articles list

http://xgenes.com/article/article-content/209/rna-seq-skin-organoids-on-grch38-chrhsv1-final/ http://xgenes.com/article/article-content/157/prepare-virus-gtf-for-nextflow-run/

Methods

Data was processed using nf-core/rnaseq v3.12.0 (doi: https://doi.org/10.5281/zenodo.1400710) of the nf-core collection of workflows (Ewels et al., 2020).

The pipeline was executed with Nextflow v22.10.5 (Di Tommaso et al., 2017) with the following command:

nextflow run rnaseq/main.nf --input samplesheet.csv --outdir results --fasta /home/jhuang/DATA/Data_Tam_RNAseq_2024/CP059040.fasta --gff /home/jhuang/DATA/Data_Tam_RNAseq_2024/CP059040.gff -profile docker -resume --max_cpus 55 --max_memory 512.GB --max_time 2400.h --save_align_intermeds --save_unaligned --save_reference --aligner star_salmon --gtf_group_features gene_id --gtf_extra_attributes gene_name --featurecounts_group_type gene_biotype --featurecounts_feature_type transcript

Preparing raw data

They are wildtype strains grown in different medium.
AUM - artificial urine medium
Urine - human urine
MHB - Mueller-Hinton broth
AUM（人工尿液培养基）：pH值、营养成分、无菌性、渗透压、温度、污染物。
Urine（人类尿液）：pH值、比重、温度、污染物、化学成分、微生物负荷。
MHB（Mueller-Hinton培养基）：pH值、无菌性、营养成分、温度、渗透压、抗生素浓度。
mkdir raw_data; cd raw_data
ln -s ../X101SC24105589-Z01-J001/01.RawData/AUM-1/AUM-1_1.fq.gz AUM_r1_R1.fq.gz
ln -s ../X101SC24105589-Z01-J001/01.RawData/AUM-1/AUM-1_2.fq.gz AUM_r1_R2.fq.gz
ln -s ../X101SC24105589-Z01-J001/01.RawData/AUM-2/AUM-2_1.fq.gz AUM_r2_R1.fq.gz
ln -s ../X101SC24105589-Z01-J001/01.RawData/AUM-2/AUM-2_2.fq.gz AUM_r2_R2.fq.gz
ln -s ../X101SC24105589-Z01-J001/01.RawData/AUM-3/AUM-3_1.fq.gz AUM_r3_R1.fq.gz
ln -s ../X101SC24105589-Z01-J001/01.RawData/AUM-3/AUM-3_2.fq.gz AUM_r3_R2.fq.gz
ln -s ../X101SC24105589-Z01-J001/01.RawData/MHB-1/MHB-1_1.fq.gz MHB_r1_R1.fq.gz
ln -s ../X101SC24105589-Z01-J001/01.RawData/MHB-1/MHB-1_2.fq.gz MHB_r1_R2.fq.gz
ln -s ../X101SC24105589-Z01-J001/01.RawData/MHB-2/MHB-2_1.fq.gz MHB_r2_R1.fq.gz
ln -s ../X101SC24105589-Z01-J001/01.RawData/MHB-2/MHB-2_2.fq.gz MHB_r2_R2.fq.gz
ln -s ../X101SC24105589-Z01-J001/01.RawData/MHB-3/MHB-3_1.fq.gz MHB_r3_R1.fq.gz
ln -s ../X101SC24105589-Z01-J001/01.RawData/MHB-3/MHB-3_2.fq.gz MHB_r3_R2.fq.gz
ln -s ../X101SC24105589-Z01-J001/01.RawData/Urine-1/Urine-1_1.fq.gz Urine_r1_R1.fq.gz
ln -s ../X101SC24105589-Z01-J001/01.RawData/Urine-1/Urine-1_2.fq.gz Urine_r1_R2.fq.gz
ln -s ../X101SC24105589-Z01-J001/01.RawData/Urine-2/Urine-2_1.fq.gz Urine_r2_R1.fq.gz
ln -s ../X101SC24105589-Z01-J001/01.RawData/Urine-2/Urine-2_2.fq.gz Urine_r2_R2.fq.gz
ln -s ../X101SC24105589-Z01-J001/01.RawData/Urine-3/Urine-3_1.fq.gz Urine_r3_R1.fq.gz
ln -s ../X101SC24105589-Z01-J001/01.RawData/Urine-3/Urine-3_2.fq.gz Urine_r3_R2.fq.gz

(Optional) using trinity to find the most closely reference

Trinity --seqType fq --max_memory 50G --left trimmed/wt_r1_R1.fastq.gz  --right trimmed/wt_r1_R2.fastq.gz --CPU 12
#https://www.genome.jp/kegg/tables/br08606.html#prok
acb     KGB     Acinetobacter baumannii ATCC 17978  2007    GenBank
abm     KGB     Acinetobacter baumannii SDF     2008    GenBank
aby     KGB     Acinetobacter baumannii AYE     2008    GenBank
abc     KGB     Acinetobacter baumannii ACICU   2008    GenBank
abn     KGB     Acinetobacter baumannii AB0057  2008    GenBank
abb     KGB     Acinetobacter baumannii AB307-0294  2008    GenBank
abx     KGB     Acinetobacter baumannii 1656-2  2012    GenBank
abz     KGB     Acinetobacter baumannii MDR-ZJ06    2012    GenBank
abr     KGB     Acinetobacter baumannii MDR-TJ  2012    GenBank
abd     KGB     Acinetobacter baumannii TCDC-AB0715     2012    GenBank
abh     KGB     Acinetobacter baumannii TYTH-1  2012    GenBank
abad    KGB     Acinetobacter baumannii D1279779    2013    GenBank
abj     KGB     Acinetobacter baumannii BJAB07104   2013    GenBank
abab    KGB     Acinetobacter baumannii BJAB0715    2013    GenBank
abaj    KGB     Acinetobacter baumannii BJAB0868    2013    GenBank
abaz    KGB     Acinetobacter baumannii ZW85-1  2013    GenBank
abk     KGB     Acinetobacter baumannii AbH12O-A2   2014    GenBank
abau    KGB     Acinetobacter baumannii AB030   2014    GenBank
abaa    KGB     Acinetobacter baumannii AB031   2014    GenBank
abw     KGB     Acinetobacter baumannii AC29    2014    GenBank
abal    KGB     Acinetobacter baumannii LAC-4   2015    GenBank
#Note that the Acinetobacter baumannii strain ATCC 19606 chromosome, complete genome (GenBank: CP059040.1) was choosen as reference!

Downloading CP059040.fasta and CP059040.gff from GenBank

(Optional) Preparing CP059040.fasta, CP059040_gene.gff3 and CP059040.bed

#Reference genome: https://www.ncbi.nlm.nih.gov/nuccore/CP059040
cp /media/jhuang/Elements2/Data_Tam_RNASeq3/CP059040.fasta .     # Elements (Anna C.arnes)
cp /media/jhuang/Elements2/Data_Tam_RNASeq3/CP059040_gene.gff3 .
cp /media/jhuang/Elements2/Data_Tam_RNASeq3/CP059040_gene.gtf .
cp /media/jhuang/Elements2/Data_Tam_RNASeq3/CP059040.bed .
rsync -a -P CP059040.fasta jhuang@hamm:~/DATA/Data_Tam_RNAseq_2024/
rsync -a -P CP059040_gene.gff3 jhuang@hamm:~/DATA/Data_Tam_RNAseq_2024/
rsync -a -P CP059040.bed jhuang@hamm:~/DATA/Data_Tam_RNAseq_2024/
(base) jhuang@WS-2290C:/media/jhuang/Elements2/Data_Tam_RNASeq3$ find . -name "CP059040*"
./CP059040.fasta
./CP059040.bed
./CP059040.gb
./CP059040.gff3
./CP059040.gff3_backup
./CP059040_full.gb
./CP059040_gene.gff3
./CP059040_gene.gtf
./CP059040_gene_old.gff3
./CP059040_rRNA.gff3
./CP059040_rRNA_v.gff3
# ---- REF: Acinetobacter baumannii ATCC 17978 (DEBUG, gene_name failed) ----
#gffread -E -F -T GCA_000015425.1_ASM1542v1_genomic.gff -o GCA_000015425.1_ASM1542v1_genomic.gtf_
#grep "CDS" GCA_000015425.1_ASM1542v1_genomic.gtf_ > GCA_000015425.1_ASM1542v1_genomic.gtf
#sed -i -e "s/\tCDS\t/\texon\t/g" GCA_000015425.1_ASM1542v1_genomic.gtf
#gffread -E -F --bed GCA_000015425.1_ASM1542v1_genomic.gtf -o GCA_000015425.1_ASM1542v1_genomic.bed
grep "locus_tag" GCA_000015425.1_ASM1542v1_genomic.gtf_ > GCA_000015425.1_ASM1542v1_genomic.gtf
sed -i -e "s/\ttranscript\t/\texon\t/g" GCA_000015425.1_ASM1542v1_genomic.gtf # or using fc_count_type=transcript
sed -i -e "s/\tgene_name\t/\tName\t/g" GCA_000015425.1_ASM1542v1_genomic.gtf
gffread -E -F --bed GCA_000015425.1_ASM1542v1_genomic.gtf -o GCA_000015425.1_ASM1542v1_genomic.bed
#grep "gene_name" GCA_000015425.1_ASM1542v1_genomic.gtf | wc -l  #69=3887-3803
cp CP059040.gff3 CP059040_backup.gff3
sed -i -e "s/\tGenbank\tgene\t/\tGenbank_gene\t/g" CP059040.gff3
grep "Genbank_gene" CP059040.gff3 > CP059040_gene.gff3
sed -i -e "s/\tGenbank_gene\t/\tGenbank\tgene\t/g" CP059040_gene.gff3
#3796-3754=42--> they are pseudogene since grep "pseudogene" CP059040.gff3 | wc -l = 42
# --------------------------------------------------------------------------------------------------------------------------------------------------
# ---------- PREPARING gff3 file including gene_biotype=protein_coding+gene_biotype=tRNA = total(3754)) and gene_biotype=pseudogene(42) ------------
cp CP059040.gff3 CP059040_backup.gff3
sed -i -e "s/\tGenbank\tgene\t/\tGenbank_gene\t/g" CP059040.gff3
grep "Genbank_gene" CP059040.gff3 > CP059040_gene.gff3
sed -i -e "s/\tGenbank_gene\t/\tGenbank\tgene\t/g" CP059040_gene.gff3
grep "gene_biotype=pseudogene" CP059040.gff3_backup >> CP059040_gene.gff3    #-->3796
#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
sed -i -e "s/\tID=gene-/\tgene_id \"/g" CP059040_gene.gtf
sed -i -e "s/;/\"; /g" CP059040_gene.gtf
sed -i -e "s/=/=\"/g" CP059040_gene.gtf
#sed -i -e "s/\n/\"\n/g" CP059040_gene.gtf
#using editor instead!
#The following is GTF-format.
CP000521.1      Genbank exon    95      1492    .       +       .       transcript_id "gene0"; gene_id "gene0"; Name "A1S_0001"; gbkey "Gene"; gene_biotype "protein_coding"; locus_tag "A1S_0001";
#NZ_MJHA01000001.1       RefSeq  region  1       8663    .       +       .       ID=id0;Dbxref=taxon:575584;Name=unnamed1;collected-by=IG Schaub;collection-date=1948;country=USA: Vancouver;culture-collection=ATCC:19606;gbkey=Src;genome=plasmid;isolation-source=urine;lat-lon=37.53 N 75.4 W;map=unlocalized;mol_type=genomic DNA;nat-host=Homo sapiens;plasmid-name=unnamed1;strain=ATCC 19606;type-material=type strain of Acinetobacter baumannii
#NZ_MJHA01000001.1       RefSeq  gene    228     746     .       -       .       ID=gene0;Name=BIT33_RS00005;gbkey=Gene;gene_biotype=protein_coding;locus_tag=BIT33_RS00005;old_locus_tag=BIT33_18795
#NZ_MJHA01000001.1       Protein Homology        CDS     228     746     .       -       0       ID=cds0;Parent=gene0;Dbxref=Genbank:WP_000839337.1;Name=WP_000839337.1;gbkey=CDS;inference=COORDINATES: similar to AA sequence:RefSeq:WP_000839337.1;product=hypothetical protein;protein_id=WP_000839337.1;transl_table=11
##gff-version 3
##sequence-region CP059040.1 1 3980852
##species https://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?id=470
gffread -E -F --bed CP059040.gff3 -o CP059040.bed    #-->3796
##prepare the GTF-format (see above) --> ERROR! ----> using CP059040.gff3
##stringtie adeIJ.abx_r1.sorted.bam -o adeIJ.abx_r1.sorted_transcripts.gtf -v -G /media/jhuang/Elements/Data_Tam_RNASeq3/CP059040.gff3 -A adeIJ.abx_r1.sorted.gene_abund.txt -C adeIJ.abx_r1.sorted.bam.cov_refs.gtf -e -b adeIJ.abx_r1.sorted_ballgown
#[01/21 10:57:46] Loading reference annotation (guides)..
#GFF warning: merging adjacent/overlapping segments of gene-H0N29_00815 on CP059040.1 (179715-179786, 179788-180810)
#[01/21 10:57:46] 3796 reference transcripts loaded.
#Default stack size for threads: 8388608
#WARNING: no reference transcripts found for genomic sequence "gi|1906906720|gb|CP059040.1|"! (mismatched reference names?)
#WARNING: no reference transcripts were found for the genomic sequences where reads were mapped!
#Please make sure the -G annotation file uses the same naming convention for the genome sequences.
#[01/21 10:58:30] All threads finished.
#  ERROR: failed to find the gene identifier attribute in the 9th column of the provided GTF file.
#  The specified gene identifier attribute is 'Name'
#  An example of attributes included in your GTF annotation is 'ID=exon-H0N29_00075-1;Parent=rna-H0N29_00075;gbkey=rRNA;locus_tag=H0N29_00075;product=16S ribosomal RNA'
#  The program has to termin
#  ERROR: failed to find the gene identifier attribute in the 9th column of the provided GTF file.
#  The specified gene identifier attribute is 'gene_biotype'
#  An example of attributes included in your GTF annotation is 'ID=exon-H0N29_00075-1;Parent=rna-H0N29_00075;gbkey=rRNA;locus_tag=H0N29_00075;product=16S ribosomal RNA'
#  The program has to terminate.
#grep "ID=cds-" CP059040.gff3 | wc -l
#grep "ID=exon-" CP059040.gff3 | wc -l
#grep "ID=gene-" CP059040.gff3 | wc -l   #the same as H0N29_18980/5=3796
grep "gbkey=" CP059040.gff3 | wc -l  7695
grep "ID=id-" CP059040.gff3 | wc -l  5
grep "locus_tag=" CP059040.gff3 | wc -l    7689
#...
cds   3701                             locus_tag=xxxx, no gene_biotype
exon   96                              locus_tag=xxxx, no gene_biotype
gene   3796                            locus_tag=xxxx, gene_biotype=xxxx,
id  (riboswitch+direct_repeat,5)       both no --> ignoring them!!  # grep "ID=id-" CP059040.gff3
rna    96                              locus_tag=xxxx, no gene_biotype
------------------
    7694
cp CP059040.gff3_backup CP059040.gff3
grep "^##" CP059040.gff3 > CP059040_gene.gff3
grep "ID=gene" CP059040.gff3 >> CP059040_gene.gff3
#!!!!VERY_IMPORTANT!!!!: change type '\tCDS\t' to '\texon\t'!
sed -i -e "s/\tgene\t/\texon\t/g" CP059040_gene.gff3

Preparing the directory trimmed

mkdir trimmed trimmed_unpaired;
for sample_id in AUM_r1 AUM_r2 AUM_r3 Urine_r1 Urine_r2 Urine_r3 MHB_r1 MHB_r2 MHB_r3; do \
for sample_id in MHB_r1 MHB_r2 MHB_r3; do \
        java -jar /home/jhuang/Tools/Trimmomatic-0.36/trimmomatic-0.36.jar PE -threads 100 raw_data/${sample_id}_R1.fq.gz raw_data/${sample_id}_R2.fq.gz trimmed/${sample_id}_R1.fq.gz trimmed_unpaired/${sample_id}_R1.fq.gz trimmed/${sample_id}_R2.fq.gz trimmed_unpaired/${sample_id}_R2.fq.gz ILLUMINACLIP:/home/jhuang/Tools/Trimmomatic-0.36/adapters/TruSeq3-PE-2.fa:2:30:10:8:TRUE LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36 AVGQUAL:20; done 2> trimmomatic_pe.log;
done

Preparing samplesheet.csv

sample,fastq_1,fastq_2,strandedness
AUM_r1,AUM_r1_R1.fq.gz,AUM_r1_R2.fq.gz,auto
AUM_r2,AUM_r2_R1.fq.gz,AUM_r2_R2.fq.gz,auto
AUM_r3,AUM_r3_R1.fq.gz,AUM_r3_R2.fq.gz,auto
MHB_r1,MHB_r1_R1.fq.gz,MHB_r1_R2.fq.gz,auto
MHB_r2,MHB_r2_R1.fq.gz,MHB_r2_R2.fq.gz,auto
MHB_r3,MHB_r3_R1.fq.gz,MHB_r3_R2.fq.gz,auto
Urine_r1,Urine_r1_R1.fq.gz,Urine_r1_R2.fq.gz,auto
Urine_r2,Urine_r2_R1.fq.gz,Urine_r2_R2.fq.gz,auto
Urine_r3,Urine_r3_R1.fq.gz,Urine_r3_R2.fq.gz,auto

nextflow run

#Example1: http://xgenes.com/article/article-content/157/prepare-virus-gtf-for-nextflow-run/
docker pull nfcore/rnaseq
ln -s /home/jhuang/Tools/nf-core-rnaseq-3.12.0/ rnaseq
#Default: --gtf_group_features 'gene_id'  --gtf_extra_attributes 'gene_name' --featurecounts_group_type 'gene_biotype' --featurecounts_feature_type 'exon'
#(host_env) !NOT_WORKING! jhuang@WS-2290C:~/DATA/Data_Tam_RNAseq_2024$ /usr/local/bin/nextflow run rnaseq/main.nf --input samplesheet.csv --outdir results    --fasta "/home/jhuang/DATA/Data_Tam_RNAseq_2024/CP059040.fasta" --gff "/home/jhuang/DATA/Data_Tam_RNAseq_2024/CP059040.gff"        -profile docker -resume  --max_cpus 55 --max_memory 512.GB --max_time 2400.h    --save_align_intermeds --save_unaligned --save_reference    --aligner 'star_salmon'    --gtf_group_features 'gene_id'  --gtf_extra_attributes 'gene_name' --featurecounts_group_type 'gene_biotype' --featurecounts_feature_type 'transcript'
# -- DEBUG_1 (CDS --> exon in CP059040.gff) --
#Checking the record (see below) in results/genome/CP059040.gtf
#In ./results/genome/CP059040.gtf e.g. "CP059040.1      Genbank transcript      1       1398    .       +       .       transcript_id "gene-H0N29_00005"; gene_id "gene-H0N29_00005"; gene_name "dnaA"; Name "dnaA"; gbkey "Gene"; gene "dnaA"; gene_biotype "protein_coding"; locus_tag "H0N29_00005";"
#--featurecounts_feature_type 'transcript' returns only the tRNA results
#Since the tRNA records have "transcript and exon". In gene records, we have "transcript and CDS". replace the CDS with exon
grep -P "\texon\t" CP059040.gff | sort | wc -l    #96
grep -P "cmsearch\texon\t" CP059040.gff | wc -l    #=10  ignal recognition particle sRNA small typ, transfer-messenger RNA, 5S ribosomal RNA
grep -P "Genbank\texon\t" CP059040.gff | wc -l    #=12  16S and 23S ribosomal RNA
grep -P "tRNAscan-SE\texon\t" CP059040.gff | wc -l    #tRNA 74
wc -l star_salmon/AUM_r3/quant.genes.sf  #--featurecounts_feature_type 'transcript' results in 96 records!
grep -P "\tCDS\t" CP059040.gff | wc -l  #3701
sed 's/\tCDS\t/\texon\t/g' CP059040.gff > CP059040_m.gff
grep -P "\texon\t" CP059040_m.gff | sort | wc -l  #3797
# -- DEBUG_2: combination of 'CP059040_m.gff' and 'exon' results in ERROR, using 'transcript' instead!
--gff "/home/jhuang/DATA/Data_Tam_RNAseq_2024/CP059040_m.gff" --featurecounts_feature_type 'transcript'
# ---- SUCCESSFUL with directly downloaded gff3 and fasta from NCBI using docker after replacing 'CDS' with 'exon' ----
(host_env) /usr/local/bin/nextflow run rnaseq/main.nf --input samplesheet.csv --outdir results    --fasta "/home/jhuang/DATA/Data_Tam_RNAseq_2024/CP059040.fasta" --gff "/home/jhuang/DATA/Data_Tam_RNAseq_2024/CP059040_m.gff"        -profile docker -resume  --max_cpus 55 --max_memory 512.GB --max_time 2400.h    --save_align_intermeds --save_unaligned --save_reference    --aligner 'star_salmon'    --gtf_group_features 'gene_id'  --gtf_extra_attributes 'gene_name' --featurecounts_group_type 'gene_biotype' --featurecounts_feature_type 'transcript'
# -- DEBUG_3: make sure the header of fasta is the same to the *_m.gff file

Import data and pca-plot

#mamba activate r_env
#install.packages("ggfun")
# Import the required libraries
library("AnnotationDbi")
library("clusterProfiler")
library("ReactomePA")
library(gplots)
library(tximport)
library(DESeq2)
#library("org.Hs.eg.db")
library(dplyr)
library(tidyverse)
#install.packages("devtools")
#devtools::install_version("gtable", version = "0.3.0")
library(gplots)
library("RColorBrewer")
#install.packages("ggrepel")
library("ggrepel")
# install.packages("openxlsx")
library(openxlsx)
library(EnhancedVolcano)
library(DESeq2)
setwd("~/DATA/Data_Tam_RNAseq_2024/results/star_salmon")
# Define paths to your Salmon output quantification files
files <- c("AUM_r1" = "./AUM_r1/quant.sf",
        "AUM_r2" = "./AUM_r2/quant.sf",
        "AUM_r3" = "./AUM_r3/quant.sf",
        "Urine_r1" = "./Urine_r1/quant.sf",
        "Urine_r2" = "./Urine_r2/quant.sf",
        "Urine_r3" = "./Urine_r3/quant.sf",
        "MHB_r1" = "./MHB_r1/quant.sf",
        "MHB_r2" = "./MHB_r2/quant.sf",
        "MHB_r3" = "./MHB_r3/quant.sf")
# Import the transcript abundance data with tximport
txi <- tximport(files, type = "salmon", txIn = TRUE, txOut = TRUE)
# Define the replicates and condition of the samples
replicate <- factor(c("r1", "r2", "r3", "r1", "r2", "r3", "r1", "r2", "r3"))
condition <- factor(c("AUM","AUM","AUM", "Urine","Urine","Urine", "MHB","MHB","MHB"))
# Define the colData for DESeq2
colData <- data.frame(condition=condition, replicate=replicate, row.names=names(files))
# -- transcript-level count data (x2) --
# Create DESeqDataSet object
dds <- DESeqDataSetFromTximport(txi, colData=colData, design=~condition)
write.csv(counts(dds), file="transcript_counts.csv")
# -- gene-level count data (x2) --
# Read in the tx2gene map from salmon_tx2gene.tsv
tx2gene <- read.table("salmon_tx2gene.tsv", header=FALSE, stringsAsFactors=FALSE)
# Set the column names
colnames(tx2gene) <- c("transcript_id", "gene_id", "gene_name")
# Remove the gene_name column if not needed
tx2gene <- tx2gene[,1:2]
# Import and summarize the Salmon data with tximport
txi <- tximport(files, type = "salmon", tx2gene = tx2gene, txOut = FALSE)
# Continue with the DESeq2 workflow as before...
colData <- data.frame(condition=condition, replicate=replicate, row.names=names(files))
dds <- DESeqDataSetFromTximport(txi, colData=colData, design=~condition+replicate)
#dds <- dds[rowSums(counts(dds) > 3) > 2, ]    #3796->3487
write.csv(counts(dds, normalized=FALSE), file="gene_counts.csv")
dim(counts(dds))
head(counts(dds), 10)
rld <- rlogTransformation(dds)
#We don't need to run DESeq(dds) before estimateSizeFactors(dds). In fact, the typical workflow in DESeq2 is the opposite: we usually run estimateSizeFactors(dds) (and other preprocessing functions) before running the main DESeq(dds) function.
#The estimateSizeFactors function is used to calculate size factors for normalization, which corrects for differences in library size (i.e., the number of read counts) between samples. This normalization step is crucial to ensure that differences in gene expression aren't merely due to differences in sequencing depth between samples.
#The DESeq function, on the other hand, performs the main differential expression analysis, comparing gene expression between different conditions or groups.
#So, the typical workflow is:
#  - Create the DESeqDataSet object.
#  - Use estimateSizeFactors to normalize for library size.
#  - (Optionally, estimate dispersion with estimateDispersions if not using the full DESeq function later.)
#  - Use DESeq for the differential expression analysis.
#  - However, it's worth noting that if you run the main DESeq function directly after creating the DESeqDataSet object, it will automatically perform the normalization (using estimateSizeFactors) and dispersion estimation steps for you. In that case, there's no need to run estimateSizeFactors separately before DESeq.
# draw simple pca and heatmap
#mat <- assay(rld)
#mm <- model.matrix(~condition, colData(rld))
#mat <- limma::removeBatchEffect(mat, batch=rld$batch, design=mm)
#assay(rld) <- mat
# -- pca --
png("pca.png", 1200, 800)
plotPCA(rld, intgroup=c("condition"))
dev.off()
# -- heatmap --
png("heatmap.png", 1200, 800)
distsRL <- dist(t(assay(rld)))
mat <- as.matrix(distsRL)
hc <- hclust(distsRL)
hmcol <- colorRampPalette(brewer.pal(9,"GnBu"))(100)
heatmap.2(mat, Rowv=as.dendrogram(hc),symm=TRUE, trace="none",col = rev(hmcol), margin=c(13, 13))
dev.off()

(Optional (ERROR-->need to be debugged!) ) estimate size factors and dispersion values.

#Size Factors: These are used to normalize the read counts across different samples. The size factor for a sample accounts for differences in sequencing depth (i.e., the total number of reads) and other technical biases between samples. After normalization with size factors, the counts should be comparable across samples. Size factors are usually calculated in a way that they reflect the median or mean ratio of gene expression levels between samples, assuming that most genes are not differentially expressed.
#Dispersion: This refers to the variability or spread of gene expression measurements. In RNA-seq data analysis, each gene has its own dispersion value, which reflects how much the counts for that gene vary between different samples, more than what would be expected just due to the Poisson variation inherent in counting. Dispersion is important for accurately modeling the data and for detecting differentially expressed genes.
#So in summary, size factors are specific to samples (used to make counts comparable across samples), and dispersion values are specific to genes (reflecting variability in gene expression).
sizeFactors(dds)
#NULL
# Estimate size factors
dds <- estimateSizeFactors(dds)
# Estimate dispersions
dds <- estimateDispersions(dds)
#> sizeFactors(dds)
#control_r1 control_r2  HSV.d2_r1  HSV.d2_r2  HSV.d4_r1  HSV.d4_r2  HSV.d6_r1
#2.3282468  2.0251928  1.8036883  1.3767551  0.9341929  1.0911693  0.5454526
#HSV.d6_r2  HSV.d8_r1  HSV.d8_r2
#0.4604461  0.5799834  0.6803681
# (DEBUG) If avgTxLength is Necessary
#To simplify the computation and ensure sizeFactors are calculated:
assays(dds)$avgTxLength <- NULL
dds <- estimateSizeFactors(dds)
sizeFactors(dds)
#If you want to retain avgTxLength but suspect it is causing issues, you can explicitly instruct DESeq2 to compute size factors without correcting for library size with average transcript lengths:
dds <- estimateSizeFactors(dds, controlGenes = NULL, use = FALSE)
sizeFactors(dds)
# If alone with virus data, the following BUG occured:
#Still NULL --> BUG --> using manual calculation method for sizeFactor calculation!
                    HeLa_TO_r1                      HeLa_TO_r2
                    0.9978755                       1.1092227
data.frame(genes = rownames(dds), dispersions = dispersions(dds))
#Given the raw counts, the control_r1 and control_r2 samples seem to have a much lower sequencing depth (total read count) than the other samples. Therefore, when normalization methods are applied, the normalization factors for these control samples will be relatively high, boosting the normalized counts.
1/0.9978755=1.002129023
1/1.1092227=
#bamCoverage --bam ../markDuplicates/${sample}Aligned.sortedByCoord.out.bam -o ${sample}_norm.bw --binSize 10 --scaleFactor  --effectiveGenomeSize 2864785220
bamCoverage --bam ../markDuplicates/HeLa_TO_r1Aligned.sortedByCoord.out.markDups.bam -o HeLa_TO_r1.bw --binSize 10 --scaleFactor 1.002129023     --effectiveGenomeSize 2864785220
bamCoverage --bam ../markDuplicates/HeLa_TO_r2Aligned.sortedByCoord.out.markDups.bam -o HeLa_TO_r2.bw --binSize 10 --scaleFactor  0.901532217        --effectiveGenomeSize 2864785220
raw_counts <- counts(dds)
normalized_counts <- counts(dds, normalized=TRUE)
#write.table(raw_counts, file="raw_counts.txt", sep="\t", quote=F, col.names=NA)
#write.table(normalized_counts, file="normalized_counts.txt", sep="\t", quote=F, col.names=NA)
#convert bam to bigwig using deepTools by feeding inverse of DESeq’s size Factor
estimSf <- function (cds){
    # Get the count matrix
    cts <- counts(cds)
    # Compute the geometric mean
    geomMean <- function(x) prod(x)^(1/length(x))
    # Compute the geometric mean over the line
    gm.mean  <-  apply(cts, 1, geomMean)
    # Zero values are set to NA (avoid subsequentcdsdivision by 0)
    gm.mean[gm.mean == 0] <- NA
    # Divide each line by its corresponding geometric mean
    # sweep(x, MARGIN, STATS, FUN = "-", check.margin = TRUE, ...)
    # MARGIN: 1 or 2 (line or columns)
    # STATS: a vector of length nrow(x) or ncol(x), depending on MARGIN
    # FUN: the function to be applied
    cts <- sweep(cts, 1, gm.mean, FUN="/")
    # Compute the median over the columns
    med <- apply(cts, 2, median, na.rm=TRUE)
    # Return the scaling factor
    return(med)
}
#https://dputhier.github.io/ASG/practicals/rnaseq_diff_Snf2/rnaseq_diff_Snf2.html
#http://bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#data-transformations-and-visualization
#https://hbctraining.github.io/DGE_workshop/lessons/02_DGE_count_normalization.html
#https://hbctraining.github.io/DGE_workshop/lessons/04_DGE_DESeq2_analysis.html
#https://genviz.org/module-04-expression/0004/02/01/DifferentialExpression/
#DESeq2’s median of ratios [1]
#EdgeR’s trimmed mean of M values (TMM) [2]
#http://www.nathalievialaneix.eu/doc/html/TP1_normalization.html  #very good website!
test_normcount <- sweep(raw_counts, 2, sizeFactors(dds), "/")
sum(test_normcount != normalized_counts)

Select the differentially expressed genes

#https://galaxyproject.eu/posts/2020/08/22/three-steps-to-galaxify-your-tool/
#https://www.biostars.org/p/282295/
#https://www.biostars.org/p/335751/
#> dds$condition
#[1] AUM   AUM   AUM   Urine Urine Urine MHB   MHB   MHB
#Levels: AUM MHB Urine
#CONSOLE: mkdir star_salmon/degenes
setwd("degenes")
#---- relevel to control ----
dds$condition <- relevel(dds$condition, "MHB")
dds = DESeq(dds, betaPrior=FALSE)
resultsNames(dds)
clist <- c("AUM_vs_MHB","Urine_vs_MHB")
for (i in clist) {
  contrast = paste("condition", i, sep="_")
  res = results(dds, name=contrast)
  res <- res[!is.na(res$log2FoldChange),]
  res_df <- as.data.frame(res)
  write.csv(as.data.frame(res_df[order(res_df$pvalue),]), file = paste(i, "all.txt", sep="-"))
  up <- subset(res_df, padj<=0.05 & log2FoldChange>=1.35)
  down <- subset(res_df, padj<=0.05 & log2FoldChange<=-1.35)
  write.csv(as.data.frame(up[order(up$log2FoldChange,decreasing=TRUE),]), file = paste(i, "up.txt", sep="-"))
  write.csv(as.data.frame(down[order(abs(down$log2FoldChange),decreasing=TRUE),]), file = paste(i, "down.txt", sep="-"))
}
# -- Under host-env --
grep -P "\tgene\t" CP059040.gff > CP059040_gene.gff
python3 ~/Scripts/replace_gene_names.py /home/jhuang/DATA/Data_Tam_RNAseq_2024/CP059040_gene.gff AUM_vs_MHB-all.txt AUM_vs_MHB-all.csv
python3 ~/Scripts/replace_gene_names.py /home/jhuang/DATA/Data_Tam_RNAseq_2024/CP059040_gene.gff AUM_vs_MHB-up.txt AUM_vs_MHB-up.csv
python3 ~/Scripts/replace_gene_names.py /home/jhuang/DATA/Data_Tam_RNAseq_2024/CP059040_gene.gff AUM_vs_MHB-down.txt AUM_vs_MHB-down.csv
python3 ~/Scripts/replace_gene_names.py /home/jhuang/DATA/Data_Tam_RNAseq_2024/CP059040_gene.gff Urine_vs_MHB-all.txt Urine_vs_MHB-all.csv
python3 ~/Scripts/replace_gene_names.py /home/jhuang/DATA/Data_Tam_RNAseq_2024/CP059040_gene.gff Urine_vs_MHB-up.txt Urine_vs_MHB-up.csv
python3 ~/Scripts/replace_gene_names.py /home/jhuang/DATA/Data_Tam_RNAseq_2024/CP059040_gene.gff Urine_vs_MHB-down.txt Urine_vs_MHB-down.csv
#for i in AUM_vs_MHB Urine_vs_MHB; do
#  echo "contrast = paste(\"condition\", \"${i}\", sep=\"_\")"
#  echo "res = results(dds, name=contrast)"
#  echo "res <- res[!is.na(res$log2FoldChange),]"
#  echo "res_df <- as.data.frame(res)"
#  #selectLab = selectLab_italics,
#  echo "png(\"${i}.png\",width=1200, height=1000)"
#  #legendPosition = 'right',legendLabSize = 12,  arrowheads = FALSE,
#  #subtitle=expression(~Delta*\"$(echo $i | cut -d'_' -f1) versus \" *~Delta*\"$(echo $i | cut -d'_' -f3)\"))"
#  echo "EnhancedVolcano(res, lab = rownames(res),x = 'log2FoldChange',y = 'padj', pCutoff=5e-2, FCcutoff=1.2, title='', subtitleLabSize = 18, pointSize = 3.0, labSize = 5.0, colAlpha=1, legendIconSize = 4.0, drawConnectors = TRUE, widthConnectors = 0.5, colConnectors = 'black', subtitle=expression(\"$(echo $i | cut -d'_' -f1) versus $(echo $i | cut -d'_' -f3)\"))"
#  echo "dev.off()"
#done
res <- read.csv("AUM_vs_MHB-all.csv")
# Replace empty GeneName with modified GeneID
res$GeneName <- ifelse(
  res$GeneName == "" | is.na(res$GeneName),
  gsub("gene-", "", res$GeneID),
  res$GeneName
)
duplicated_genes <- res[duplicated(res$GeneName), "GeneName"]
#print(duplicated_genes)
# [1] "bfr"  "lipA" "ahpF" "pcaF" "alr"  "pcaD" "cydB" "lpdA" "pgaC" "ppk1"
#[11] "pcaF" "tuf"  "galE" "murI" "yccS" "rrf"  "rrf"  "arsB" "ptsP" "umuD"
#[21] "map"  "pgaB" "rrf"  "rrf"  "rrf"  "pgaD" "uraH" "benE"
#res[res$GeneName == "bfr", ]
#1st_strategy First occurrence is kept and Subsequent duplicates are removed
#res <- res[!duplicated(res$GeneName), ]
#2nd_strategy keep the row with the smallest padj value for each GeneName
res <- res %>%
  group_by(GeneName) %>%
  slice_min(padj, with_ties = FALSE) %>%
  ungroup()
res <- as.data.frame(res)
# Sort res first by padj (ascending) and then by log2FoldChange (descending)
res <- res[order(res$padj, -res$log2FoldChange), ]
# Assuming res is your dataframe and already processed
# Filter up-regulated genes: log2FoldChange > 2 and padj < 1e-2
up_regulated <- res[res$log2FoldChange > 2 & res$padj < 1e-2, ]
# Filter down-regulated genes: log2FoldChange < -2 and padj < 1e-2
down_regulated <- res[res$log2FoldChange < -2 & res$padj < 1e-2, ]
# Create a new workbook
wb <- createWorkbook()
# Add the complete dataset as the first sheet
addWorksheet(wb, "Complete_Data")
writeData(wb, "Complete_Data", res)
# Add the up-regulated genes as the second sheet
addWorksheet(wb, "Up_Regulated")
writeData(wb, "Up_Regulated", up_regulated)
# Add the down-regulated genes as the third sheet
addWorksheet(wb, "Down_Regulated")
writeData(wb, "Down_Regulated", down_regulated)
# Save the workbook to a file
saveWorkbook(wb, "Gene_Expression_AUM_vs_MHB.xlsx", overwrite = TRUE)
# Set the 'GeneName' column as row.names
rownames(res) <- res$GeneName
# Drop the 'GeneName' column since it's now the row names
res$GeneName <- NULL
head(res)
## Ensure the data frame matches the expected format
## For example, it should have columns: log2FoldChange, padj, etc.
#res <- as.data.frame(res)
## Remove rows with NA in log2FoldChange (if needed)
#res <- res[!is.na(res$log2FoldChange),]
# Replace padj = 0 with a small value
res$padj[res$padj == 0] <- 1e-150
#library(EnhancedVolcano)
# Assuming res is already sorted and processed
png("AUM_vs_MHB.png", width=1200, height=2000)
#max.overlaps = 10
EnhancedVolcano(res,
                lab = rownames(res),
                x = 'log2FoldChange',
                y = 'padj',
                pCutoff = 1e-2,
                FCcutoff = 2,
                title = '',
                subtitleLabSize = 18,
                pointSize = 3.0,
                labSize = 5.0,
                colAlpha = 1,
                legendIconSize = 4.0,
                drawConnectors = TRUE,
                widthConnectors = 0.5,
                colConnectors = 'black',
                subtitle = expression("AUM versus MHB"))
dev.off()
res <- read.csv("Urine_vs_MHB-all.csv")
# Replace empty GeneName with modified GeneID
res$GeneName <- ifelse(
  res$GeneName == "" | is.na(res$GeneName),
  gsub("gene-", "", res$GeneID),
  res$GeneName
)
duplicated_genes <- res[duplicated(res$GeneName), "GeneName"]
res <- res %>%
  group_by(GeneName) %>%
  slice_min(padj, with_ties = FALSE) %>%
  ungroup()
res <- as.data.frame(res)
# Sort res first by padj (ascending) and then by log2FoldChange (descending)
res <- res[order(res$padj, -res$log2FoldChange), ]
# Assuming res is your dataframe and already processed
# Filter up-regulated genes: log2FoldChange > 2 and padj < 1e-2
up_regulated <- res[res$log2FoldChange > 2 & res$padj < 1e-2, ]
# Filter down-regulated genes: log2FoldChange < -2 and padj < 1e-2
down_regulated <- res[res$log2FoldChange < -2 & res$padj < 1e-2, ]
# Create a new workbook
wb <- createWorkbook()
# Add the complete dataset as the first sheet
addWorksheet(wb, "Complete_Data")
writeData(wb, "Complete_Data", res)
# Add the up-regulated genes as the second sheet
addWorksheet(wb, "Up_Regulated")
writeData(wb, "Up_Regulated", up_regulated)
# Add the down-regulated genes as the third sheet
addWorksheet(wb, "Down_Regulated")
writeData(wb, "Down_Regulated", down_regulated)
# Save the workbook to a file
saveWorkbook(wb, "Gene_Expression_Urine_vs_MHB.xlsx", overwrite = TRUE)
# Set the 'GeneName' column as row.names
rownames(res) <- res$GeneName
# Drop the 'GeneName' column since it's now the row names
res$GeneName <- NULL
head(res)
## Ensure the data frame matches the expected format
## For example, it should have columns: log2FoldChange, padj, etc.
#res <- as.data.frame(res)
## Remove rows with NA in log2FoldChange (if needed)
#res <- res[!is.na(res$log2FoldChange),]
# Replace padj = 0 with a small value
res$padj[res$padj == 0] <- 1e-305
#library(EnhancedVolcano)
# Assuming res is already sorted and processed
png("Urine_vs_MHB.png", width=1200, height=2000)
#max.overlaps = 10
EnhancedVolcano(res,
                lab = rownames(res),
                x = 'log2FoldChange',
                y = 'padj',
                pCutoff = 1e-2,
                FCcutoff = 2,
                title = '',
                subtitleLabSize = 18,
                pointSize = 3.0,
                labSize = 5.0,
                colAlpha = 1,
                legendIconSize = 4.0,
                drawConnectors = TRUE,
                widthConnectors = 0.5,
                colConnectors = 'black',
                subtitle = expression("Urine versus MHB"))
dev.off()

Report

Attached are the results of the analysis.
In the Urine_vs_MHB comparison, we identified a total of 259 up-regulated genes (log2FoldChange > 2 and padj < 1e-2) and 138 down-regulated genes (log2FoldChange < -2 and padj < 1e-2) (please refer to the attached volcano plot and Excel files). Notably, the following genes have a p-adjusted value of 0, indicating very high confidence in their differential expression. The bas-series genes (basA, basB, basC, basD, basE, basJ) are particularly prominent:
GeneName    GeneID  baseMean    log2FoldChange  lfcSE   stat    pvalue  padj
basJ    gene-H0N29_05120    11166.90    11.42   0.30    38.27   0   0
basE    gene-H0N29_05085    12006.52    10.45   0.23    45.76   0   0
basD    gene-H0N29_05080    12217.80    10.15   0.24    42.42   0   0
bauA    gene-H0N29_05070    25280.68    9.55    0.19    51.48   0   0
basA    gene-H0N29_05040    9750.68 9.02    0.18    48.90   0   0
basC    gene-H0N29_05075    5034.14 8.58    0.21    40.07   0   0
H0N29_08320 gene-H0N29_08320    4935.78 7.87    0.20    40.01   0   0
barB    gene-H0N29_05105    5187.29 7.81    0.18    43.39   0   0
H0N29_09380 gene-H0N29_09380    3477.26 7.41    0.19    38.91   0   0
H0N29_13950 gene-H0N29_13950    13959.05    6.85    0.15    45.70   0   0
H0N29_10825 gene-H0N29_10825    3664.70 6.44    0.17    37.59   0   0
H0N29_10790 gene-H0N29_10790    2574.12 6.41    0.17    37.86   0   0
H0N29_10010 gene-H0N29_10010    9376.84 -8.14   0.19    -43.70  0   0
In the AUM_vs_MHB comparison, we identified a total of 149 up-regulated genes (log2FoldChange > 2 and padj < 1e-2) and 65 down-regulated genes (log2FoldChange < -2 and padj < 1e-2) (please refer to the attached volcano plot and Excel files). The following genes also show a p-adjusted value of 0, indicating very high confidence in their differential expression:
GeneName    GeneID  baseMean    log2FoldChange  lfcSE   stat    pvalue  padj
putA    gene-H0N29_09870    36100.24    -7.25   0.15    -49.78  0   0
H0N29_10010 gene-H0N29_10010    9376.84 -7.43   0.18    -41.96  0   0
To ensure proper visualization, I replaced the padj = 0 values with small numbers: 1e-305 for Urine_vs_MHB and 1e-150 for AUM_vs_MHB.
We have now identified the significantly expressed genes. If you would like any further analysis based on these genes or need additional plots, please let me know.

(TODO) clustering the genes and draw heatmap

for i in HSV.d2_vs_control HSV.d4_vs_control HSV.d6_vs_control HSV.d8_vs_control HSV.d4_vs_HSV.d2 HSV.d6_vs_HSV.d2 HSV.d8_vs_HSV.d2 HSV.d6_vs_HSV.d4 HSV.d8_vs_HSV.d4 HSV.d8_vs_HSV.d6; do echo "cut -d',' -f1-1 ${i}-up_annotated.txt > ${i}-up.id"; echo "cut -d',' -f1-1 ${i}-down_annotated.txt > ${i}-down.id"; done
cat *.id | sort -u > ids
#add Gene_Id in the first line, delete the ""
GOI <- read.csv("ids")$Gene_Id  #4647
RNASeq.NoCellLine <- assay(rld)
#install.packages("gplots")
library("gplots")
#clustering methods: "ward.D", "ward.D2", "single", "complete", "average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC) or "centroid" (= UPGMC).  pearson or spearman
datamat = RNASeq.NoCellLine[GOI, ]
#datamat = RNASeq.NoCellLine
write.csv(as.data.frame(datamat), file ="gene_expressions.txt")
constant_rows <- apply(datamat, 1, function(row) var(row) == 0)
if(any(constant_rows)) {
  cat("Removing", sum(constant_rows), "constant rows.\n")
  datamat <- datamat[!constant_rows, ]
}
hr <- hclust(as.dist(1-cor(t(datamat), method="pearson")), method="complete")
hc <- hclust(as.dist(1-cor(datamat, method="spearman")), method="complete")
mycl = cutree(hr, h=max(hr$height)/1.05)
mycol = c("YELLOW", "BLUE", "ORANGE", "MAGENTA", "CYAN", "RED", "GREEN", "MAROON", "LIGHTBLUE", "PINK", "MAGENTA", "LIGHTCYAN", "LIGHTRED", "LIGHTGREEN");
mycol = mycol[as.vector(mycl)]
#png("DEGs_heatmap.png", width=900, height=800)
#cex.lab=10, labRow="",
png("DEGs_heatmap.png", width=800, height=1000)
heatmap.2(as.matrix(datamat),Rowv=as.dendrogram(hr),Colv = NA, dendrogram = 'row',labRow="",
            scale='row',trace='none',col=bluered(75), cexCol=1.8,
            RowSideColors = mycol, margins=c(10,2), cexRow=1.5, srtCol=30, lhei = c(1, 8), lwid=c(2, 8))  #rownames(datamat)
#heatmap.2(datamat, Rowv=as.dendrogram(hr), col=bluered(75), scale="row", RowSideColors=mycol, trace="none", margin=c(5,5), sepwidth=c(0,0), dendrogram = 'row', Colv = 'false', density.info='none', labRow="", srtCol=30, lhei=c(0.1,2))
dev.off()
#### cluster members #####
write.csv(names(subset(mycl, mycl == '1')),file='cluster1_YELLOW.txt')
write.csv(names(subset(mycl, mycl == '2')),file='cluster2_DARKBLUE.txt')
write.csv(names(subset(mycl, mycl == '3')),file='cluster3_DARKORANGE.txt')
write.csv(names(subset(mycl, mycl == '4')),file='cluster4.txt')
#~/Tools/csv2xls-0.4/csv_to_xls.py cluster*.txt -d',' -o DEGs_heatmap_cluster_members.xls
~/Tools/csv2xls-0.4/csv_to_xls.py \
significant_gene_expressions.txt \
-d',' -o DEGs_heatmap_expression_data.xls;
#### cluster members (advanced) #####
subset_1<-names(subset(mycl, mycl == '1'))
data <- as.data.frame(datamat[rownames(datamat) %in% subset_1, ])  #2575
subset_2<-names(subset(mycl, mycl == '2'))
data <- as.data.frame(datamat[rownames(datamat) %in% subset_2, ])  #1855
subset_3<-names(subset(mycl, mycl == '3'))
data <- as.data.frame(datamat[rownames(datamat) %in% subset_3, ])  #217
subset_4<-names(subset(mycl, mycl == '4'))
data <- as.data.frame(datamat[rownames(datamat) %in% subset_4, ])  #
subset_5<-names(subset(mycl, mycl == '5'))
data <- as.data.frame(datamat[rownames(datamat) %in% subset_5, ])  #
# Initialize an empty data frame for the annotated data
annotated_data <- data.frame()
# Determine total number of genes
total_genes <- length(rownames(data))
# Loop through each gene to annotate
for (i in 1:total_genes) {
    gene <- rownames(data)[i]
    result <- 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 = gene,
                    mart = ensembl)
    # If multiple rows are returned, take the first one
    if (nrow(result) > 1) {
        result <- result[1, ]
    }
    # Check if the result is empty
    if (nrow(result) == 0) {
        result <- data.frame(ensembl_gene_id = gene,
                            external_gene_name = NA,
                            gene_biotype = NA,
                            entrezgene_id = NA,
                            chromosome_name = NA,
                            start_position = NA,
                            end_position = NA,
                            strand = NA,
                            description = NA)
    }
    # Transpose expression values
    expression_values <- t(data.frame(t(data[gene, ])))
    colnames(expression_values) <- colnames(data)
    # Combine gene information and expression data
    combined_result <- cbind(result, expression_values)
    # Append to the final dataframe
    annotated_data <- rbind(annotated_data, combined_result)
    # Print progress every 100 genes
    if (i %% 100 == 0) {
        cat(sprintf("Processed gene %d out of %d\n", i, total_genes))
    }
}
# Save the annotated data to a new CSV file
#write.csv(annotated_data, "cluster1_YELLOW.csv", row.names=FALSE)
write.csv(annotated_data, "cluster2_DARKBLUE.csv", row.names=FALSE)
write.csv(annotated_data, "cluster3_DARKORANGE.csv", row.names=FALSE)
write.csv(annotated_data, "cluster4_DARKMAGENTA.csv", row.names=FALSE)
write.csv(annotated_data, "cluster5_DARKCYAN.csv", row.names=FALSE)
#~/Tools/csv2xls-0.4/csv_to_xls.py cluster*.csv -d',' -o DEGs_heatmap_clusters.xls

RNA-seq Tam on Acinetobacter baumannii strain ATCC 19606 CP059040.1 (Data_Tam_RNAseq_2024)

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