Display viral transcripts found in mRNA-seq MKL-1, WaGa EVs compared to cells

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Tags: python, R, processing, virus, human, genome

plot_samples_against_FPKM

Dowload the file fpkm_values_WaGa.csv

Download the file fpkm_values_MKL-1.csv

STEP1: using following code (R_scripts.txt) to generate merged_gene_counts_WaGa.csv and merged_gene_counts_MKL-1.csv

# ---- GENERATE BED-file for WaGa ----
cp WaGa.gff3 WaGa.gff3_backup
sed -i -e "s/\tGenbank\tgene\t/\tGenbank_gene\t/g" WaGa.gff3
grep "Genbank_gene" WaGa.gff3 > WaGa_gene.gff3
sed -i -e "s/\tGenbank_gene\t/\tGenbank\tgene\t/g" WaGa_gene.gff3
grep "gene_biotype=pseudogene" WaGa.gff3_backup >> WaGa_gene.gff3    #-->4

#The whole point of the GTF format was to standardise certain aspects that are left open in GFF. Hence, there are many different valid ways to encode the same information in a valid GFF format, and any parser or converter needs to be written specifically for the choices the author of the GFF file made. For example, a GTF file requires the gene ID attribute to be called "gene_id", while in GFF files, it may be "ID", "Gene", something different, or completely missing. 
# from gff3 to gtf
cp WaGa_gene.gff3 WaGa_gene.gtf
sed -i -e "s/\tID=gene-/\tgene_id \"/g" WaGa_gene.gtf
sed -i -e "s/;/\"; /g" WaGa_gene.gtf
sed -i -e "s/=/=\"/g" WaGa_gene.gtf

#sed -i -e $'s/\n/\"\n/g' WaGa_gene.gtf
gffread -E -F --bed WaGa.gff3 -o WaGa.bed    #-->4

# ---- GENERATE BED-file for MKL-1 ----
cp MKL-1.gff3 MKL-1.gff3_backup
sed -i -e "s/\tGenbank\tgene\t/\tGenbank_gene\t/g" MKL-1.gff3
grep "Genbank_gene" MKL-1.gff3 > MKL-1_gene.gff3
sed -i -e "s/\tGenbank_gene\t/\tGenbank\tgene\t/g" MKL-1_gene.gff3
grep "gene_biotype=pseudogene" MKL-1.gff3_backup >> MKL-1_gene.gff3    #-->4

# from gff3 to gtf
cp MKL-1_gene.gff3 MKL-1_gene.gtf
sed -i -e "s/\tID=gene-/\tgene_id \"/g" MKL-1_gene.gtf
sed -i -e "s/;/\"; /g" MKL-1_gene.gtf
sed -i -e "s/=/=\"/g" MKL-1_gene.gtf

#sed -i -e $'s/\n/\"\n/g' MKL-1_gene.gtf
gffread -E -F --bed MKL-1.gff3 -o MKL-1.bed    #-->4

# ---- GENERATE GFF3-file for WaGa ----
cp WaGa.gff3_backup WaGa.gff3
grep "^##" WaGa.gff3 > WaGa_gene.gff3
grep "ID=gene" WaGa.gff3 >> WaGa_gene.gff3
#!!!!VERY_IMPORTANT!!!!: change type '\tCDS\t' to '\texon\t'! 
sed -i -e "s/\tgene\t/\texon\t/g" WaGa_gene.gff3
#INPUT are 3796 genes (pseudogene, rRNA, tRNA, gene), why output are only 3726 (-pseudogene-rRNA)

# ---- GENERATE GFF3-file for MKL-1 ----
cp MKL-1.gff3_backup MKL-1.gff3
grep "^##" MKL-1.gff3 > MKL-1_gene.gff3
grep "ID=gene" MKL-1.gff3 >> MKL-1_gene.gff3
#!!!!VERY_IMPORTANT!!!!: change type '\tCDS\t' to '\texon\t'! 
sed -i -e "s/\tgene\t/\texon\t/g" MKL-1_gene.gff3
#INPUT are 3796 genes (pseudogene, rRNA, tRNA, gene), why output are only 3726 (-pseudogene-rRNA)

#MODIFY LTtr1 to LT1, LTtr2 to LT2, since the gff3 files contains only the records of LT1 and LT2, not LTtr1 and LTtr2.
#KJ128379.1      Genbank exon    196     429     .       +       .       ID=gene-LTtr1;Name=LTtr1;gbkey=Gene;gene=LTtr1;gene_biotype=protein_coding
#KJ128379.1      Genbank exon    861     1463    .       +       .       ID=gene-LTtr2;Name=LTtr2;gbkey=Gene;gene=LTtr2;gene_biotype=protein_coding

# generate ./results_virus/featureCounts/merged_gene_counts_MKL-1.txt
nextflow run rnaseq/main_Fraction_O_for_virus.nf --reads '/home/jhuang/DATA/Data_Ute_RNA-Seq_MKL-1/Raw_Data/*.fastq.gz' --fasta /home/jhuang/DATA/MCPyV_refs/MKL-1.fasta --gtf /home/jhuang/DATA/MCPyV_refs/MKL-1_gene.gff3 --bed12 /home/jhuang/DATA/MCPyV_refs/MKL-1.bed --singleEnd -profile standard --aligner hisat2 --fcGroupFeatures Name --fcGroupFeaturesType gene_biotype --saveReference -resume --saveAlignedIntermediates --skip_rseqc --skip_dupradar --skip_genebody_coverage --skip_preseq --skip_edger  #-work-dir

# generate ./results_virus/featureCounts/merged_gene_counts_WaGa.txt
nextflow run rnaseq/main_Fraction_O_for_virus.nf --reads '/home/jhuang/DATA/Data_Ute_RNA-Seq_WaGa/Raw_Data/*.fastq.gz' --fasta /home/jhuang/DATA/MCPyV_refs/WaGa.fasta --gtf /home/jhuang/DATA/MCPyV_refs/WaGa_gene.gff3 --bed12 /home/jhuang/DATA/MCPyV_refs/WaGa.bed --singleEnd -profile standard --aligner hisat2 --fcGroupFeatures Name --fcGroupFeaturesType gene_biotype --saveReference -resume --saveAlignedIntermediates --skip_rseqc --skip_dupradar --skip_genebody_coverage --skip_preseq --skip_edger

rsync -a -P jhuang@hamm:~/DATA/Data_Ute_RNA-Seq_WaGa/results_human/featureCounts/merged_gene_counts.txt ./results_human/featureCounts/merged_gene_counts_WaGa.txt
rsync -a -P jhuang@hamm:~/DATA/Data_Ute_RNA-Seq_WaGa/results_virus/featureCounts/merged_gene_counts.txt ./results_virus/featureCounts/merged_gene_counts_WaGa.txt
rsync -a -P jhuang@hamm:~/DATA/Data_Ute_RNA-Seq_MKL-1/results_human/featureCounts/merged_gene_counts.txt ./results_human/featureCounts/merged_gene_counts_MKL-1.txt
rsync -a -P jhuang@hamm:~/DATA/Data_Ute_RNA-Seq_MKL-1/results_virus/featureCounts/merged_gene_counts.txt ./results_virus/featureCounts/merged_gene_counts_MKL-1.txt

sed 's/Aligned.sortedByCoord.out.bam//g' results_human/featureCounts/merged_gene_counts_WaGa.txt > merged_gene_counts_WaGa_human.txt
sed 's/.sorted.bam//g' results_virus/featureCounts/merged_gene_counts_WaGa.txt > merged_gene_counts_WaGa_virus.txt
sed 's/Aligned.sortedByCoord.out.bam//g' results_human/featureCounts/merged_gene_counts_MKL-1.txt > merged_gene_counts_MKL-1_human.txt
sed 's/.sorted.bam//g' results_virus/featureCounts/merged_gene_counts_MKL-1.txt > merged_gene_counts_MKL-1_virus.txt

python3 concat_files_WaGa.py merged_gene_counts_WaGa_human.txt merged_gene_counts_WaGa_virus.txt merged_gene_counts_WaGa.csv     #(23 samples)
python3 concat_files_MKL-1.py merged_gene_counts_MKL-1_human.txt merged_gene_counts_MKL-1_virus.txt merged_gene_counts_MKL-1.csv #(18 samples)

The code snippet of the file concat_files_MKL-1.py

    #!/usr/bin/env python3

    #"MKL-1 RNA","MKL-1 RNA 118","MKL-1 RNA 147",    
    #"MKL-1 EV","MKL-1 EV 2","MKL-1 EV 118","MKL-1 EV 87","MKL-1 EV 27","MKL-1 EV 042","MKL-1 EV 0505",     
    #"MKL-1 EV sT DMSO 042","MKL-1 EV sT DMSO 0505",   "MKL-1 EV scr DMSO 042","MKL-1 EV scr DMSO 0505",   "MKL-1 EV sT Dox 042","MKL-1 EV sT Dox 0505",   "MKL-1 EV scr Dox 042","MKL-1 EV scr Dox 0505",

    #"WaGa RNA","WaGa RNA 118","WaGa RNA 147",    
    #"WaGa EV","WaGa EV 2","WaGa EV 118","WaGa EV 147","WaGa EV 226","WaGa EV 1107","WaGa EV 1605","WaGa EV 2706",    
    #"WaGa EV sT DMSO 1107","WaGa EV sT DMSO 1605","WaGa EV sT DMSO 2706",     "WaGa EV scr DMSO 1107","WaGa EV scr DMSO 1605","WaGa EV scr DMSO 2706",     "WaGa EV sT Dox 1107","WaGa EV sT Dox 1605","WaGa EV sT Dox 2706",     "WaGa EV scr Dox 1107","WaGa EV scr Dox 1605","WaGa EV scr Dox 2706"

    #Geneid  gene_name       WaGa_RNA        1605_WaGa_wt_EV 2706_WaGa_scr_DMSO_EV   WaGa_EV-RNA_226 1107_WaGa_scr_Dox_EV    1107_WaGa_sT_Dox_EV     1605_WaGa_sT_Dox_EV     1107_WaGa_scr_DMSO_EV   1107_WaGa_wt_EV 1605_WaGa_sT_DMSO_EV    WaGa_EV-RNA_2   2706_WaGa_sT_Dox_EV     WaGa_EV-RNA_147 2706_WaGa_sT_DMSO_EV    1107_WaGa_sT_DMSO_EV    2706_WaGa_scr_Dox_EV    WaGa_RNA_147    1605_WaGa_scr_DMSO_EV   2706_WaGa_wt_EV WaGa_RNA_118    1605_WaGa_scr_Dox_EV    WaGa_EV-RNA_118 WaGa_EV-RNA

    #Geneid  gene_name

    import sys
    import pandas as pd

    # Check if the correct number of arguments is provided
    if len(sys.argv) != 4:
        print("Usage: python merge_files.py <file1.csv> <file2.csv> <output.csv>")
        sys.exit()

    # Get the file names from command line arguments
    file1 = sys.argv[1]
    file2 = sys.argv[2]
    output_file = sys.argv[3]

    # Read the files
    df1 = pd.read_csv(file1, sep='\t')
    df2 = pd.read_csv(file2, sep='\t')

    # Rename columns in both DataFrames
    #df1.rename(columns=column_mapping, inplace=True)
    #df2.rename(columns=column_mapping, inplace=True)

    # Define the desired column order
    #"MKL-1 RNA","MKL-1 RNA 118","MKL-1 RNA 147",    
    #"MKL-1 EV","MKL-1 EV 2","MKL-1 EV 118","MKL-1 EV 87","MKL-1 EV 27","MKL-1 EV 042","MKL-1 EV 0505",     
    #"MKL-1 EV sT DMSO 042","MKL-1 EV sT DMSO 0505",   "MKL-1 EV scr DMSO 042","MKL-1 EV scr DMSO 0505",   "MKL-1 EV sT Dox 042","MKL-1 EV sT Dox 0505",   "MKL-1 EV scr Dox 042","MKL-1 EV scr Dox 0505",

    #Geneid  gene_name       0505_MKL-1_scr_Dox_EV   MKL-1_EV-RNA    0505_MKL-1_scr_DMSO_EV  0505_MKL-1_wt_EV        MKL-1_RNA_147   MKL-1_EV-RNA_2  MKL-1_RNA       MKL-1_EV-RNA_87 042_MKL-1_wt_EV 042_MKL-1_scr_Dox_EV    MKL-1_EV-RNA_27 042_MKL-1_sT_Dox        042_MKL-1_sT_DMSO       MKL-1_EV-RNA_118        0505_MKL-1_sT_Dox_EV    0505_MKL-1_sT_DMSO_EV   MKL-1_RNA_118   042_MKL-1_scr_DMSO_EV
    column_order = ['Geneid','gene_name',  'MKL-1_RNA','MKL-1_RNA_118','MKL-1_RNA_147',  'MKL-1_EV-RNA','MKL-1_EV-RNA_2','MKL-1_EV-RNA_118','MKL-1_EV-RNA_87','MKL-1_EV-RNA_27','042_MKL-1_wt_EV','0505_MKL-1_wt_EV',  '042_MKL-1_sT_DMSO','0505_MKL-1_sT_DMSO_EV', '042_MKL-1_scr_DMSO_EV','0505_MKL-1_scr_DMSO_EV',  '042_MKL-1_sT_Dox','0505_MKL-1_sT_Dox_EV', '042_MKL-1_scr_Dox_EV','0505_MKL-1_scr_Dox_EV']

    # Reorder columns in both DataFrames
    df1 = df1[column_order]
    df2 = df2[column_order]

    # Concatenate the dataframes
    result = pd.concat([df1, df2], axis=0, ignore_index=True)

    # Save the result to a new file
    result.to_csv(output_file, index=False)

The code snippet of the file concat_files_WaGa.py

    #!/usr/bin/env python3

    #"WaGa RNA","WaGa RNA 118","WaGa RNA 147",    
    #"WaGa EV","WaGa EV 2","WaGa EV 118","WaGa EV 147","WaGa EV 226","WaGa EV 1107","WaGa EV 1605","WaGa EV 2706",    
    #"WaGa EV sT DMSO 1107","WaGa EV sT DMSO 1605","WaGa EV sT DMSO 2706",     "WaGa EV scr DMSO 1107","WaGa EV scr DMSO 1605","WaGa EV scr DMSO 2706",     "WaGa EV sT Dox 1107","WaGa EV sT Dox 1605","WaGa EV sT Dox 2706",     "WaGa EV scr Dox 1107","WaGa EV scr Dox 1605","WaGa EV scr Dox 2706"

    #Geneid  gene_name       WaGa_RNA        1605_WaGa_wt_EV 2706_WaGa_scr_DMSO_EV   WaGa_EV-RNA_226 1107_WaGa_scr_Dox_EV    1107_WaGa_sT_Dox_EV     1605_WaGa_sT_Dox_EV     1107_WaGa_scr_DMSO_EV   1107_WaGa_wt_EV 1605_WaGa_sT_DMSO_EV    WaGa_EV-RNA_2   2706_WaGa_sT_Dox_EV     WaGa_EV-RNA_147 2706_WaGa_sT_DMSO_EV    1107_WaGa_sT_DMSO_EV    2706_WaGa_scr_Dox_EV    WaGa_RNA_147    1605_WaGa_scr_DMSO_EV   2706_WaGa_wt_EV WaGa_RNA_118    1605_WaGa_scr_Dox_EV    WaGa_EV-RNA_118 WaGa_EV-RNA

    #Geneid  gene_name

    import sys
    import pandas as pd

    # Check if the correct number of arguments is provided
    if len(sys.argv) != 4:
        print("Usage: python merge_files.py <file1.csv> <file2.csv> <output.csv>")
        sys.exit()

    # Get the file names from command line arguments
    file1 = sys.argv[1]
    file2 = sys.argv[2]
    output_file = sys.argv[3]

    # Read the files
    df1 = pd.read_csv(file1, sep='\t')
    df2 = pd.read_csv(file2, sep='\t')

    # Define the column mapping for renaming columns
    column_mapping = {
        'WaGa_RNA': 'WaGa RNA',
        'WaGa_RNA_118': 'WaGa RNA 118',
        'WaGa_RNA_147': 'WaGa RNA 147',
        'WaGa_EV-RNA': 'WaGa EV',
        'WaGa_EV-RNA_2': 'WaGa EV 2',
        'WaGa_EV-RNA_118': 'WaGa EV 118',
        'WaGa_EV-RNA_147': 'WaGa EV 147',
        'WaGa_EV-RNA_226': 'WaGa EV 226',
        '1107_WaGa_wt_EV': 'WaGa EV 1107',
        '1605_WaGa_wt_EV': 'WaGa EV 1605',
        '2706_WaGa_wt_EV': 'WaGa EV 2706',
        '1107_WaGa_sT_DMSO_EV': 'WaGa EV sT DMSO 1107',
        '1605_WaGa_sT_DMSO_EV': 'WaGa EV sT DMSO 1605',
        '2706_WaGa_sT_DMSO_EV': 'WaGa EV sT DMSO 2706',
        '1107_WaGa_scr_DMSO_EV': 'WaGa EV scr DMSO 1107',
        '1605_WaGa_scr_DMSO_EV': 'WaGa EV scr DMSO 1605',
        '2706_WaGa_scr_DMSO_EV': 'WaGa EV scr DMSO 2706',
        '1107_WaGa_sT_Dox_EV': 'WaGa EV sT Dox 1107',
        '1605_WaGa_sT_Dox_EV': 'WaGa EV sT Dox 1605',
        '2706_WaGa_sT_Dox_EV': 'WaGa EV sT Dox 2706',
        '1107_WaGa_scr_Dox_EV': 'WaGa EV scr Dox 1107',
        '1605_WaGa_scr_Dox_EV': 'WaGa EV scr Dox 1605',
        '2706_WaGa_scr_Dox_EV': 'WaGa EV scr Dox 2706',
    }  # Replace with your desired column names

    # Rename columns in both DataFrames
    #df1.rename(columns=column_mapping, inplace=True)
    #df2.rename(columns=column_mapping, inplace=True)

    # Define the desired column order
    #column_order = ['Geneid', 'gene_name', 'WaGa RNA','WaGa RNA 118','WaGa RNA 147','WaGa EV','WaGa EV 2','WaGa EV 118','WaGa EV 147','WaGa EV 226','WaGa EV 1107','WaGa EV 1605','WaGa EV 2706','WaGa EV sT DMSO 1107','WaGa EV sT DMSO 1605','WaGa EV sT DMSO 2706','WaGa EV scr DMSO 1107','WaGa EV scr DMSO 1605','WaGa EV scr DMSO 2706','WaGa EV sT Dox 1107','WaGa EV sT Dox 1605','WaGa EV sT Dox 2706','WaGa EV scr Dox 1107','WaGa EV scr Dox 1605','WaGa EV scr Dox 2706']  # Replace with your desired column order
    column_order = ['Geneid','gene_name','WaGa_RNA','WaGa_RNA_118','WaGa_RNA_147','WaGa_EV-RNA','WaGa_EV-RNA_2','WaGa_EV-RNA_118','WaGa_EV-RNA_147','WaGa_EV-RNA_226','1107_WaGa_wt_EV','1605_WaGa_wt_EV','2706_WaGa_wt_EV','1107_WaGa_sT_DMSO_EV','1605_WaGa_sT_DMSO_EV','2706_WaGa_sT_DMSO_EV','1107_WaGa_scr_DMSO_EV','1605_WaGa_scr_DMSO_EV','2706_WaGa_scr_DMSO_EV','1107_WaGa_sT_Dox_EV','1605_WaGa_sT_Dox_EV','2706_WaGa_sT_Dox_EV','1107_WaGa_scr_Dox_EV','1605_WaGa_scr_Dox_EV','2706_WaGa_scr_Dox_EV']

    # Reorder columns in both DataFrames
    df1 = df1[column_order]
    df2 = df2[column_order]

    # Concatenate the dataframes
    result = pd.concat([df1, df2], axis=0, ignore_index=True)

    # Save the result to a new file
    result.to_csv(output_file, index=False)

STEP2: using following code (R_scripts.txt) to generate fpkm_values_WaGa.csv and fpkm_values_MKL-1.csv

    #if (!requireNamespace("BiocManager", quietly = TRUE))
    #    install.packages("BiocManager")
    #BiocManager::install("biomaRt")
    library(biomaRt)
    library(dplyr)

    # Replace 'input_file.csv' with the name of your CSV file containing Ensembl gene IDs and counts
    input_data <- read.csv("merged_gene_counts_WaGa.csv")
    #input_data <- read.csv("merged_gene_counts_MKL-1.csv")
    input_data <- input_data %>% rename(ensembl_gene_id = Geneid)

    # Specify the rows you want to sum
    row1 <- 60665
    row2 <- 60666

    # Create a new row with the sums of the two specified rows, except for the first two columns
    new_row <- input_data[c(row1, row2), -c(1, 2)] %>%
      summarise(across(everything(), sum))

    # Name the first two columns
    new_row$ensembl_gene_id <- "LT"
    new_row$gene_name <- ""

    # Add the new row to the original data frame
    input_data <- bind_rows(input_data, new_row)

    # Delete row1 and row2
    input_data <- input_data[-c(row1, row2),]

    # Assuming your CSV file has columns named 'ensembl_gene_id' and 'counts'
    gene_ids <- input_data$ensembl_gene_id

    #ensembl <- useEnsembl(biomart = "ensembl", dataset = "hsapiens_gene_ensembl")
    ensembl <- useEnsembl(biomart = "ensembl", dataset = "hsapiens_gene_ensembl", version="104")

    #geness <- getBM(
    #    attributes = c('ensembl_gene_id', 'external_gene_name', 'gene_biotype', 'entrezgene_id', 'chromosome_name', 'start_position', 'end_position', 'strand', 'description'),
    #    filters = 'ensembl_gene_id',
    #    values = rownames(res), 
    #    mart = ensembl)
    #geness_uniq <- distinct(geness, ensembl_gene_id, .keep_all= TRUE)

    # Get transcript lengths for each Ensembl gene ID
    transcript_lengths <- getBM(
      attributes = c("ensembl_gene_id", "transcript_length"),
      filters = "ensembl_gene_id",
      values = gene_ids,
      mart = ensembl
    )

    # Calculate the median transcript length for each gene
    median_transcript_lengths <- transcript_lengths %>%
      group_by(ensembl_gene_id) %>%
      summarize(median_length = median(transcript_length, na.rm = TRUE))

    #add to median_transcript_lengths WaGa
    #KJ128379.1      Genbank exon    196     429     .       +       .       ID=gene-LT1;Name=LT1;gbkey=Gene;gene=LT1;gene_biotype=protein_coding --> 234
    #KJ128379.1      Genbank exon    861     1463    .       +       .       ID=gene-LT2;Name=LT2;gbkey=Gene;gene=LT2;gene_biotype=protein_coding --> 603
    #KJ128379.1      Genbank exon    196     756     .       +       .       ID=gene-ST;Name=ST;gbkey=Gene;gene=ST;gene_biotype=protein_coding  --> 561
    #KJ128379.1      Genbank exon    3156    4427    .       -       .       ID=gene-VP1;Name=VP1;gbkey=Gene;gene=VP1;gene_biotype=protein_coding --> 1272
    #KJ128379.1      Genbank exon    4393    5118    .       -       .       ID=gene-VP2;Name=VP2;gbkey=Gene;gene=VP2;gene_biotype=protein_coding --> 726
    #MKL-1
    #FJ173815.1      Genbank exon    196     429     .       +       .       ID=gene-LT1;Name=LT1;gbkey=Gene;gene=LT1;gene_biotype=protein_coding --> 234
    #FJ173815.1      Genbank exon    861     1619    .       +       .       ID=gene-LT2;Name=LT2;gbkey=Gene;gene=LT2;gene_biotype=protein_coding --> 759
    #FJ173815.1      Genbank exon    196     756     .       +       .       ID=gene-ST;Name=ST;gbkey=Gene;gene=ST;gene_biotype=protein_coding --> 561
    #FJ173815.1      Genbank exon    3110    4381    .       -       .       ID=gene-VP1;Name=VP1;gbkey=Gene;gene=VP1;gene_biotype=protein_coding --> 1272
    #FJ173815.1      Genbank exon    4347    5072    .       -       .       ID=gene-VP2;Name=VP2;gbkey=Gene;gene=VP2;gene_biotype=protein_coding --> 726

    # Add four lengths for LT, ST, VP1, and VP2, WaGa
    additional_lengths <- data.frame(
      ensembl_gene_id = c("LT", "ST", "VP1", "VP2"),
      median_length = c(837, 561, 1272, 726)
    )
    #additional_lengths <- data.frame(
    #  ensembl_gene_id = c("LT", "ST", "VP1", "VP2"),
    #  median_length = c(993, 561, 1272, 726)
    #)

    # Combine the original median_transcript_lengths data frame with the additional lengths
    median_transcript_lengths <- rbind(median_transcript_lengths, additional_lengths)

    # Merge input_data with median_transcript_lengths
    merged_data <- merge(input_data, median_transcript_lengths, by = "ensembl_gene_id")

    # [1] ensembl_gene_id        gene_name              WaGa_RNA              
    # [4] WaGa_RNA_118           WaGa_RNA_147           WaGa_EV.RNA           
    # [7] WaGa_EV.RNA_2          WaGa_EV.RNA_118        WaGa_EV.RNA_147       
    #[10] WaGa_EV.RNA_226        X1107_WaGa_wt_EV       X1605_WaGa_wt_EV      
    #[13] X2706_WaGa_wt_EV       X1107_WaGa_sT_DMSO_EV  X1605_WaGa_sT_DMSO_EV 
    #[16] X2706_WaGa_sT_DMSO_EV  X1107_WaGa_scr_DMSO_EV X1605_WaGa_scr_DMSO_EV
    #[19] X2706_WaGa_scr_DMSO_EV X1107_WaGa_sT_Dox_EV   X1605_WaGa_sT_Dox_EV  
    #[22] X2706_WaGa_sT_Dox_EV   X1107_WaGa_scr_Dox_EV  X1605_WaGa_scr_Dox_EV 
    #[25] X2706_WaGa_scr_Dox_EV  median_length         
    #<0 rows> (or 0-length row.names)
    # List of column names containing count data
    count_columns <- c("WaGa_RNA", "WaGa_RNA_118", "WaGa_RNA_147", "WaGa_EV.RNA", "WaGa_EV.RNA_2", "WaGa_EV.RNA_118", "WaGa_EV.RNA_147", "WaGa_EV.RNA_226", "X1107_WaGa_wt_EV", "X1605_WaGa_wt_EV", "X2706_WaGa_wt_EV", "X1107_WaGa_sT_DMSO_EV", "X1605_WaGa_sT_DMSO_EV", "X2706_WaGa_sT_DMSO_EV", "X1107_WaGa_scr_DMSO_EV", "X1605_WaGa_scr_DMSO_EV", "X2706_WaGa_scr_DMSO_EV", "X1107_WaGa_sT_Dox_EV", "X1605_WaGa_sT_Dox_EV", "X2706_WaGa_sT_Dox_EV", "X1107_WaGa_scr_Dox_EV", "X1605_WaGa_scr_Dox_EV", "X2706_WaGa_scr_Dox_EV")

    # [1] ensembl_gene_id         gene_name               MKL.1_RNA              
    # [4] MKL.1_RNA_118           MKL.1_RNA_147           MKL.1_EV.RNA           
    # [7] MKL.1_EV.RNA_2          MKL.1_EV.RNA_118        MKL.1_EV.RNA_87        
    #[10] MKL.1_EV.RNA_27         X042_MKL.1_wt_EV        X0505_MKL.1_wt_EV      
    #[13] X042_MKL.1_sT_DMSO      X0505_MKL.1_sT_DMSO_EV  X042_MKL.1_scr_DMSO_EV 
    #[16] X0505_MKL.1_scr_DMSO_EV X042_MKL.1_sT_Dox       X0505_MKL.1_sT_Dox_EV  
    #[19] X042_MKL.1_scr_Dox_EV   X0505_MKL.1_scr_Dox_EV  median_length 
    #count_columns <- c("MKL.1_RNA","MKL.1_RNA_118","MKL.1_RNA_147","MKL.1_EV.RNA","MKL.1_EV.RNA_2","MKL.1_EV.RNA_118","MKL.1_EV.RNA_87","MKL.1_EV.RNA_27","X042_MKL.1_wt_EV","X0505_MKL.1_wt_EV","X042_MKL.1_sT_DMSO","X0505_MKL.1_sT_DMSO_EV","X042_MKL.1_scr_DMSO_EV","X0505_MKL.1_scr_DMSO_EV","X042_MKL.1_sT_Dox","X0505_MKL.1_sT_Dox_EV","X042_MKL.1_scr_Dox_EV","X0505_MKL.1_scr_Dox_EV")

    # Calculate FPKM values for each sample
    fpkm_data <- merged_data %>%
      mutate(across(all_of(count_columns), ~ .x / median_length * 1e9 / sum(.x), .names = "FPKM_{.col}"))
    # # Calculate FPKM values
    # total_counts <- sum(merged_data$counts)
    # merged_data$FPKM <- (merged_data$counts / (merged_data$median_length / 1000)) / (total_counts / 1e6)
    # merged_data$FPKM <- (merged_data$counts * 1e9) / (sum(merged_data$counts) * merged_data$median_length)

    # Save the FPKM values to a CSV file
    write.csv(fpkm_data, "fpkm_values_WaGa.csv", row.names = FALSE)

STEP3: using following code to draw plots

    #For plotting a category against values with randomly assigned x-axis positions for the points within each category, you can use the geom_jitter() function in ggplot2. This function adds a small amount of random noise to the x-axis position of each point, allowing them to be distinguished from each other and preventing overplotting.
    #This will produce a scatter plot with the category on the x-axis and the values on the y-axis, with the x-axis positions of the points randomly assigned within each category. You can adjust the amount of jitter by changing the width parameter.

    library(ggplot2)
    fpkm_data_waga = read.csv("fpkm_values_WaGa.csv")
    fpkm_data_waga_selected = fpkm_data_waga[, c('ensembl_gene_id', 'FPKM_WaGa_RNA', 'FPKM_X1107_WaGa_wt_EV')]
    fpkm_data_mkl1= read.csv("fpkm_values_MKL-1.csv")
    fpkm_data_mkl1_selected = fpkm_data_mkl1[, c('ensembl_gene_id', 'FPKM_MKL.1_RNA', 'FPKM_X042_MKL.1_wt_EV')]

    # merge the two data frames by the 'id' column
    fpkm_data_selected <- merge(fpkm_data_waga_selected, fpkm_data_mkl1_selected, by = "ensembl_gene_id")

    ## reorder the columns
    #fpkm_data_selected <- select(fpkm_data_selected, ensembl_gene_id, FPKM_WaGa_RNA, FPKM_MKL.1_RNA, FPKM_WaGa_EV.RNA, FPKM_MKL.1_EV.RNA)

    # select columns with FPKM values
    fpkm_cols <- c('FPKM_WaGa_RNA', 'FPKM_X1107_WaGa_wt_EV', 'FPKM_MKL.1_RNA', 'FPKM_X042_MKL.1_wt_EV')

    ## take the log2 of selected columns
    #fpkm_data_selected_log2 <- fpkm_data_selected %>% 
    #  mutate(across(fpkm_cols, log2, .names = "log2_{.col}"))
    #fpkm_data <- fpkm_data %>%
    #  mutate(across(c(count_columns), ~log2(. + 1), .names = "{.col}_log2")) %>%
    #  select(-all_of(count_columns))

    # subset the data frame to exclude rows with missing values in column "ensembl_gene_id"
    #fpkm_data_filtered <- fpkm_data_selected %>% filter(ensembl_gene_id != "")

    fpkm_data_selected <- fpkm_data_selected %>%
      mutate(across(all_of(fpkm_cols), ~log2(. + 1), .names = "log2_{.col}")) %>% select(-all_of(fpkm_cols)) %>% rename("WaGa RNA" = "log2_FPKM_WaGa_RNA", "MKL-1 RNA" = "log2_FPKM_MKL.1_RNA", "WaGa EV" = "log2_FPKM_X1107_WaGa_wt_EV", "MKL-1 EV" = "log2_FPKM_X042_MKL.1_wt_EV")

    df_long <- pivot_longer(fpkm_data_selected, cols = -ensembl_gene_id, names_to = "x", values_to = "y")
    # use mutate() and ifelse() to add a color column
    df_long <- mutate(df_long, color = ifelse(ensembl_gene_id %in% c("ST", "LT", "VP1", "VP2"),
                                         "viral",
                                         "human"))
    df_long$x <- factor(df_long$x, levels=c("WaGa RNA","MKL-1 RNA","WaGa EV","MKL-1 EV")) 
    # Sample 1000 rows from your data frame
    #df_sampled <- df_long %>% sample_n(1000)

    # plot using geom_jitter
    png("plot_samples_against_FPKM.png", width = 800, height = 1000)
    #svg("plot_samples_against_FPKM.svg")
    ggplot(df_long, aes(x = x, y = y, color=color)) +
      scale_color_manual(values = c("human" = "darkblue", "viral" = "red")) +
      geom_jitter(width = 0.4) + 
      labs(x = "", y = "log2(Fragments Per Kilobase of transcript per Million mapped reads)")
    dev.off()

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