Unconfigured Ad

Collapse
X
 
  • Time
  • Show
Clear All
new posts
  • sunnycqcn
    Member
    • Apr 2013
    • 17

    #1

    extract data from txt files

    I want to extract gene expression values from a txt file based on gene names.

    for example:

    A 1500

    B 2500

    C 3500

    D 3400

    I want to extract A and C gene expression values using gene name file extract it.

    A 1500

    C 3500

    I have wroten a perl code, but it is not work well. Could you help me modify it or give me a new code?

    Thanks,

    #!/usr/bin/perl -w
    use strict;
    open(IN,"file1");
    open(NAME,"file2");
    open(OUT,">result");
    while(<IN>){
    chomp;
    @data=split / /,$_;
    $hash{$data[0]}=$data[1];
    }
    while(<NAME>){
    chomp;
    print OUT "$_ $hash{$_}\n";
    }
    close IN;
    close NAME;
    close OUT;
  • Michael.Ante
    Senior Member
    • Oct 2011
    • 127

    #2
    Why not simply grep:
    Code:
    grep -f name.txt gene_expression.txt > out.txt
    P.S.: Please be aware of the fact, that Cross-Postings are forbidden/not welcome..
    Last edited by Michael.Ante; 09-14-2015, 07:54 AM.

    Comment

    • sunnycqcn
      Member
      • Apr 2013
      • 17

      #3
      Thanks, you are right.

      Originally posted by Michael.Ante View Post
      Why not simply grep:
      Code:
      grep -f name.txt gene_expression.txt > out.txt
      P.S.: Please be aware of the fact, that Cross-Postings are forbidden/not welcome..

      Comment

      • ashishbansal
        Member
        • Dec 2018
        • 17

        #4
        After RMA processing, you should end up with an ExpressionSet object. Let's assume it's called eset, and your probe set IDs are in a character vector called yourProbeSetIDs. Then:

        exprs(eset) # gives a dataframe with expression levels, probe set IDs are the row names
        exprs(eset)[yourProbeSetIDs, ] # gives you of subset dataframe with your probe sets of interest
        To write a tab-delimited file:

        write.table(yourDataFrame, file="filename.txt", sep="\t", quote=F, row.names=T)
        Clinical Research

        Comment

        • Urmila785
          Junior Member
          • Jun 2020
          • 1

          #5
          I'm looking for something like that, Thank you.
          Online Clinical Research Training
          Online Clinical Research Training

          Comment

          Latest Articles

          Collapse

          • SEQadmin2
            Beyond CRISPR/Cas9: Understand, Choose, and Use the Right Genome Editing Tool
            by SEQadmin2



            CRISPR/Cas9 sparked the gene editing revolution for both research and therapeutics.1 But this system still showed severe issues that limited its applications. The most prominent were the heavy reliance on PAM sequences, delivery limitations, double-stranded breaks that prompt unintended edits and cell death, and editing inefficiency (both in targeting and in knock-in reliability).

            Despite this, “CRISPR helped turn genome editing from a specialized technique into
            ...
            07-31-2026, 11:01 AM
          • SEQadmin2
            Proteomic Platforms: How to Choose the Right Analytical Strategy to Improve Detection and Clinical Applications
            by SEQadmin2


            Proteomics platforms are evolving rapidly, with advances in mass spectrometry and affinity-based approaches expanding what researchers can detect and at what scale. As the field moves toward deeper proteome coverage and clinical applications, scientists face an increasingly complex landscape of tools. This article will explore how researchers are navigating these choices to find the right platform for their work.

            The systematic characterization of the human proteome has
            ...
            07-20-2026, 11:48 AM
          • SEQadmin2
            Advanced Sequencing Platforms Tackle Neuroscience’s Toughest Genomics Problems
            by SEQadmin2



            Genomics studies in neuroscience face a special challenge due to the brain’s complexity and scarcity of samples. Mapping changes in cell type and state using conventional next-generation sequencing methods remains challenging. Advances in technologies like single-cell sequencing, spatial transcriptomics, and long-read sequencing have opened the door to deeper studies of the brain and diseases like Alzheimer’s, amyotrophic lateral sclerosis (ALS), and schizophrenia.
            ...
            07-09-2026, 11:10 AM

          ad_right_rmr

          Collapse

          News

          Collapse

          Topics Statistics Last Post
          Started by SEQadmin2, Today, 07:41 AM
          0 responses
          9 views
          0 reactions
          Last Post SEQadmin2  
          Started by SEQadmin2, 08-03-2026, 10:13 AM
          0 responses
          25 views
          0 reactions
          Last Post SEQadmin2  
          Started by SEQadmin2, 07-31-2026, 02:55 AM
          0 responses
          38 views
          0 reactions
          Last Post SEQadmin2  
          Started by SEQadmin2, 07-24-2026, 12:17 PM
          0 responses
          25 views
          0 reactions
          Last Post SEQadmin2  
          Working...