Answers.Body: <p>The files have pretty self-explanatory headers.</p>
  <p>The first file has EnsEMBL gene ID, HGNC symbol and then TPM for each sample. The second file has EnsEMBL gene ID, EnsEMBL transcript ID and TPM for each sample. Each header beginning with <code>GTEX</code> is a sample.</p>
  <p>You should be using linux command line to look at this, not basic Windows utilities like Notepad. At least use Notepad++, basic Windows utilities are not equipped to handle large text files.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-05-24T21:49:56.270Z
Answers.Text: This is the first I've heard of linux command line. Is it a software application or a function in R? Did a quick google search and wasn't entirely sure.
Answers.UserDisplayName: Macromind101
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-05-25T15:05:15.123Z
Answers.Text: Are you new to bioinformatics? Most of us use linux or macOS. You might be able to use [WSL](https://learn.microsoft.com/en-us/windows/wsl/) but without the command line, you're missing out on a lot of the power a computer has to offer in text processing. R and python are good but sometimes you need to do some quick operations and loading libraries/writing code to read a file can get tedious.
Answers.UserDisplayName: Ram RS
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-05-24T21:02:43.923Z
Answers.LastActivityDate: 2023-05-24T21:02:43.923Z
Answers.UserDisplayName: Ram RS
Body: <p>From the link <a href="https://gtexportal.org/home/datasets" rel="nofollow noreferrer">https://gtexportal.org/home/datasets</a>, under V7, I'm trying to do R/Python analyses on the Gene TPM and Transcript TPM files. But in these files (and to open them I had to use Universal Viewer since the files are too large to view with an app like NotePad), I'm seeing a bunch of ID's for samples (i.e. <code>GTEX-1117F-0226-SM-5GZZ7</code>), followed by transcript ID's like ENSG00000223972.4, and then a bunch of numbers like 0.02865 (and they take up like 99% of the large files). Can someone help me decipher what the numbers mean, please? And are the numbers supposed to be assigned to a specific sample ID? (The amount of letters far exceed the amount of samples, btw). I tried opening these files as tables in R but I do not think R is categorizing the contents of the file correctly.</p>
  <p>For context, I am planning to match males with females for sex comparison but in order to do that, I need to get R to categorize everything correctly. (I know that females have &quot;F&quot; where <code>####-#####-####-#x-#####</code> where <code>x</code> is and males have &quot;M&quot;).</p>
  <p><strong>Example subsets</strong></p>
  <p>For gene TPM:</p>
  <pre><code>#1.2 56202 11688
  Name Description GTEX-1117F-0226-SM-5GZZ7 GTEX-111CU-1826-SM-5GZYN GTEX-111FC-0226-SM-5N9B8 GTEX-111VG-2326-SM-5N9BK GTEX-111YS-2426-SM-5GZZQ GTEX-1122O-2026-SM-5NQ91 GTEX-1128S-2126-SM-5H12U GTEX-113IC-0226-SM-5HL5C 7C-0005-SM-57WDL GTEX-ZVE2-0006-SM-51MRW GTEX-ZVZQ-0006-SM-51MR8 GTEX-ZXES-0005-SM-57WCB GTEX-ZXG5-0005-SM-57WCN
  ENSG00000223972.4 DDX11L1 0.1082 0.1158 0.02104 0.02329 0 0.04641 0.03076 0.09358 0.121 0.02859 0 0.02373 0.08024 0.02231 0.0189 0.09309 0.07772 0.1596 0.1173 0.07969 0.07763 0.02062 0.07831 0.05159 0.1181 0.04187
  </code></pre>
  <p>For transcript TPM:</p>
  <pre><code>transcript_id gene_id GTEX-1117F-0226-SM-5GZZ7 GTEX-1117F-0426-SM-5EGHI GTEX-1117F-0526-SM-5EGHJ GTEX-1117F-0626-SM-5N9CS GTEX-1117F-0726-SM-5GIEN GTEX-1117F-1326-SM-5EGHH GTEX-1117F-2226-SM-5N9CH GTEX-1117F-2426-SM-5EGGH GTEX-111CU-0226-SM-5GZXC GTEX-111CU-0326-SM-5GZXO
  ENST00000373020.4 ENSG00000000003.10 26.84 4.13 13.54 31.17 6.87 39.75 32.25 30.81 19.69 46.42 9.07 27.83 9.04 12.87 18.18 11.12 11.28 7.86 5.52 24.67 6.48 8.13 21.41 5.39 7.98 8.56 51.61 12.51 70.01 17.48 2.1 14.16 3.96 11.39 1.29 6.5 29.93 4.74 3.1 20.82 16.81 14.82 50.8 16.9 9.35 39.3 3.61 2.87 11.73
  </code></pre>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-05-24T20:44:06.947Z
Comments.Text: Is what I have there currently fine?
Comments.UserDisplayName: Macromind101
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-05-24T21:13:22.237Z
Comments.Text: Thank you @Macromind101
Comments.UserDisplayName: M__
Tags: <r><rna-seq><sequence-analysis><genomics><tpm>
Title: What do the numbers mean in these RNA-Seq gene/transcript TPM files?
ThreadText: # What do the numbers mean in these RNA-Seq gene/transcript TPM files?
  
  (Asked by: username_0 on 2023-05-24)
  
  From the link <https://gtexportal.org/home/datasets>, under V7, I'm trying to do R/Python analyses on the Gene TPM and Transcript TPM files. But in these files (and to open them I had to use Universal Viewer since the files are too large to view with an app like NotePad), I'm seeing a bunch of ID's for samples (i.e. `GTEX-1117F-0226-SM-5GZZ7`), followed by transcript ID's like ENSG00000223972.4, and then a bunch of numbers like 0.02865 (and they take up like 99% of the large files). Can someone help me decipher what the numbers mean, please? And are the numbers supposed to be assigned to a specific sample ID? (The amount of letters far exceed the amount of samples, btw). I tried opening these files as tables in R but I do not think R is categorizing the contents of the file correctly.
  
  For context, I am planning to match males with females for sex comparison but in order to do that, I need to get R to categorize everything correctly. (I know that females have "F" where `####-#####-####-#x-#####` where `x` is and males have "M").
  
  **Example subsets**
  
  For gene TPM:
  
  ```
  #1.2 56202 11688
  Name Description GTEX-1117F-0226-SM-5GZZ7 GTEX-111CU-1826-SM-5GZYN GTEX-111FC-0226-SM-5N9B8 GTEX-111VG-2326-SM-5N9BK GTEX-111YS-2426-SM-5GZZQ GTEX-1122O-2026-SM-5NQ91 GTEX-1128S-2126-SM-5H12U GTEX-113IC-0226-SM-5HL5C 7C-0005-SM-57WDL GTEX-ZVE2-0006-SM-51MRW GTEX-ZVZQ-0006-SM-51MR8 GTEX-ZXES-0005-SM-57WCB GTEX-ZXG5-0005-SM-57WCN
  ENSG00000223972.4 DDX11L1 0.1082 0.1158 0.02104 0.02329 0 0.04641 0.03076 0.09358 0.121 0.02859 0 0.02373 0.08024 0.02231 0.0189 0.09309 0.07772 0.1596 0.1173 0.07969 0.07763 0.02062 0.07831 0.05159 0.1181 0.04187
  
  ```
  
  For transcript TPM:
  
  ```
  transcript_id gene_id GTEX-1117F-0226-SM-5GZZ7 GTEX-1117F-0426-SM-5EGHI GTEX-1117F-0526-SM-5EGHJ GTEX-1117F-0626-SM-5N9CS GTEX-1117F-0726-SM-5GIEN GTEX-1117F-1326-SM-5EGHH GTEX-1117F-2226-SM-5N9CH GTEX-1117F-2426-SM-5EGGH GTEX-111CU-0226-SM-5GZXC GTEX-111CU-0326-SM-5GZXO
  ENST00000373020.4 ENSG00000000003.10 26.84 4.13 13.54 31.17 6.87 39.75 32.25 30.81 19.69 46.42 9.07 27.83 9.04 12.87 18.18 11.12 11.28 7.86 5.52 24.67 6.48 8.13 21.41 5.39 7.98 8.56 51.61 12.51 70.01 17.48 2.1 14.16 3.96 11.39 1.29 6.5 29.93 4.74 3.1 20.82 16.81 14.82 50.8 16.9 9.35 39.3 3.61 2.87 11.73
  
  ```
  
  **Tags:** r, rna-seq, sequence-analysis, genomics, tpm
  
  > **Comments:**
  > **username_0**: Is what I have there currently fine?  
  > **username_1**: Thank you @username_0  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2023-05-24. Score: 1
  
  The files have pretty self-explanatory headers.
  
  The first file has EnsEMBL gene ID, HGNC symbol and then TPM for each sample. The second file has EnsEMBL gene ID, EnsEMBL transcript ID and TPM for each sample. Each header beginning with `GTEX` is a sample.
  
  You should be using linux command line to look at this, not basic Windows utilities like Notepad. At least use Notepad++, basic Windows utilities are not equipped to handle large text files.
  
  > **Comments:**
  > **username_0**: This is the first I've heard of linux command line. Is it a software application or a function in R? Did a quick google search and wasn't entirely sure.  
  > **username_2**: Are you new to bioinformatics? Most of us use linux or macOS. You might be able to use [WSL](https://learn.microsoft.com/en-us/windows/wsl/) but without the command line, you're missing out on a lot of the power a computer has to offer in text processing. R and python are good but sometimes you need to do some quick operations and loading libraries/writing code to read a file can get tedious.  
  
  ---
  
  