TCGA database preprocess¶
We often download patient survival data from the TCGA database for analysis in order to verify the importance of genes in cancer. However, the pre-processing of the TCGA database is often a headache. Here, we have introduced the TCGA module in ov, a way to quickly process the file formats we download from the TCGA database. We need to prepare 3 files as input:
- gdc_sample_sheet (
.tsv): TheSample Sheetbutton of TCGA, and we can gettsvfile from it - gdc_download_files (
folder): TheDownload/Cartbutton of TCGA, and we gettar.gzincluded all file you selected/ - clinical_cart (
folder): TheClinicalbutton of TCGA, and we can gettar.gzincluded all clinical of your files
import omicverse as ov
import scanpy as sc
ov.plot_set()
____ _ _ __ / __ \____ ___ (_)___| | / /__ _____________ / / / / __ `__ \/ / ___/ | / / _ \/ ___/ ___/ _ \ / /_/ / / / / / / / /__ | |/ / __/ / (__ ) __/ \____/_/ /_/ /_/_/\___/ |___/\___/_/ /____/\___/ Version: 1.6.4, Tutorials: https://omicverse.readthedocs.io/ All dependencies are satisfied.
TCGA counts read¶
Here, we use ov.bulk.TCGA to perform the gdc_sample_sheet, gdc_download_files and clinical_cart you download before. The raw count, fpkm and tpm matrix will be stored in anndata object
%%time
gdc_sample_sheep='data/TCGA_OV/gdc_sample_sheet.2024-07-05.tsv'
gdc_download_files='data/TCGA_OV/gdc_download_20240705_180129.081531'
clinical_cart='data/TCGA_OV/clinical.cart.2024-07-05'
aml_tcga=ov.bulk.pyTCGA(gdc_sample_sheep,gdc_download_files,clinical_cart)
aml_tcga.adata_init()
tcga module init success ...index init ... expression matrix init ...anndata construct CPU times: user 1min 30s, sys: 4.16 s, total: 1min 34s Wall time: 1min 34s
We can save the anndata object for the next use
aml_tcga.adata.write_h5ad('data/TCGA_OV/ov_tcga_raw.h5ad',compression='gzip')
Note: Each time we read the anndata file, we need to initialize the TCGA object using three paths so that the subsequent TCGA functions such as survival analysis can be used properly
If you wish to create your own TCGA data, we have provided sample data here for download:
gdc_sample_sheep='data/TCGA_OV/gdc_sample_sheet.2024-07-05.tsv'
gdc_download_files='data/TCGA_OV/gdc_download_20240705_180129.081531'
clinical_cart='data/TCGA_OV/clinical.cart.2024-07-05'
aml_tcga=ov.bulk.pyTCGA(gdc_sample_sheep,gdc_download_files,clinical_cart)
aml_tcga.adata_read('data/TCGA_OV/ov_tcga_raw.h5ad')
tcga module init success ... anndata reading
Meta init¶
As the TCGA reads the gene_id, we need to convert it to gene_name as well as adding basic information about the patient. Therefore we need to initialise the patient's meta information.
aml_tcga.adata_meta_init()
...anndata meta init ['gene_name', 'gene_type'] ['Case ID', 'Sample Type']
AnnData object with n_obs × n_vars = 429 × 60664
obs: 'Case ID', 'Sample Type'
var: 'gene_name', 'gene_type', 'gene_id'
layers: 'deseq_normalize', 'fpkm', 'tpm'
Survial init¶
We set up the path for Clinical earlier, but in fact we did not import the patient information in the previous process, we only initially determined the id of the patient's TCGA, so we attracted to initialize the clinical information
aml_tcga.survial_init()
aml_tcga.adata
AnnData object with n_obs × n_vars = 429 × 60664
obs: 'Case ID', 'Sample Type', 'vital_status', 'days'
var: 'gene_name', 'gene_type', 'gene_id'
layers: 'deseq_normalize', 'fpkm', 'tpm'
To visualize the gene you interested, we can use survival_analysis to finish it.
aml_tcga.survival_analysis('MYC',layer='deseq_normalize',plot=True)
(0.9528907736380975, 0.328984565709021)
If you want to calculate the survival of all genes, you can also use the survial_analysis_all to finish it. It may calculate a lot of times.
aml_tcga.survial_analysis_all()
aml_tcga.adata
Don't forget to save your result.
aml_tcga.adata.write_h5ad('data/TCGA_OV/ov_tcga_survial_all.h5ad',compression='gzip')