Supplementary Data for Coale et al. 2025

Supplementary Data 1-16 for Coale et al. 2025. 01: P. calceolata physiology data from Fe/light co-limitation experiment including cell concentraions, chlorophyll a, C, N, Fe, Cu and protein cellular contents. 02: P. calceolata transcriptomic data. Counts and CPM of P. calceolata transcripts from Fe/light co-limitation experiment. 03: Annotations and abbreviations for P. calceolata gene models. 04: Fasta file of P. calceolata gene model coding sequences. 05: Fasta file of P. calceolata gene model amino acid sequences. 06: edgeR comparisons made using transcriptomic data. 07: Gene membership and GO enrichment in WGCNA modules. 08: Proteomics intensities (normalized and imputed). 09: Proteomics DE analysis via limma. 10: Results of Fe x Light interaction test - physiological parameters. 11: Results of Fe x Light interaction test - transcriptomics. 12: Results of Fe x Light interaction test - proteomics. 13: P. calceolata dyneins - class, Fe sensitivity, and protein sequence. 14: Environnmental data from the NCOG project. 15: Transcriptomics response types determine with edgeR. 16: Results of imputation analysis of proteomics data. Source data for Figures: SourceData.xlsx Code 01: R script for edgeR analysis of transcriptomes. 02: R script for limma analysis of proteomes. README for code: README – RNA-seq and Proteomics Differential Expression Pipelines================================================================ - Code_01_Transcriptomics_edgeR.R reads `SupplementaryData_02_transcriptomics.xlsx`.- Code_02_Proteomics_limma.R reads `SupplementaryData_08_proteomics.csv`. ----------------------------------------------------------------1. Contents---------------------------------------------------------------- This Zenodo deposit (10.5281/zenodo.17859961) includes: - Scripts - Code_01_Transcriptomics_edgeR.R - RNA-seq differential expression and Fe × Light interaction (edgeR) - Code_02_Proteomics_limma.R - Proteomics differential abundance analyses (limma) ----------------------------------------------------------------2. Software Requirements---------------------------------------------------------------- - R 4.3.x- CRAN packages - edgeR ≥ 3.42 - limma ≥ 3.56 - readxl ≥ 1.4 - readr ≥ 2.1 - dplyr ≥ 1.1 - tidyr ≥ 1.3 - stringr ≥ 1.5 - matrixStats ≥ 1.3 - ggplot2 ≥ 3.5 - patchwork ≥ 1.2- BiocManager ≥ 1.30 ----------------------------------------------------------------3. RNA-seq Differential Expression (edgeR)---------------------------------------------------------------- 3.1 Input-------- - File: `SupplementaryData_02_transcriptomics.xlsx` - Sheet: `mapped_read_counts` - Column 1: gene IDs - Remaining columns: raw integer read counts for each RNA-seq sample. The script: 1. Reads the Excel sheet using `readxl::read_excel`.2. Converts all count columns to numeric.3. Uses a manually defined `group` vector that encodes each sample as a treatment combination (e.g., `HLpDay`, `LLmNight`, `HLd00`, `HLr03`, etc.), corresponding to Light (HL/LL), Fe status (p/m/d/r), and time of day (Day/Night/diel times). 3.2 edgeR GLM contrasts----------------------- The first part of the script: 1. Constructs a `DGEList` and performs TMM normalization.2. Fits a negative binomial GLM with design `~ 0 + group`.3. Defines a panel of pairwise contrasts using `makeContrasts`, including: - HL vs LL within a given Fe and time (e.g., `HLpDayvsLLpDay`, `HLmNightvsLLmNight`) - pFe vs −Fe within HL or LL (e.g., `HLpDayvsHLmDay`, `LLpNightvsLLmNight`) - Day vs Night within Fe × Light (e.g., `HLmDayvsHLmNight`, `LLpDayvsLLpNight`)4. For each contrast, runs `glmLRT`, extracts the full ranked gene table with: - log2 fold change, logCPM, LR, PValue, FDR5. Combines all contrasts into a single data frame and writes: Output: - `all_contrasts_with_FDR.csv` - A wide table where each contrast contributes five columns (`logFC_*`, `logCPM_*`, `LR_*`, `PValue_*`, `FDR_*`). Each row corresponds to one gene ID. 3.3 Fe × Light interaction (RNA-seq)------------------------------------ The second part of the script focuses on the Fe × Light interaction, restricting to +Fe (p) and −Fe (m) samples: 1. Builds a `meta` data frame from column names and the `group` vector, with: - `Light` (LL / HL) - `Fe` (p / m / d / r) - `TimeOfDay` (Day / Night; coded times like 00, 23, 03 are treated as Day when not explicitly labeled).2. Filters to +Fe vs −Fe only (Fe ∈ {p, m}) and drops resupply (r) and DFOB (d) conditions.3. Runs an edgeR quasi-likelihood pipeline with design: design_int <- model.matrix(~ Fe * Light + TimeOfDay, data = meta_int) which includes main effects for Fe and Light and their interaction term, plus TimeOfDay as a covariate.4. Identifies the Fe:Light interaction coefficient and runs `glmQLFTest` on that term.5. Outputs a full ranked table of genes with interaction statistics: Outputs (in directory `interaction_rna/`): - `rna_FeXLight_interaction_edgeR.csv` - Columns: `gene_id`, `logFC`, `logCPM`, `F`, `PValue`, `FDR` - `logFC` is the estimated effect of the Fe × Light interaction on expression.- `rna_FeXLight_interaction_summary.csv` - Summary counts of genes tested and the number passing FDR < 0.05 and FDR < 0.10. ----------------------------------------------------------------4. Proteomics Differential Abundance (limma)---------------------------------------------------------------- 4.1 Input-------- - File: `SupplementaryData_08_proteomics.csv` - Contains normalized, imputed protein intensities. - The script: - Skips lines 2–3 using `read_csv_drop_lines()` (to remove extra annotation rows). - Expects one column named exactly `id` for protein IDs. - Treats all remaining columns that match the pattern `^(HL|LL)([pmr])(00|11|23)([A-Z])$` as sample columns, where: - `HL` / `LL` = light treatment - `p` / `m` / `r` = +Fe, −Fe, or resupply - `00`, `11`, `23` = sampling time codes - `A`, `B`, `C` = biological replicates 4.2 Preprocessing----------------- 1. Builds a sample metadata (`md`) with: - `Light` ∈ {LL, HL} - `Fe` ∈ {p, m, r} - `Tcode` ∈ {11, 00, 23} - `cond` = interaction of `Light.Fe.Tcode` (e.g., `HL.m.23`).2. Extracts the expression matrix `X`, converts to numeric, and sets rownames to protein IDs.3. Applies log2 transform with a pseudocount (offset = 1), then `normalizeBetweenArrays` (quantile normalization).4. Removes proteins with zero variance across samples using `rowSds`. 4.3 Design and contrasts------------------------ 1. Builds a design matrix with one coefficient per observed `cond`: design <- model.matrix(~ 0 + cond, data = md) 2. Fits a linear model with `lmFit` and `eBayes` (trend = TRUE, robust = TRUE).3. Programmatically constructs a series of numeric contrast vectors (stored in list `C`), including: - Fe effects within light at “day” sampling: - (−Fe “day”) − (+Fe “day”) for each light level (LL, HL), pooling Day00/Day23 where appropriate. - Resupply vs +Fe within light at day: - Resupply “day” vs +Fe “day” for LL and HL. - Day vs Night within each Fe × Light combination: - (Fe “day”) − (Fe “night”) for p, m, r in LL and HL. - HL vs LL within a given Fe at day: - (HL, Fe, day) − (LL, Fe, day) against their appropriate Day timepoints. All contrasts are built only if the required timepoints/conditions exist in the design (the code skips missing combinations safely). 4.4 Outputs----------- All results are written to the folder: - `limma_results/` Key files: - Per-contrast differential abundance tables - For each contrast name (e.g., `HL_Day_r_vs_p`, `LL_Day_m_vs_p`, `HLvsLL_p_Day`), the script writes: - `limma_results/<contrast>.csv` - Columns: - `ProteinID` - `logFC` - `AveExpr` - `t` - `P.Value` - `adj.P.Val` - `B` - Summaries - `limma_results/summary_counts.csv` - Basic counts of significant proteins per contrast. - `limma_results/summary_counts_with_pct.csv` - Adds `n_tested`, `pct_FDR_5`, and counts of up- and down-regulated proteins at FDR ≤ 0.05. - Design and contrast metadata - `limma_results/design_columns.csv` - Lists the design matrix coefficients. - `limma_results/<contrast>_weights.csv` for each contrast - Records the non-zero coefficient weights used to build that contrast. ----------------------------------------------------------------5. Running the Scripts---------------------------------------------------------------- From R (or RStudio), set the working directory to the folder containing the scripts and supplemental data, for example: setwd("path/to/unzipped_zenodo_archive") Then: - RNA-seq differential expression + Fe × Light interaction source("path_to_RNAseq_script.R") - Proteomics limma analyses source("path_to_proteomics_script.R") (Replace `path_to_RNAseq_script.R` and `path_to_proteomics_script.R` with the actual filenames in this archive.) Provided the paths at the top of each script (`infile`, `outdir`, and any hard-coded paths) point to the included supplementary data files, the scripts will reproduce the RNA and protein differential expression results used in the manuscript and supplementary figures.