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9 results for “KIRC”
Files from TCGA-KIRC Study for Body Part Regression Tutorial
<p>The data here are in whole based upon data generated by the TCGA Research Network: <a href="https://cancergenome.nih.gov/">http://cancergenome.nih.gov/</a>.</p> <p><br> The DICOM files from the <a href="https://wiki.cancerimagingarchive.net/display/Public/TCGA-KIRC#580038695f8cd691bda43dda71b4093c69c7318">TCGA-KIRC </a>study were converted to nifti files. Moreover, the nifti files with greater size than 35 MB and smaller size than 5 MB were removed (to reduce the size of the dataset and to remove the files with few slices). Furthermore, the metadata from the DICOM files is saved in a separate excel-file.</p>
TCGA Kidney Renal Clear Cell Carcinoma (KIRC) Gene Expression
<p><strong>Abstract:</strong></p> <p>The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset contains information about KIRC, the most common and aggressive subtype of kidney cancer, originating from the cells lining the tubules of the kidney and characterized by its clear appearance under the microscope. The gene expression profile was measured experimentally using the Illumina HiSeq 2000 RNA Sequencing platform by the University of North Carolina TCGA genome characterization center. The Sample IDs serve as unique identifiers for each sample.</p> <p><strong>Inspiration:</strong></p> <p>This dataset was uploaded to UBRITE for GTKB project. </p> <p><strong>Instruction:</strong></p> <p>The log2(x+1) normalization was removed, and z-normalization was performed on the dataset using a Python script.</p> <p><strong>Acknowledgments:</strong></p> <p>Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8</p> <p>The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764</p> <p><strong>U-BRITE last update: </strong>07/13/2023</p>
Processed data for FOXA2 analysis in TCGA KIRP and KIRC patients
<pre># Data and code to test whether FOXA2 is changed in KIRP patients with low FH ## Code https://github.com/ArianeMora/foxa2_kirp_kirc ## Datasets RNA count data were downloaded from TCGA (https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga) using scidat (https://github.com/ArianeMora/scidat) for patients with kidney cancers (rna_df.csv). ## Processing The kidney cancer patient count data were split into KIRC and KIRP, and only the tumour data was used for this analysis, see notebook FOXA2.ipynb in the code folder. Samples were split by their expression of FH in their tumour samples, with several annotations used to separate patients for completeness: 1. Low-High: Comparing the bottom 25% (< Q1) of patients by FH vs “high” FH (i.e. top 25%, > Q3): p.adj 0.00004 2. Low-Normal: Comparing the bottom 25% of patients by FH to the patients with “normal” range FH (between Q1 and Q3): p.adj 0.053 3. Outlier-High: Comparing outlier FH to “high” (i.e. top 25%): p.adj 0.067 4. Outlier-Normal: Comparing the outlier FH (Q1 – 1.5*IQR) to all “normal” FH patients: p.adj 0.169 We did the same for KIRC patients – we don’t see FOXA2 as expected 1. Comparing the bottom 25% of patients by FH vs the top 25% of patients with FH: 0.14 2. Comparing the bottom 25% of patients by FH to the patients with “normal” range FH: 0.25 3. Comparing the outlier FH to all “normal” FH patients: 0.31 4. Comparing outlier FH to “high” (i.e. top 25%): 0.32 Each of these groups were used to also perform DE analysis between the two groups, see respective RMD files in code for details. ### References If you use this work please cite TCGA: ``` Creighton, C. J., Morgan, M., Gunaratne, P. H., Wheeler, D. A., Gibbs, R. A., Gordon Robertson, A., Chu, A., Beroukhim, R., Cibulskis, K., Signoretti, S., Vandin Hsin-Ta Wu, F., Raphael, B. J., Verhaak, R. G. W., Tamboli, P., Torres-Garcia, W., Akbani, R., Weinstein, J. N., Reuter, V., Hsieh, J. J., … University of North Carolina at Chapel Hill. (2013). Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature, 499(7456), Article 7456. https://doi.org/10.1038/nature12222 ``` </pre>
TCGA KIRC CNV preprocessed by Polewko-Klim
<p>The TCGA KIRC CNV dataset preprocessed by Polewko-Klim, Aneta, originally for the purposes of analysis in the conference paper: "Analysis of Ensemble Feature Selection for Correlated High-Dimensional RNA-Seq Cancer Data", ICCS 2020, Springer.</p> <p>The decision variable is set to vitality status (1 for dead). There are 172 dead and 341 alive patients (513 patients in total).</p> <p>The first column in each file is the identifier of the sample (patient).</p> <p>The dataset includes 21106 copy number variation variables.</p> <p>KIRC = kidney renal clear cell carcinoma<br> CNV = copy number variation</p>
TCGA Kidney Renal Clear Cell Carcinoma (KIRC) Clinical Data
<p><strong>Abstract:</strong></p> <p>The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset includes curated survival data from the Pan-cancer Atlas paper titled <a href="http://www.cell.com/cell/fulltext/S0092-8674(18)30229-0">"An Integrated TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR) to drive high quality survival outcome analytics"</a>. The paper highlights four types of carefully curated survival endpoints, and <a href="http://www.cell.com/action/showFullTableImage?isHtml=true&tableId=tbl3&pii=S0092867418302290">recommends the use of the endpoints of OS, PFI, DFI, and DSS for each TCGA cancer type</a>. The dataset also includes phenotypic information about KIRC. The Sample IDs are unique identifiers, which can be paired with the gene expression dataset. </p> <p><strong>Inspiration:</strong></p> <p>This dataset was uploaded to UBRITE for GTKB project. </p> <p><strong>Instruction:</strong></p> <p>The survival and phenotype data were merged into one file. Empty columns were removed. Columns with the same value for every sample were also removed. </p> <p><strong>Acknowledgments:</strong></p> <p>Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8</p> <p>Liu, Jianfang, Caesar-Johnson, Samantha J. et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell, Volume 173, Issue 2, 400 - 416.e11. <a href="https://doi.org/10.1016/j.cell.2018.02.052">https://doi.org/10.1016/j.cell.2018.02.052</a></p> <p>The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764</p> <p><strong>U-BRITE last update: </strong>07/13/2023</p>
DICOM converted Slide Microscopy images for the TCGA-KIRC collection
<p>This dataset corresponds to a collection of images and/or image-derived data available from National Cancer Institute <a href="https://portal.imaging.datacommons.cancer.gov/">Imaging Data Commons (IDC)</a> [1]. This dataset was converted into DICOM representation and ingested by the IDC team. You can explore and visualize the corresponding images using IDC Portal here: <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=tcga_kirc">TCGA-KIRC</a>. You can use the manifests included in this Zenodo record to download the content of the collection following the <b>Download instructions</b> below.</p> <h3>Collection description</h3> <p>The <a href="http://imaging.cancer.gov/" target="_blank"><u>Cancer Imaging Program (CIP)</u></a> is working directly with primary investigators from institutes participating in TCGA to obtain and load images relating to the genomic, clinical, and pathological data being stored within the <a href="http://tcga-data.nci.nih.gov/" target="_blank">TCGA Data Portal</a>. Currently this large CT and MR multi-sequence image collection of kidney renal clear cell carcinoma (KIRC) patients can be matched by each unique case identifier with the extensive gene and expression data of the same case from The Cancer Genome Atlas Data Portal to research the link between clinical phenome and tissue genome.</p><br> <br><p>Please see the <a href="">TCGA -KIRC <i></i></a> page to learn more about the images and to obtain any supporting metadata for this collection.</p> <h3>Files included</h3> <p>A manifest file's name indicates the IDC data release in which a version of collection data was first introduced. For example, <code>collection_id-idc_v8-aws.s5cmd</code> corresponds to the contents of the <code>collection_id</code> collection introduced in IDC data release v8. If there is a subsequent version of this Zenodo page, it will indicate when a subsequent version of the corresponding collection was introduced.</p> <ol> <li><code>tcga_kirc-idc_v18-aws.s5cmd</code>: manifest of files available for download from public IDC Amazon Web Services buckets</li> <li><code>tcga_kirc-idc_v18-gcs.s5cmd</code>: manifest of files available for download from public IDC Google Cloud Storage buckets</li> <li><code>tcga_kirc-idc_v18-dcf.dcf</code>: Gen3 manifest (for details see <a href="Gen3 manifest documentation">https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids</a>)</li> </ol> <p>Note that manifest files that end in <code>-aws.s5cmd</code> reference files stored in Amazon Web Services (AWS) buckets, while <code>-gcs.s5cmd</code> reference files in Google Cloud Storage. The actual files are identical and are mirrored between AWS and GCP.</p> <h3>Download instructions</h3> <p>Each of the manifests include instructions in the header on how to download the included files.</p> <p>To download the files using <code>.s5cmd</code> manifests:</p> <ol> <li>install <a href="https://github.com/ImagingDataCommons/idc-index">idc-index</a> package: <code>pip install --upgrade idc-index</code></li> <li>download the files referenced by manifests included in this dataset by passing the <code>.s5cmd</code> manifest file: <code>idc download manifest.s5cmd</code>.</li> </ol> <p>To download the files using <code>.dcf</code> manifest, see manifest header.</p> <h3>Acknowledgments</h3> <p>Imaging Data Commons team has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l.</p> <h3>References</h3> <p>[1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. <i>National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence</i>. RadioGraphics (2023). <a href="https://doi.org/10.1148/rg.230180">https://doi.org/10.1148/rg.230180</a></p>
Dataset of TCGA-KIRC (htseq and methylation)
<p>This repository contains a dataset to start the pipeline of paper <em>Methylation-driven genes involved in renal carcinoma progression</em>.</p> <p>Pipeline was published in `https://github.com/josemaz/kirc-methyl`</p> <p> </p>
SAMPLER representations of frozen and FFPE TCGA-KIRC WSIs using an InceptionV3 backbone pretrained on imagenet
Open the record for dataset details and reuse information.
caArray_EXP-574: TCGA(Kirc): Gene Expression Analysis of TCGA samples using Agilent Expression 244K microarrays (UNC)
GEO Series GSE68784. Homo sapiens. 74 samples. Type: Expression profiling by array.
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