNA data
The cBioPortal for Cancer Genomics provides visualization, analysis and download of large-scale cancer genomics data sets.
TCGA癌症中英文对照
https://github.com/cBioPortal/cbioportal
https://gdac.broadinstitute.org/
- RCTGA::downloadTCGA() uses the above website. * Understanding TCGA mRNA Level3 analysis results files from FireBrowse
TCGA实战大全
TCGA学习01：数据下载与整理, TCGA下载和提取临床数据, TCGA临床数据整理, TCGA样本命名详解. TCGA数据下载与ID转换
UCSCXenaTools
- UCSCXenaTools: Retrieve Gene Expression and Clinical Information from UCSC Xena for Survival Analysis
- https://docs.ropensci.org/UCSCXenaTools/
- Several hubs are used. For example TCGA Hub is from https://tcga.xenahubs.net/
LinkedOmics. TCGA-KIRC
TCGA Pancancer Clinical Data RData & tsv.
cBioPortal FAQs

R packages

cBioPortalData
TCGAWorkflow package. Rank 7/29. It suggests
- TCGAbiolinksGUI.data (rank 2/416) Why?
- TCGAbiolinks package (rank 91/2083) & TCGAbiolinksGUI
- curatedTCGAData (rank 34/416)
- TCGAWorkflowData (rank 54/416)
- HarmonizedTCGAData (276/416)
- RTCGAToolbox, Downloading RNAseq, me450k and clinical data from TCGA melanoma tumours (rank 251/2083).
RTCGA.RPPA package. RPPA = reverse phase protein array.
cgdsr package
- Tutorial: retrieve full TCGA datasets from cBioportal with R
- 生物信息学生R入门教程
TCGA processed RNA-Seq data (GSE62944) as a SummarizedExperiment
- This was used in GSEABenchmarkeR package vignette
Comparison of RTCGA, UCSCXenaTools (CRAN), TCGAbiolinks & curatedTCGAData
- Differences between RTCGA and TCGAbiolinks data

PanCancer atlas vs Firehose legacy

What are TCGA Firehose Legacy datasets and how do they compare to the publication-associated datasets and the PanCancer Atlas datasets?

Relationship of TCGA, Firehose and cBioportal

https://www.biostars.org/p/366545/

Some examples

Colon sample case: Clinical data
- Compared to firebrowse.org, it seems cbioportal website has a good clinical data format to use. It also has several web-based analyses tool to use that may not be useful.
- http://www.cbioportal.org/. Bowel > Colorectal Adenocarcinoma (TCGA, Firehose Legacy); click the last icon "View clinical and genomic data of this study"
- Tab "Summary" and check "Colon Adenocarcinoma 392"
- Click the download button "Download clinical data for the selected cases"
- Save the file "coadread_tcga_clinical_data.tsv". It is a tab delimited text file. Column 3 is "Sample ID", column W is Disease Free (Months), column X is Disease Free Status, column BB is Overall Survival (Months), column BC is Overall Survival Status. (Cf. firebrowse gives a complicated table).

Colon sample case: Mutation data
- http://www.cbioportal.org/. Bowel > Colorectal Adenocarcinoma (TCGA, Firehose Legacy) > Query By Gene
- Query
  - Check "Mutations"
  - Uncheck "Putative copy-number alterations from GISTIC"
  - Check "mRNA Expression. Select one of the profiles below: > mRNA expression z-scores relative to diploid samples (microarray)"
  - Select Patient/Case Set: All samples (640)
  - Copy contents in oncomineGene.txt to "Enter Genes:" > Replace gene symbol: MRE11A:MRE11, RB:RB1
  - oncomineGene.txt has 169 genes. It has RB and RB1 genes. This website changes RB to RB1. There are 2 RB1. Remove one duplicate RB1 and total 168 genes.
  - Submit Query
- Click the left-most tab "Download" > Mutations (OQL is not in effect) Tab Delimited Format
- Save the file mutations.txt for 640 samples.

Colon sample case: RNASeq
- http://firebrowse.org/ (good for downloading gene expression data)
  - Click tab "BROAD GDAC"
  - Click last Browse (data column): Colon adenocarcinoma COAD 460 Browse Browse
  - Click "mRNAseq_Preprocess" and save the file gdac.broadinstitute.org_COAD.mRNAseq_Preprocess.Level_3.2016012800.0.0.tar.gz
  - Unzip the above file and pick "COAD.uncv2.mRNAseq_raw_counts.txt"
  - Remove rows with ? mark gene names.
  - Remove | and numbers after gene names.
  - SLC35E2 has 2 rows in COAD.uncv2.mRNAseq_raw_counts.txt. Pick the first one (SLC35E2|728661).
- Second method - GDC data portal website. See Download data from TCGA, GDC RNA-Seq Data Processing – July 27, 2020 GDC Monthly Webinar (video)
- Third method - TCGAbiolinks R package
- Fourth method - GenomicDataCommons R package. Tutorial:Protocol To Downlad TCGA Data From GDC
- Fifth method - GDC Data Transfer Tool Tutorial:Protocol To Downlad TCGA Data From GDC

Pancreatic sample: RNASeq
- https://www.cbioportal.org/datasets
- Pancreatic Adenocarcinoma (TCGA, PanCancer Atlas)
- Click the download icon in the same line and save the file paad_tcga_pan_can_atlas_2018.tar.gz
- Unzip the above file and pick "data_mrna_seq_v2_rsem.txt" (non-integer)

Tumor vs normal

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62944
https://cloud.r-project.org/web/packages/UCSCXenaShiny/vignettes/api.html
How can I get the normal sample of TCGA data?
How do I compare tumor vs normal expression?
Compare tumor vs normal within or across tissue types

curatedTCGAData, MultiAssayExperiment and curatedTCGAData Bioconductor 2020 workshop (video)

data(sampleTypes, package = "TCGAutils")
head(sampleTypes)
unique(sampleTypes$Definition) # 17 types
grep("Normal", unique(sampleTypes$Definition), val = T)
# [1] "Blood Derived Normal"    "Solid Tissue Normal"    
# [3] "Buccal Cell Normal"      "EBV Immortalized Normal"
# [5] "Bone Marrow Normal"

TCGAbiolinks package.
See the example KM Plot for gene of interest (e.g. TP53) using TCGA-PAAD dataset. TCGAquery_SampleTypes(barcode, typesample). For typesample, TP=PRIMARY SOLID TUMOR, NT=Solid Tissue Normal.

Drug response

Evaluating the molecule-based prediction of clinical drug responses in cancer Ding 2016 Bioinformatics, "bioinformatics_32_19_2891_s1.zip". 572 samples.

library(readxl)
dat <- read_excel("~/Downloads/bioinformatics_32_19_2891_s1/bioinfo16_supplementary_tables.xlsx", 
                  "Table S2", skip=2)
dat <- dat[-1, ]
dim(dat)
# [1] 2572   14
kable(table(dat$Cancer))
|Var1                                                                    | Freq|
|:-----------------------------------------------------------------------|----:|
|Adrenocortical carcinoma (ACC)                                          |   13|
|Bladder Urothelial Carcinoma (BLCA)                                     |  164|
|Brain Lower Grade Glioma (LGG)                                          |  162|
|Breast invasive carcinoma (BRCA)                                        |  389|
|Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) |   97|
|Colon adenocarcinoma (COAD)                                             |  192|
|Esophageal carcinoma (ESCA)                                             |   25|
|Glioblastoma multiforme (GBM)                                           |   10|
|Head and Neck squamous cell carcinoma (HNSC)                            |  112|
|Kidney Chromophobe (KICH)                                               |    2|
|Kidney renal clear cell carcinoma (KIRC)                                |   14|
|Kidney renal papillary cell carcinoma (KIRP)                            |   14|
|Liver hepatocellular carcinoma (LIHC)                                   |   29|
|Lung adenocarcinoma (LUAD)                                              |  151|
|Lung squamous cell carcinoma (LUSC)                                     |   69|
|Mesothelioma (MESO)                                                     |   80|
|Ovarian serous cystadenocarcinoma (OV)                                  |   11|
|Pancreatic adenocarcinoma (PAAD)                                        |   99|
|Pheochromocytoma and Paraganglioma (PCPG)                               |    7|
|Prostate adenocarcinoma (PRAD)                                          |   45|
|Rectum adenocarcinoma (READ)                                            |   67|
|Sarcoma (SARC)                                                          |  101|
|Skin Cutaneous Melanoma (SKCM)                                          |  137|
|Stomach adenocarcinoma (STAD)                                           |  243|
|Testicular Germ Cell Tumors (TGCT)                                      |  159|
|Thyroid carcinoma (THCA)                                                |   10|
|Uterine Carcinosarcoma (UCS)                                            |   83|
|Uterine Corpus Endometrial Carcinoma (UCEC)                             |   87|

kable(table(dat$drug_name))
|Var1                                               | Freq|
|:--------------------------------------------------|----:|
|Aldesleukin                                        |    6|
|Alverine                                           |    1|
|Anastrozole                                        |   18|
|anti-A5B1 integrin monoclonal antibody PF-04605412 |    1|
|anti-endosialin/TEM1 monoclonal antibody MORAb-004 |    1|
|autologous vaccine                                 |    1|
|Axitinib                                           |    3|
|AZD2171                                            |    1|
|Bacillus Calmette-Guerin (BCG)                     |    1|
|BCG                                                |    2|
|Bevacizumab                                        |   48|
|Bicalutamide                                       |   17|
|Bleomycin                                          |   54|
|BRAF inhibitor                                     |    1|
|Cabazitaxel                                        |    2|
|Cabozantinib                                       |    1|
|Cancer Vax                                         |    2|
|Capecitabine                                       |   55|
|Carboplatin                                        |  181|
|Carmustine                                         |    6|
|Cetuximab                                          |   21|
|Chemo, Multi-Agent, NOS                            |    1|
|Chemo, NOS                                         |    3|
|Cilengtide                                         |    1|
|Cisplatin                                          |  330|
|Copolang                                           |    5|
|COPOLANG CAPS                                      |    1|
|Cyclophosphamide                                   |  103|
|cyclophosphamide, vincristine, and dacarbazine     |    1|
|Cyclosporine                                       |    1|
|Dabrafenib                                         |    4|
|Dacarbazine                                        |   30|
|Dactinomycin                                       |    3|
|Dasatinib                                          |    8|
|Degarelix                                          |    1|
|Denosumab                                          |    2|
|Dexamethasone                                      |    6|
|Didox                                              |    3|
|Docetaxel                                          |  106|
|Docetaxel +/- Zactima                              |    1|
|Doxorubicin                                        |  108|
|doxorubicin/cyclophosphamide                       |    1|
|E7389                                              |    2|
|Enoticumab                                         |    1|
|Epirubicin                                         |   28|
|Epoetin alfa                                       |    1|
|Eribulin                                           |    1|
|Erlotinib                                          |    7|
|Etoposide                                          |   87|
|Everolimus                                         |    6|
|Everolimus, Gemcitabine, and Cisplatin             |    1|
|Exemestane                                         |    3|
|EZN-2968                                           |    1|
|Fluorouracil                                       |  212|
|Folfiri                                            |    1|
|Folfox                                             |    2|
|FOLFOX                                             |    2|
|Fotemustine                                        |    3|
|Fulvestrant                                        |    1|
|Gefitinib                                          |    2|
|Gemcitabine                                        |  165|
|Gemox                                              |    1|
|Goserelin                                          |    8|
|GP-100                                             |    1|
|GP100                                              |    1|
|HSC vaccine injection                              |    1|
|Hydrocortisone                                     |    1|
|Hydroxyurea                                        |    1|
|Ifosfamid                                          |    1|
|Ifosfamide                                         |   24|
|Imatinib                                           |    3|
|Infliximab                                         |    2|
|Interferon alfa-n1                                 |    6|
|Interferon alfacon-1                               |    8|
|iodine I 131 monoclonal antibody 81C6              |    1|
|Ipilimumab                                         |   11|
|Irinotecan                                         |   30|
|Ixabepilone                                        |    1|
|Ketoconazole                                       |    1|
|Lapatinib                                          |    2|
|Letrozole                                          |    5|
|Leucovorin                                         |   93|
|Leuprolide                                         |   16|
|Levothyroxine                                      |    1|
|Liothyronine                                       |    7|
|Lomustine                                          |   11|
|LY228820                                           |    1|
|Megestrol acetate                                  |    2|
|MEL-44                                             |    2|
|Melphalan                                          |    6|
|Methotrexate                                       |   15|
|Methylprednisolone                                 |    1|
|Mitomycin                                          |    7|
|Mitotane                                           |    1|
|Mitoxantrone                                       |    1|
|Mycophenolic acid                                  |    6|
|Nilutamide                                         |    2|
|nivolumab                                          |    1|
|Ondansetron                                        |    1|
|Oxaliplatin                                        |   75|
|Paclitaxel                                         |  172|
|Pamidronate                                        |    3|
|Panitumumab                                        |    1|
|Pazopanib                                          |    6|
|Pegfilgrastim                                      |    6|
|Pemetrexed                                         |   44|
|PI-88                                              |    1|
|Platinum                                           |    5|
|PNU-159548                                         |    1|
|Poly E                                             |    1|
|Polyplatillen                                      |    2|
|Procarbazine                                       |    8|
|px-866                                             |    1|
|R1507                                              |    1|
|Raloxifene                                         |    1|
|recMAGE- A3                                        |    1|
|recombinant interferon-∥2b                         |    1|
|Regorafenib                                        |    1|
|RenAmin                                            |    1|
|Resiquimod                                         |    1|
|ridaforolimus                                      |    1|
|rigosertib                                         |    1|
|Rituximab                                          |    1|
|Sargramostim                                       |    2|
|Sorafenib                                          |   17|
|Streptozocin                                       |    1|
|Sulindac                                           |    1|
|Sunitinib                                          |   10|
|Talimogene Laherparepvec (T-VEC)                   |    1|
|Tamoxifen                                          |   24|
|tegafur-gimeracil-oteracil potassium               |    3|
|Temozolomide                                       |  116|
|Temsirolimus                                       |    3|
|Thalidomide                                        |    1|
|Themozolomide                                      |    2|
|Threshold-302                                      |    1|
|Topotecan                                          |    4|
|Toremifene                                         |    1|
|Trabectedin                                        |    3|
|Trametinib                                         |    2|
|Trastuzumab                                        |   17|
|Trelstar                                           |    2|
|triptorelin                                        |    1|
|Tyrosine kinase inhibitor                          |    1|
|veliparib                                          |    2|
|Vemurafenib                                        |    3|
|Vinblastine                                        |   16|
|Vincristine                                        |   13|
|Vinorelbine                                        |   31|
|Vorinostat                                         |    3|
|Yervoy                                             |    2|
|Zoledronate                                        |    2|

kable(table(dat$Cancer[dat$drug_name == "Gemcitabine"]))
|Var1                                                                    | Freq|
|:-----------------------------------------------------------------------|----:|
|Bladder Urothelial Carcinoma (BLCA)                                     |   48|
|Breast invasive carcinoma (BRCA)                                        |    1|
|Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) |    2|
|Esophageal carcinoma (ESCA)                                             |    2|
|Liver hepatocellular carcinoma (LIHC)                                   |    3|
|Lung adenocarcinoma (LUAD)                                              |    7|
|Lung squamous cell carcinoma (LUSC)                                     |   10|
|Mesothelioma (MESO)                                                     |    6|
|Pancreatic adenocarcinoma (PAAD)                                        |   60|
|Pheochromocytoma and Paraganglioma (PCPG)                               |    1|
|Sarcoma (SARC)                                                          |   22|
|Skin Cutaneous Melanoma (SKCM)                                          |    1|
|Uterine Carcinosarcoma (UCS)                                            |    1|
|Uterine Corpus Endometrial Carcinoma (UCEC)                             |    1|

kable(table(dat$drug_name[dat$Cancer == "Pancreatic adenocarcinoma (PAAD)"]))
|Var1             | Freq|
|:----------------|----:|
|Capecitabine     |    6|
|Carboplatin      |    1|
|Cyclophosphamide |    1|
|Docetaxel        |    1|
|Doxorubicin      |    1|
|Erlotinib        |    1|
|Fluorouracil     |   13|
|Gemcitabine      |   60|
|Irinotecan       |    3|
|Leucovorin       |    4|
|Oxaliplatin      |    6|
|Paclitaxel       |    2|

The above data was used by Predicting cancer prognosis and drug response from the tumor microbiome Hermida 2022.

TCGA immunotherapy treated melanoma data. This uses recount::all_metadata() function.

# Get all metadata 
metadata_clean <- recount::all_metadata("tcga")
dim(metadata_clean)
# [1] 11284   864
kable(table(metadata_clean$gdc_cases.project.project_id))
|Var1      | Freq|
|:---------|----:|
|TCGA-ACC  |   79|
|TCGA-BLCA |  433|
|TCGA-BRCA | 1246|
|TCGA-CESC |  309|
|TCGA-CHOL |   45|
|TCGA-COAD |  546|
|TCGA-DLBC |   48|
|TCGA-ESCA |  198|
|TCGA-GBM  |  175|
|TCGA-HNSC |  548|
|TCGA-KICH |   91|
|TCGA-KIRC |  616|
|TCGA-KIRP |  323|
|TCGA-LAML |  126|
|TCGA-LGG  |  532|
|TCGA-LIHC |  424|
|TCGA-LUAD |  601|
|TCGA-LUSC |  555|
|TCGA-MESO |   87|
|TCGA-OV   |  430|
|TCGA-PAAD |  183|
|TCGA-PCPG |  187|
|TCGA-PRAD |  558|
|TCGA-READ |  177|
|TCGA-SARC |  265|
|TCGA-SKCM |  473|
|TCGA-STAD |  453|
|TCGA-TGCT |  156|
|TCGA-THCA |  572|
|TCGA-THYM |  122|
|TCGA-UCEC |  589|
|TCGA-UCS  |   57|
|TCGA-UVM  |   80|

# Get only PAAD project
x <- metadata_clean[metadata_clean$gdc_cases.project.project_id == "TCGA-PAAD",]
dim(x)
# [1] 183 864
class(x)
# [1] "DFrame"
x$xml_tumor_response_cdus_type
  [1] <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
 [17] <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
 [33] <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
 [49] <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
 [65] <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
 [81] <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
 [97] <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
[113] <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
[129] <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
[145] <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
[161] <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
[177] <NA> <NA> <NA> <NA> <NA> <NA> <NA>
Levels:  Complete response Partial response Progression Stable

library(knitr)
kable(table(toupper(x$cgc_drug_therapy_drug_name)))
|Var1                  | Freq|
|:---------------------|----:|
|5 FU                  |    3|
|5-FLUOROURACIL        |    3|
|5-FU                  |    4|
|5FU                   |    1|
|ABRAXANE              |    2|
|CAPECITABINE          |    2|
|CHEMO, NOS            |    2|
|CISPLATIN             |    2|
|CYCLOPHOSPHAMIDE      |    1|
|DOCETAXEL             |    1|
|FLUOROURACIL          |    2|
|FOLINIC ACID          |    1|
|FU7                   |    1|
|GEMCITABINE           |   68|
|GEMCITABINE INJECTION |    1|
|GEMCITIBINE           |    1|
|GEMZAR                |    7|
|LEUCOVORIN            |    3|
|LEUCOVORIN CALCIUM    |    2|
|OXALIPLATIN           |    4|
|XELODA                |    2|

x$gdc_cases.submitter_id
sum(x$gdc_cases.project.project_id == "TCGA-PAAD")
# [1] 183
x$cgc_case_primary_therapy_outcome_success
x$cgc_case_id == "TCGA-F2-6880"
x$xml_bcr_patient_barcode
x$xml_vital_status
x$xml_tumor_type
x$xml_primary_therapy_outcome_success
colnames(x)[c(198, 359)] # Open the dumped xlsx file, search "complete"
# [1] "cgc_case_primary_therapy_outcome_success" "xml_primary_therapy_outcome_success" 
# cgc column is characters and xml column is a factor

# write.table(as.matrix(x), file = "x.txt", sep="\t") NOT WORKING
writexl::write_xlsx(as.data.frame(x), "~/Downloads/x.xlsx")

kable(table(x[x$gdc_cases.project.project_id == "TCGA-PAAD", 
              "xml_primary_therapy_outcome_success"] ))
|Var1                        | Freq|
|:---------------------------|----:|
|                            |    0|
|Complete Remission/Response |   43|
|Partial Remission/Response  |    8|
|Progressive Disease         |   40|
|Stable Disease              |    8|
|0                           |    0|
|1                           |    0|
|2                           |    0|
|NO                          |    0|
|YES                         |    0|

x[x$gdc_cases.project.project_id == "TCGA-PAAD" & !is.na(x$xml_primary_therapy_outcome_success), 
  c("xml_bcr_patient_barcode", "xml_vital_status", "cgc_case_pathologic_stage", 
    "cgc_case_primary_therapy_outcome_success")] |> dim()
# [1] 99  4

x[x$gdc_cases.project.project_id == "TCGA-PAAD", "cgc_case_primary_therapy_outcome_success"] |> table() |> kable()
|Var1                        | Freq|
|:---------------------------|----:|
|Complete Remission/Response |   44|
|Partial Remission/Response  |    8|
|Progressive Disease         |   40|
|Stable Disease              |    8|

Understand TCGA Barcode/Sample ID

https://docs.gdc.cancer.gov/Encyclopedia/pages/TCGA_Barcode/

The TCGA sample label you provided, "TCGA.06.0675.11A.32R.A36H.07", is a standardized label used by The Cancer Genome Atlas (TCGA) project to identify biological samples collected from patients with cancer. The label provides important information about the sample, including the tumor type, the patient ID, and the sample collection site.

Here's a breakdown of the label components:

"TCGA" - This is the prefix used for all TCGA samples.
"06" - This represents the TCGA disease program, in this case, it refers to the program for Prostate Adenocarcinoma.
"0675" - This is the patient ID, a unique identifier assigned to each patient whose samples were included in the TCGA study.
"11A" - This represents the type + vial of sample, in this case, it's a primary tumor. Tumor types range from 01 - 09, normal types from 10 - 19 and control samples from 20 - 29. vial = a tube for collecting something.
"32R" - This is the portion of the tumor that was collected, in this case, it's the 32nd sample collected from the right lobe of the prostate.
"A36H" - This represents the TCGA biospecimen type, in this case, it's a "Solid Tissue Normal" sample from the patient's adrenal gland.
"07" - This is the TCGA sample type, in this case, it's "Diagnostic Slide".

in silico method/data/approach

A simple in silico approach to generate gene-expression profiles from subsets of cancer genomics data.
- It also demonstrated how to use cbioportal to do data analysis.
- It focuses on gene expression, DNA methylation and protein expression.
- Supp.
- Fig 1 (Representative analysis). KM plot.
- Fig 2 (Representative analysis). mRNA expression of genes boxplot colored by mutation
- Fig 3 (Representative analysis). mRNA expression vs mutation boxplot
- Fig 4 (Representative analysis). Methylation vs mRNA expression (counts) colored by mutation
- Fig 5 (Representative analysis). RPPA vs mutation. RPPA vs mRNA expression

Microbiome

Predicting cancer prognosis and drug response from the tumor microbiome Hermida 2022.

@@ Line 7: / Line 7: @@
 * [https://www.jianshu.com/p/3c0f74e85825 TCGA癌症中英文对照]
 * https://github.com/cBioPortal/cbioportal
+* https://gdac.broadinstitute.org/
+** [https://github.com/RTCGA/RTCGA/blob/master/R/downloadTCGA.R RCTGA::downloadTCGA()] uses the above website. * [http://zyxue.github.io/2017/06/02/understanding-TCGA-mRNA-Level3-analysis-results-files-from-firebrose.html Understanding TCGA mRNA Level3 analysis results files from FireBrowse]
+* [https://github.com/jmzeng1314/tcga_example TCGA实战大全]
+* [https://www.jianshu.com/p/79816a20cbb1 TCGA学习01：数据下载与整理], [https://biowolf.cn/m/view.php?aid=25 TCGA下载和提取临床数据], [https://blog.csdn.net/sayhello1025/article/details/103474816 TCGA临床数据整理], [https://www.cnblogs.com/nkwy2012/p/10112581.html TCGA样本命名详解]. [https://www.jianshu.com/p/69dc9e1e4f62 TCGA数据下载与ID转换]
+* UCSCXenaTools
+** [https://shixiangwang.github.io/home/en/post/ucscxenatools-201908/ UCSCXenaTools: Retrieve Gene Expression and Clinical Information from UCSC Xena for Survival Analysis]
+** https://docs.ropensci.org/UCSCXenaTools/
+** Several hubs are used. For example TCGA Hub is from https://tcga.xenahubs.net/
+* [http://www.linkedomics.org/ LinkedOmics]. [http://www.linkedomics.org/data_download/TCGA-KIRC/ TCGA-KIRC]
+* [https://github.com/GerkeLab/TCGAclinical TCGA Pancancer Clinical Data] RData & tsv.
+* [https://docs.cbioportal.org/user-guide/faq/ cBioPortal FAQs]
+= R packages =
+* [https://bioconductor.org/packages/release/bioc/html/cBioPortalData.html cBioPortalData]
 * [https://bioconductor.org/packages/release/workflows/html/TCGAWorkflow.html TCGAWorkflow] package. Rank 7/29. It suggests
 ** [http://bioconductor.org/packages/release/data/experiment/html/TCGAbiolinksGUI.data.html TCGAbiolinksGUI.data] (rank 2/416) Why?
@@ Line 14: / Line 28: @@
 ** [http://bioconductor.org/packages/release/data/experiment/html/HarmonizedTCGAData.html HarmonizedTCGAData] (276/416)
 ** [https://bioconductor.org/packages/release/bioc/html/RTCGAToolbox.html RTCGAToolbox], [https://antonioahn.github.io/post/tcgadata_download/ Downloading RNAseq, me450k and clinical data from TCGA melanoma tumours] (rank 251/2083).
-* https://gdac.broadinstitute.org/
+* [http://www.bioconductor.org/packages/release/data/experiment/html/RTCGA.RPPA.html RTCGA.RPPA] package. RPPA = reverse phase '''p'''rotein array.
-** [https://github.com/RTCGA/RTCGA/blob/master/R/downloadTCGA.R RCTGA::downloadTCGA()] uses the above website. [http://www.bioconductor.org/packages/release/data/experiment/html/RTCGA.RPPA.html RTCGA.RPPA] package. RPPA = reverse phase '''p'''rotein array.
 * [https://cran.r-project.org/web/packages/cgdsr/ cgdsr] package
 ** [https://www.biostars.org/p/219024/ Tutorial: retrieve full TCGA datasets from cBioportal with R]
 ** [http://qiubio.com/new/book/sessionInfo/#cbioportal 生物信息学生R入门教程]
-* [http://zyxue.github.io/2017/06/02/understanding-TCGA-mRNA-Level3-analysis-results-files-from-firebrose.html Understanding TCGA mRNA Level3 analysis results files from FireBrowse]
-* [https://github.com/jmzeng1314/tcga_example TCGA实战大全]
-* [https://www.jianshu.com/p/79816a20cbb1 TCGA学习01：数据下载与整理], [https://biowolf.cn/m/view.php?aid=25 TCGA下载和提取临床数据], [https://blog.csdn.net/sayhello1025/article/details/103474816 TCGA临床数据整理], [https://www.cnblogs.com/nkwy2012/p/10112581.html TCGA样本命名详解]. [https://www.jianshu.com/p/69dc9e1e4f62 TCGA数据下载与ID转换]
-* UCSCXenaTools
-** [https://shixiangwang.github.io/home/en/post/ucscxenatools-201908/ UCSCXenaTools: Retrieve Gene Expression and Clinical Information from UCSC Xena for Survival Analysis]
-** https://docs.ropensci.org/UCSCXenaTools/
-** Several hubs are used. For example TCGA Hub is from https://tcga.xenahubs.net/
 * [https://www.bioconductor.org/packages/release/data/experiment/html/GSE62944.html TCGA processed RNA-Seq data (GSE62944) as a SummarizedExperiment]
 ** This was used in [https://bioconductor.org/packages/release/bioc/vignettes/GSEABenchmarkeR/inst/doc/GSEABenchmarkeR.html GSEABenchmarkeR] package vignette
-* [http://www.linkedomics.org/ LinkedOmics]. [http://www.linkedomics.org/data_download/TCGA-KIRC/ TCGA-KIRC]
 * Comparison of RTCGA, UCSCXenaTools (CRAN), TCGAbiolinks & [https://www.bioconductor.org/packages/release/data/experiment/html/curatedTCGAData.html curatedTCGAData]
 ** [https://support.bioconductor.org/p/109896/ Differences between RTCGA and TCGAbiolinks data]
-* [https://github.com/GerkeLab/TCGAclinical TCGA Pancancer Clinical Data] RData & tsv.
-* [https://docs.cbioportal.org/user-guide/faq/ cBioPortal FAQs]
 = PanCancer atlas vs Firehose legacy =
@@ Line 488: / Line 491: @@
 = in silico method/data/approach =
-[https://www.future-science.com/doi/10.2144/btn-2018-0179 A simple in silico approach to generate gene-expression profiles from subsets of cancer genomics data]
+* [https://www.future-science.com/doi/10.2144/btn-2018-0179 A simple in silico approach to generate gene-expression profiles from subsets of cancer genomics data].
+** It also demonstrated how to use cbioportal to do data analysis.
+** It focuses on gene expression, DNA methylation and protein expression.
+** [https://www.future-science.com/doi/suppl/10.2144/btn-2018-0179 Supp].
+** Fig 1 (Representative analysis). KM plot.
+** Fig 2 (Representative analysis). mRNA expression of genes boxplot colored by mutation
+** Fig 3 (Representative analysis). mRNA expression vs mutation boxplot
+** Fig 4 (Representative analysis). Methylation vs mRNA expression (counts) colored by mutation
+** Fig 5 (Representative analysis). RPPA vs mutation. RPPA vs mRNA expression
+= Microbiome =
+[https://www.nature.com/articles/s41467-022-30512-3#Sec9 Predicting cancer prognosis and drug response from the tumor microbiome] Hermida 2022.

Tcga: Difference between revisions

Latest revision as of 13:55, 13 April 2023

Contents

Resources

R packages

PanCancer atlas vs Firehose legacy

Relationship of TCGA, Firehose and cBioportal

Some examples

Tumor vs normal

Drug response

Understand TCGA Barcode/Sample ID

in silico method/data/approach

Microbiome

Navigation menu

Tcga: Difference between revisions

Latest revision as of 13:55, 13 April 2023

Resources

R packages

PanCancer atlas vs Firehose legacy

Relationship of TCGA, Firehose and cBioportal

Some examples

Tumor vs normal

Drug response

Understand TCGA Barcode/Sample ID

in silico method/data/approach

Microbiome

Navigation menu

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