RamEx: An R package for high-throughput microbial ramanome analyses with accurate quality assessment

Key features

• Reliability achieved via stringent statistical control
• Robustness achieved via flexible modelling of the data and automatic parameter selection
• Reproducibility promoted by thorough recording of all analysis steps
• Ease of use: high degree of automation, an analysis can be set up in several mouse clicks, no bioinformatics expertise required
• Powerful tuning options to enable unconventional experiments
• Scalability and speed: up to 100 runs processed per minutes

More Details:

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Quick Start

Installation

remotes::install_github("qibebt-bioinfo/RamEx")

Data Loading

library(RamEx)
library(magrittr)
data(RamEx_data)

Pretreatment

RamEx_data %<>%  Preprocessing.Smooth.Sg %>% Preprocessing.Baseline.Polyfit %>% Preprocessing.Normalize(.,'ch') 
mean.spec(RamEx_data$normalized.data, RamEx_data$group)  

Quality control

qc_icod <- Qualitycontrol.ICOD(RamEx_data$normalized.data,var_tol = 0.5)
data_cleaned <- RamEx_data[qc_icod$quality,] 
mean.spec(data_cleaned$normalized.data, data_cleaned$group,0.3)

Interested Bands

data_cleaned <- Feature.Reduction.Intensity(data_cleaned, list(c(2000,2250),c(2750,3050), 1450, 1665))
# calculate CDR
CDR <- data.frame(data_cleaned@meta.data,
                  data_cleaned$`2000~2250`/(data_cleaned$`2000~2250` + data_cleaned$`2750~3050`))

Reduction

data.reduction <- Feature.Reduction.Umap(data_cleaned, draw=T, save = F)

Markers analysis

ROC_markers <- Raman.Markers.Roc(data_cleaned$normalized.data,data_cleaned$group, threshold = 0.8) 
cor_markers <- Raman.Markers.Correlations(data_cleaned$normalized.data,as.numeric(data_cleaned$group), min.cor = 0.8) 

IRCA

bands_ann <- data.frame(rbind(cbind(c(742,850,872,971,997,1098,1293,1328,1426,1576),'Nucleic acid'),
                              cbind(c(824,883,1005,1033,1051,1237,1559,1651),'Protein'),
                              cbind(c(1076,1119,1370,2834,2866,2912),'Lipids')))
colnames(bands_ann) <- c('Wave_num', 'Group')
Intraramanome.Analysis.Irca.Local(data_cleaned, bands_ann = bands_ann)

Phenotype analysis

clusters_louvain <- Phenotype.Analysis.Louvain(object = data_cleaned, resolutions = c(0.8)) 

Classifications

model.svm <- Classification.Svm(data_cleaned)

Quantifications

quan_pls <- Quantification.Pls(data_cleaned) 

Spectral decomposition

decom_mcr <- Spectral.Decomposition.Mcrals(data_cleaned,2)

Raw data formats

It accommodates data from mainstream instrument manufactures such as Horiba, Renishaw, Thermo Fisher Scientific, WITec, and Bruker. This module efficiently manages single-point data collection, where each spectrum is stored in a separate txt file, as well as mapping data enriched with coordinate information. Specifically, RamEx automatically interpret and reshape various spectral data layouts, supporting both row‑wise and column‑wise sample arrangements.
File structure detection rules: Type 1: Two columns (wavenumber, intensity) Type 2: Mapping matrix — first column is wavenumber, remaining columns are multiple spectra Type 3: Coordinate scan — columns 1–n are (x1, ..., xn) metadata annotations, column n+1 is wavenumber, column n+2 is intensity The above applies to the sample of rows as well.

Key papers

RamEx
Zhang Y., Jing G., ..., Xu J., Sun L., 2025. RamEx: An R package for high-throughput microbial ramanome analyses with accurate quality assessment. bioRxiv

IRCA
He Y., Huang S., Zhang P., Ji Y., Xu J., 2021. Intra-Ramanome Correlation Analysis Unveils Metabolite Conversion Network from an Isogenic Population of Cells. mBio

RBCS
Teng L., ..., Huang W.E., Xu J., 2016. Label-free, rapid and quantitative phenotyping of stress response in e. coli via ramanome. Scientific Reports

Contact

Any questions, feedback, comments or suggestions, please create a GitHub issue or email us.