Hello. I am running DEseq2 analysis on published RNAseq data consisting of 4 control samples and 4 cKO samples. However my log2FC values are inverted compared to the original publication. I think the issue is with how I define the control group (#set factor level) as the plotted data is always appears the same irrespective of what group I select (Control or cKO). I am struggling to find a solution here as I am new to DEseq2 so I am reaching out for some help. I attach the volcano plot images for comparison and my script. Thanks in advance.

#load libraries
library(dplyr)
library(tidyverse)
library(GEOquery)

#prepare data
reads <-read.csv(file = "GSE172457.csv")
colnames(reads) [1] <- "gene"
colnames(reads) [2] <- "GSM5257109"
colnames(reads) [3] <- "GSM5257110"
colnames(reads) [4] <- "GSM5257111"
colnames(reads) [5] <- "GSM5257112"
colnames(reads) [6] <- "GSM5257113"
colnames(reads) [7] <- "GSM5257114"
colnames(reads) [8] <- "GSM5257115"
colnames(reads) [9] <- "GSM5257116"
reads <- reads %>% separate(gene, into = c("ENSEMBL", "gene","gene_biotype"), convert = TRUE)
reads <- reads[, -c(2,3)]
rownames(reads) <- reads$ENSEMBL
reads <- reads[, -c(1)]

#get metadata
gse<- getGEO(GEO = 'GSE172457', GSEMatrix = TRUE)
metadata <- pData(phenoData(gse[[1]]))

#select column subsets from metadata and rename columns
metadata.subset <- metadata %>%
  select(1) %>%
  rename(genotype = title) %>%
  mutate(genotype = gsub("replicate 1", "", genotype)) %>%
  mutate(genotype = gsub("replicate 2", "", genotype)) %>%
  mutate(genotype = gsub("replicate 3", "", genotype)) %>%
  mutate(genotype = gsub("replicate 4", "", genotype))

#check sample information
all(colnames(reads) %in% rownames(metadata.subset))
all(colnames(reads) == rownames(metadata.subset))

#construct a DESeqDataSet
library(DESeq2)
dataset <- DESeqDataSetFromMatrix(countData = reads,
                                  colData = metadata.subset,
                                  design = ~ genotype)

#filtering data
#only keep rows that have at least 20 reads
dataset.filter <- rowSums(counts(dataset)) >= 20
dataset <- dataset[dataset.filter,]

#set factor level
metadata.subset$genotype <- as.factor(metadata.subset$genotype)
metadata.subset$genotype <- relevel(metadata.subset$genotype, ref = "Control ")

#run DEseq with p-value of 0.0001
dataset <- DESeq(dataset)
results <- results(dataset, alpha = 0.0001)
summary(results)

#load libraries for volcano plotting
library(org.Mm.eg.db)
library(EnhancedVolcano)

#convert results to a dataframe and add gene names
results.df <- as.data.frame(results)
results.df$symbol <- mapIds(org.Mm.eg.db, keys = rownames(results.df), keytype = "ENSEMBL", column = "SYMBOL")

#volcano plot
EnhancedVolcano(results.df, x = "log2FoldChange", y = "padj", lab = results.df$symbol,
                pCutoff = 1e-4, FCcutoff = 1)


> sessionInfo( )
R version 4.2.2 (2022-10-31)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Ventura 13.0.1

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] EnhancedVolcano_1.16.0      ggrepel_0.9.2               org.Mm.eg.db_3.16.0         AnnotationDbi_1.60.0        DESeq2_1.38.2              
 [6] SummarizedExperiment_1.28.0 MatrixGenerics_1.10.0       matrixStats_0.63.0          GenomicRanges_1.50.2        GenomeInfoDb_1.34.4        
[11] IRanges_2.32.0              S4Vectors_0.36.1            GEOquery_2.66.0             Biobase_2.58.0              BiocGenerics_0.44.0        
[16] forcats_0.5.2               stringr_1.5.0               purrr_1.0.0                 readr_2.1.3                 tidyr_1.2.1                
[21] tibble_3.1.8                ggplot2_3.4.0               tidyverse_1.3.2             dplyr_1.0.10                biomaRt_2.54.0             

loaded via a namespace (and not attached):
 [1] bitops_1.0-7           fs_1.5.2               lubridate_1.9.0        bit64_4.0.5            RColorBrewer_1.1-3     filelock_1.0.2        
 [7] progress_1.2.2         httr_1.4.4             tools_4.2.2            backports_1.4.1        utf8_1.2.2             R6_2.5.1              
[13] DBI_1.1.3              colorspace_2.0-3       withr_2.5.0            tidyselect_1.2.0       prettyunits_1.1.1      bit_4.0.5             
[19] curl_4.3.3             compiler_4.2.2         cli_3.5.0              rvest_1.0.3            xml2_1.3.3             DelayedArray_0.24.0   
[25] labeling_0.4.2         scales_1.2.1           rappdirs_0.3.3         digest_0.6.31          R.utils_2.12.2         XVector_0.38.0        
[31] pkgconfig_2.0.3        dbplyr_2.2.1           fastmap_1.1.0          limma_3.54.0           rlang_1.0.6            readxl_1.4.1          
[37] rstudioapi_0.14        RSQLite_2.2.20         farver_2.1.1           generics_0.1.3         jsonlite_1.8.4         BiocParallel_1.32.5   
[43] R.oo_1.25.0            googlesheets4_1.0.1    RCurl_1.98-1.9         magrittr_2.0.3         GenomeInfoDbData_1.2.9 Matrix_1.5-3          
[49] Rcpp_1.0.9             munsell_0.5.0          fansi_1.0.3            R.methodsS3_1.8.2      lifecycle_1.0.3        stringi_1.7.8         
[55] zlibbioc_1.44.0        BiocFileCache_2.6.0    grid_4.2.2             blob_1.2.3             parallel_4.2.2         crayon_1.5.2          
[61] lattice_0.20-45        Biostrings_2.66.0      haven_2.5.1            annotate_1.76.0        hms_1.1.2              KEGGREST_1.38.0       
[67] locfit_1.5-9.6         pillar_1.8.1           geneplotter_1.76.0     codetools_0.2-18       reprex_2.0.2           XML_3.99-0.13         
[73] glue_1.6.2             data.table_1.14.6      modelr_0.1.10          png_0.1-8              vctrs_0.5.1            tzdb_0.3.0            
[79] cellranger_1.1.0       gtable_0.3.1           assertthat_0.2.1       cachem_1.0.6           xtable_1.8-4           broom_1.0.2           
[85] googledrive_2.0.0      gargle_1.2.1           memoise_2.0.1          timechange_0.1.1       ellipsis_0.3.2        
>

See vignette, note on factor levels.