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How to add Gene expression data in metadata with R language

2025-04-10 Update From: SLTechnology News&Howtos shulou NAV: SLTechnology News&Howtos > Development >

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Most people do not understand the knowledge points of this article "how to add gene expression data in R language to metadata", so the editor summarizes the following content, detailed content, clear steps, and has a certain reference value. I hope you can get something after reading this article. Let's take a look at this article "how R language adds gene expression data to metadata".

Merge_metadata_genexpdata.r adds gene expression data to metadata

Instructions for use:

The result of gene expression was added to metadata. Gene expression files behave differently and are classified as different samples. As long as the files listed as different samples can be merged into metadata with this script.

$Rscript $scriptdir/merge_metadata_genexpdata.r-husage: / share/nas1/huangls/test/TCGA_immu/scripts/merge_metadata_genexpdata.r [- h]-m metadata-g expset-b by [--log2] [- o outdir] [- p prefix] merge metadata and gene expression:optional arguments:-h,-- help show this help message and exit-m metadata -- metadata metadata input metadata file path with suvival time [required]-g expset,-- expset expset input gene expression set file [required]-b by,-- by by input sample ID column name in metadata [required]-- log2 whether do log2 transfrom for expression data [optional, default: False]-o outdir -- outdir outdir output file directory [default cwd]-p prefix,-- prefix prefix out file name prefix [default cox] parameter description:

-m specifies the metadata file:

BarcodepatientsampleshortLetterCodedefinitionsample_submitter_idsample_type_idsample_idsample_typeTCGA-B7-A5TK-01A-12R-A36D-31TCGA-B7-A5TKTCGA-B7-A5TK-01ATPPrimary solid TumorTCGA-B7-A5TK-01A158937d2c-b4c3-4992-a95c-d0d1fa73f1a9Primary TumorTCGA-BR-7959-01A-11R-2343-13TCGA-BR-7959TCGA-BR-7959-01ATPPrimary solid TumorTCGA-BR-7959-01A1c8fc5fb2-ded2-48af-a87d-36c367a3330dPrimary TumorTCGA-IN-8462- 01A-11R-2343-13TCGA-IN-8462TCGA-IN-8462-01ATPPrimary solid TumorTCGA-IN-8462- 01A126509d1e-253b-463c-8654-589929889fbePrimary TumorTCGA-BR-A4CR-01A-11R-A24K-31TCGA-BR-A4CRTCGA-BR-A4CR-01ATPPrimary solid TumorTCGA-BR-A4CR-01A176165733-85ca-47d0-a82a-f64fa9b1b834Primary TumorTCGA-CG-4443-01A-01R-1157-13TCGA-CG-4443TCGA-CG-4443-01ATPPrimary solid TumorTCGA-CG-4443-01A1f4fb736a-42c9-4367-a327-d0d1c4cba359Primary TumorTCGA-KB-A93J-01A-11R-A39E-31TCGA-KB-A93JTCGA-KB-A93J-01ATPPrimary solid TumorTCGA-KB-A93J-01A1888711a8-8ffd -49bb-aa85-7455c07f1ad5Primary TumorTCGA-BR-4371-01A-01R-1157-13TCGA-BR-4371TCGA-BR-4371-01ATPPrimary solid TumorTCGA-BR-4371-01A195d6e839-a21b-4266-b7f8-e47fab262af3Primary TumorTCGA-IN-A6RO-01A-12R-A33Y-31TCGA-IN-A6ROTCGA-IN-A6RO-01ATPPrimary solid TumorTCGA-IN-A6RO-01A1c6e17043-a145-4cc3-b889-4f499b17dbf3Primary TumorTCGA-HU-A4H3-01A-21R-A251-31TCGA-HU-A4H3TCGA-HU-A4H3-01ATPPrimary solid TumorTCGA-HU-A4H3-01A1cd33e854-1bdfly42e0- 83e7-256c723c5b55Primary TumorTCGA-RD-A8MV-01A-11R-A36D-31TCGA-RD-A8MVTCGA-RD-A8MV-01ATPPrimary solid TumorTCGA-RD-A8MV-01A1f7a464d1-9939-4ab8-a03b-f2962e618817Primary TumorTCGA-VQ-A91X-01A-12R-A414-31TCGA-VQ-A91XTCGA-VQ-A91X-01ATPPrimary solid TumorTCGA-VQ-A91X-01A1288b0130-6744-495e-bd99-da6f6b5f6953Primary TumorTCGA-D7-8575-01A-11R-2343-13TCGA-D7-8575TCGA-D7-8575-01ATPPrimary solid TumorTCGA-D7-8575-01A171efd38a-03a9-488d-bde2-18b17559c775Primary TumorTCGA-BR-4257-01A-01R-1131-13TCGA-BR-4257TCGA-BR-4257-01ATPPrimary solid TumorTCGA-BR-4257-01A197b44b05-97eb-486c-94db-42838831de0bPrimary TumorTCGA-BR-8485-01A-11R-2402-01A-11R-2402-13TCGA-BR-8485TCGA-BR-8485-01A12f1460ea-827b-4c51-86a5-2d85771888bbPrimary TumorTCGA-BR-4370-01A-01R-1157-13TCGA-BR-4370TCGA-BR-4370-01ATPPrimary solid TumorTCGA-BR-4370-01A1cfb7901b-e4e1-42fe-802d-dcd34f8c4912Primary TumorTCGA-D7 -A748-01A-12R-A32D-31TCGA-D7-A748TCGA-D7-A748-01ATPPrimary solid TumorTCGA-D7-A748-01A1308bca2d-6e27-4da9-9a07-b0eb88437953Primary TumorTCGA-VQ-A91Z-01A-11R-A414-31TCGA-VQ-A91ZTCGA-VQ-A91Z-01ATPPrimary solid TumorTCGA-VQ-A91Z-01A1ec6ed61a-1d7e-4057-9ac2-dd1ed2accfb0Primary TumorTCGA-RD-A7C1-01A-11R-A32D-31TCGA-RD-A7C1TCGA-RD-A7C1-01ATPPrimary solid TumorTCGA-RD-A7C1-01A1b905ac72-aae1-4e6e-b560-46b9f4f9ef5fPrimary Tumor

-g specify the files to merge:

Cell_typeTCGA-B7-A5TK-01A-12R-A36D-31TCGA-BR-7959-01A-11R-2343-13TCGA-IN-8462-01A-11R-2343-13TCGA-BR-A4CR-01A-11R-A24K-31TCGA-CG-4443-01A-01R-1157-13TCGA-KB-A93J-01A-11R-A39E-31TCGA-BR-4371-01A-01R-1157-13TCGA-IN-A6RO-01A-12R-A33Y-31TCGA-HU-A4H3-01A-21R-A251-31TCGAmuri RDMui A8MVMuo01AMu11R- A36D-31B cells naive0.0418060.1190340.2754510.2437890.1187530.0975260.0874380.1107360.0918990.114157B cells memory0000000000Plasma cells0.0057780.0099670.04908000.0094010.0065960.0128190.0043260.001989T cells CD80.3998130.0813830.0522030.03256800.0172330.0421460.0742340.1923310.10848T cells CD4 naive0000000000T cells CD4 memory resting00.1636050.1585590.2058050.1613540.1970310.4454670.2074070.0957210.173691T cells CD4 memory activated0.2225870.0564290.0227860.0264880.0086630.0491170.0165070.0623090.1325770.077914T cells follicular helper0.006564000.00744400 .017110.0090810.0604540.0406370.056979T cells regulatory (Tregs) 00.0340660.082630.04020.0019320.03829700.0528590.0520450.045091T cells gamma delta000000.00810800.0048700NK cells resting00.03487500.028080.03182100.0180450.0062810.0234370.029263NK cells activated0.0069600.029651000.04728600.0044720.0074890Monocytes0.0054110.0137880.00713300.024350.0020080.013182000.00139Macrophages M00.0145140.0903520.0250320.1047250.167050.2242990.0784760.1175330.0683650.109002Macrophages M10.1359670.1032230.1006430.0120.080839788760.1224650.0636670. Examples of 103607Macrophages M20.0892590.1595240.0447030.1373460.4281930.1563850.1010520.102870.0716460.077649Dendritic cells resting0.0143160.0192490.0592990.00617800.0227080.043902000.004566 usage:

The results of immune infiltration were incorporated into metadata to facilitate follow-up comparative analysis.

Rscript $scriptdir/merge_metadata_genexpdata.r-m. / 08.Nomogram/nomogram_metadata.tsv-g.. / 03.TIME/immu/timer.res.tsv\-b barcode-p metadata_risk_score_timer is the content of this article on "how to add gene expression data in R language to metadata". I believe you all have some understanding. I hope the content shared by the editor will be helpful to you. If you want to know more about it, Please follow the industry information channel.

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