foundations-r-fundamentals

name	foundations-r-fundamentals
description	Read/write R syntax for bioinformatics (vectors, data.frame, matrices, d/p/q/r distributions, DESeq2). Use when porting Python to R, debugging R from a paper/pipeline, or running DESeq2/edgeR/Seurat scripts.
tool_type	r
primary_tool	R

R Fundamentals for Bioinformatics

When to Use

• Reading or adapting R code from a published pipeline, DESeq2/edgeR/Seurat vignette, or supplementary script
• Translating a Python data-analysis snippet into R (or vice versa) and need the syntax mapping
• Debugging an off-by-one or "wrong subset" bug caused by R's 1-based, exclude-on-negative indexing
• Setting up a minimal Bioconductor workflow (install, DESeqDataSet, filtering results)
• Choosing/running a basic statistical test in R (t-test, Wilcoxon, chi-squared, correlation)

Version Compatibility

R ≥ 4.2, Bioconductor ≥ 3.17 (BiocManager ≥ 1.30), DESeq2 ≥ 1.40, dplyr ≥ 1.1, ggplot2 ≥ 3.4. Base-R syntax here (<-, data.frame, apply) is stable across R versions back to R 3.x.

Prerequisites

• R installed (R --version), or an R kernel (IRkernel) for Jupyter
• install.packages("BiocManager") for any Bioconductor package (DESeq2, GenomicRanges, ...)
• Comfort with Python's data types helps — this skill is written as a Python→R translation

Python vs R Syntax Traps

Goal: avoid the bugs that come from assuming R behaves like Python. Approach: memorize this table before writing/reading any R code — every row is a real, common bug source when porting between the two languages.

Feature	Python	R
Indexing	Starts at 0	Starts at 1
Assignment	=	<- (preferred; = works but differs inside function calls)
Negative index	x[-1] = last element	x[-1] = all except first
Boolean values	True / False	TRUE / FALSE
Missing data	None	NA
Null	None	NULL
Auto-print	print(x)	Just type x (top level only)
AND/OR	&, | (vectorized)	&, | (vectorized); &&, || (scalar, first element only)

Vectors and Data Frames

Goal: manipulate vectors and data frames the R way. Approach: use vectorized operations and logical indexing instead of loops; use data.frame for tabular data and filter with boolean masks on columns.

# --- Vectors ---
gene_expression <- c(2.5, 3.1, 4.2, 1.8, 5.6)
gene_names <- c("BRCA1", "TP53", "EGFR", "MYC", "KRAS")
positions <- 1:10
coverage <- seq(from = 0, to = 100, by = 10)
groups <- rep(c("control", "treatment"), each = 3)

# Named vector + logical indexing
gc_content <- c(BRCA1 =  TP53   EGFR   MYC  
gc_rich  gc_contentgc_content  


log2fc  log2gene_expression            
pvalue      
sig_up  log2fc    pvalue            
cat gene_namessig_up 
whichsig_up                                  


gene_data  data.frame
    gene    
    log2fc    
    pvalue    
    stringsAsFactors     


gene_datagene                  
gene_data                   
gene_data           
gene_datagene_datalog2fc    gene_datapvalue     

gene_datapadj  p.adjustgene_datapvalue method     
gene_dataordergene_datapvalue                             

Matrices and Statistical Distributions

Goal: work with expression matrices and distribution functions. Approach: use matrix() with dimnames for gene-by-sample data and apply() for row/column stats; use the d/p/q/r prefix family for any distribution.

# --- Matrices (expression matrices) ---
expr_matrix <- matrix(
    c(5.2, 3.1, 8.5, 6.2, 4.8, 2.9, 7.1, 5.8),
    nrow = 2, byrow = TRUE,
    dimnames = list(c("Sample1", "Sample2"), c("BRCA1", "TP53", "EGFR", "MYC"))
)

expr_matrix[2, "EGFR"]          # specific cell
expr_matrix[1                 
expr_matrix           
applyexpr_matrix  mean     
applyexpr_matrix  sd       


set.seed                     
rnormn   mean   sd      
pnormq   lower.tail     
qnormp                       
rnbinomn   mu   size    


control  rnorm mean   sd  
treatment  rnorm mean   sd  
t.testcontrol treatmentp.value
wilcox.testcontrol treatmentp.value      
chisq.testtable 

Minimal DESeq2 Workflow

Goal: run a minimal DESeq2 differential-expression analysis. Approach: build a DESeqDataSet from a raw count matrix + sample metadata, run DESeq(), extract a contrast, then filter by FDR and effect size.

# Install once
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")
BiocManager::install("DESeq2")

library(DESeq2)

#' Run DESeq2 and return significant genes for a two-group contrast.
#' @param counts   integer matrix, genes x samples (raw counts, NOT normalized)
#' @param metadata data.frame, one row per sample, must include `condition`
#' @param alpha    FDR threshold (default 0.05)
#' @param lfc      minimum absolute log2 fold-change to call significant
#' @return data.frame of significant genes sorted by adjusted p-value
run_deseq2 <- function(counts, metadata, alpha = 0.05, lfc = 1) {
    dds <- DESeqDataSetFromMatrix(countData = counts, colData  metadata design  condition
    dds  DESeqdds
    res  resultsdds contrast    
    res_df  as.data.frameres
    sig  res_dfres_dfpadj  res_dfpadj  alpha  res_dflog2FoldChange  lfc 
    sigordersigpadj 



df  read.csv stringsAsFactors  
df  read.delim sep  
write.csvdf  row.names  

# Self-check: run_deseq2() end to end on synthetic count data
demo_deseq2 <- function() {
    set.seed(1)
    counts <- matrix(rnbinom(400, mu = 100, size = 5), nrow = 100, ncol = 4,
                      dimnames = list(paste0("gene", 1:100), paste0("s", 1:4)))
    metadata <- data.frame(condition = factor(c("control", "control",  
                            row.names  colnamescounts
    sig  run_deseq2counts metadata
    stopifnotis.data.framesig
    stopifnot   colnamessig
    cat nrowsig  nrowcounts 

demo_deseq2

Pitfalls

• R indexing starts at 1: x[1] is first; x[-1] means "all except first" (NOT last element like Python)
• Factors look like strings but are not: a factor stores integer codes + labels; functions expecting strings will misbehave — use stringsAsFactors = FALSE in data.frame() or as.character() to convert
• library() vs require(): use library() in scripts — it errors loudly if a package is missing; require() returns FALSE silently and can hide broken pipelines
• Data frames vs matrices: Bioconductor functions often require numeric matrices; a data frame with character columns cannot be used directly — convert with as.matrix(df[, numeric_cols])
• <- inside function arguments: f(x <- 1) assigns x in the enclosing scope AND passes the value as the argument; use = for keyword arguments to avoid the side effect
• Multiple testing: p.adjust(..., method = "BH") (Benjamini-Hochberg FDR) is the genomics standard; Bonferroni is overly conservative for large gene sets
• Scalar vs vectorized AND/OR: && and || only evaluate the first element — always use & and | when filtering vectors/data frames

See Also

• foundations-r-hypothesis-testing-and-nonparametrics — Shapiro-Wilk, Wilcoxon, ANOVA, correlation tests in depth
• foundations-r-regression-correlation-and-diagnostics — linear models, lm(), diagnostic plots
• foundations-biostatistics-fundamentals — statistical concepts underlying these tests
• bio-differential-expression-deseq2-basics — full DESeq2 workflow beyond this quick-start