Example code for Human analysis • rRMSAnalyzer

Creation of a new folder in the repository

open your repository, go to your folder of interest
- create a new folder
- copy and paste in this new folder:

metadata.csv created previously: to update it, open metadata.csv in Excel, fill in the file and save it using .csv with “;” as separator.
rRMSAnalyzer_template.R (https://github.com/RibosomeCRCL/rRMSAnalyzer/tree/main/vignettes)
“human.methylated.rda” and “human.suspected.rda” (https://github.com/RibosomeCRCL/rRMSAnalyzer/tree/main/data)
a folder with all the 5’end read counts files

Perform analysis using RStudio

Open RStudio

Update RMSAnalyzer

library(devtools)
devtools::install_github("RibosomeCRCL/rRMSAnalyzer")
library(rRMSAnalyzer)

Create a RiboClass (metadata, counts, C-score parameters)

As an example, the provided dataset “ribo_toy” is used.

data("ribo_toy")

Personal data preparation

Prepare the comparison table

open the metadata.csv using Excel
add as many new columns as required (1 column per comparison, here “column name” = “variableA to C”)
complete the new columns with
- ctrl = control condition
- P1 = condition 1
- P2 = condition 2….
  • NA = not applicable
  Save your metadata file using .csv and separator “;”

Here is an example of metadata:

filename	samplename	variableA	variableB	variableC	treatment	condition	lib
S26_R1.5_counts.csv	variableB_N2_P2	NA	P2	NA	variableB	P2	L2
S27_R1.5_counts.csv	variableA_N2_P1	P1	NA	NA	variableA	P1	L2
S28_R1.5_counts.csv	variableC_N1_ctrl	NA	NA	ctrl	variableC	ctrl	L2
S30_R1.5_counts.csv	variableB_N1_P2	NA	P2	NA	variableB	P2	L2
S31_R1.5_counts.csv	variableC_N2_P2	NA	NA	P2	variableC	P2	L2
S32_R1.5_counts.csv	variableA_N3_P1	P1	NA	NA	variableA	P1	L2
S33_R1.5_counts.csv	variableA_N3_ctrl	ctrl	NA	NA	variableA	ctrl	L2
S34_R1.5_counts.csv	variableC_N1_ctrl	NA	NA	ctrl	variableC	ctrl	L2
S35_R1.5_counts.csv	variableA_N1_P2	P2	NA	NA	variableA	P2	L2
S36_R1.5_counts.csv	variableA_N2_ctrl	ctrl	NA	NA	variableA	ctrl	L2
S37_R1.5_counts.csv	variableC_N2_ctrl	NA	NA	ctrl	variableC	ctrl	L2
S38_R1.5_counts.csv	variableA_N2_P2	P2	NA	NA	variableA	P2	L2
S39_R1.5_counts.csv	variableC_N1_P2	NA	NA	P2	variableC	P2	L2
S40_R1.5_counts.csv	variableB_N1_ctrl	NA	ctrl	NA	variableB	ctrl	L2
S41_R1.5_counts.csv	variableA_N1_P1	P1	NA	NA	variableA	P1	L2
S42_R1.5_counts.csv	variableA_N1_ctrl	ctrl	NA	NA	variableA	ctrl	L2
S43_R1.5_counts.csv	variableC_N3_P2	NA	NA	P2	variableC	P2	L2
S44_R1.5_counts.csv	variableB_N3_ctrl	NA	ctrl	NA	variableB	ctrl	L2
S45_R1.5_counts.csv	variableA_N3_P2	P2	NA	NA	variableA	P2	L2

In Rstudio, load a newRiboClass using:

ribo <- load_ribodata(
  #data & metadata files path
  count_path = "~/Desktop/RMS/path/counts/",
  metadata = "~/Desktop/RMS/path/metadata.csv",# data & metadata files separator
  count_sep = "\t",
  metadata_sep = ";",
  # count data parameters :
  count_header = FALSE,
  count_value = 3,
  count_rnaid = 1,
  count_pos = 2,
  # Metadata parameters :
  metadata_key = "filename",
  metadata_id = "samplename",
  # c-score parameters :
  flanking = 6,
  method = "median",
  ncores = 1)

Check importation by ploting a PCA and COA

plot_pca(ribo,"run") #change column name of the lib if necessary #ribo_toy lib_col = "run"

plot_coa(
  ribo,
  color_col = NULL,
  axes = c(1, 2),
  only_annotated = FALSE,
  title = "default",
  subtitle = "default",
  object_only = FALSE
)

Perfom QC analysis

Generate a QCreport

report_qc(ribo = ribo, specie = "human", library_col = "run", project_name = "my_project") #ribo_toy lib_col = "run"

Adjustment using ComBat-Seq if necessary

ribo_adj <- adjust_bias(ribo_toy,"run") 

#verify the adjustment using a PCA
plot_pca(ribo_adj,"lib")  #ribo_toy lib_col = "run"         

report_qc(ribo = ribo_adj, specie = "human", library_col = "run", project_name = "name", comments = "./comment_QC.Rmd") #ribo_toy lib_col = "run"

Remove problematic or unused samples (optionnal)

To keep a small number of samples

ribo_adj_small <- keep_ribo_samples(ribo_adj,c("sample1","sample2","sample3","sample4"))

To remove a small number of samples

ribo_adj_small <- remove_ribo_samples(ribo_adj,c("xxxx","xxxccc","RNA_ref-1","RNA_ref-2")) # ribo_toy : ...,c("RNA1", "RNA2"))

Uniformisation of the RiboClass name

ribo_adj <- ribo_adj_small

Annotation of human rRNA 2’Ome sites

Choose between 2 annotations: known (n=112 sites) or suspected (n=17). This section or the next one. If the two annotations are required, both of known and suspected sites analysis are desired, the 2 scripts has to be run twice from here.

Annotation of the 112 known sites

data("human_methylated")
cat("human_methylated's rna names: ", unique(human_methylated$rRNA),"\n")
cat("ribo's rna names: ", as.character(ribo$rna_names$current_name)) # change name of RiboClass if necessary

ribo_adj_name <- rename_rna(ribo, #change name of RiboClass if necessary for ribo_adj if you adjusted the data
                            new_names = c("5S", "5.8S", "18S", "28S"))

ribo_adj_annot <- annotate_site(ribo_adj_name, 
                                annot = human_methylated,
                                anno_rna = "rRNA",
                                anno_pos = "Position",
                                anno_value = "Nomenclature")

Annotation of the 19 suspected sites

load(file = "/home/path/to/RMS/human.suspected.rda") 

ribo_adj_annot <- annotate_site(ribo_adj_annot, human.suspected) # verify rRNA names

ribo_adj_annot <- annotate_site(ribo_adj_annot,
                                annot = human.suspected,
                                anno_rna = "rRNA",
                                anno_pos = "Position",
                                anno_value = "Nomenclature")

Analytic reports

Reports for comparisons explained in column 1

In the previous example: in column “VariableA”, comparison ctrl vs P1 and ctrl vs P2

Filter the data

To do only if you want to compare a subset of your samples (i.e., not all the samples included in the RiboClass).

kept_samples <- ribo_adj_annot$metadata %>%
  # keep lines that are not "NA"
  dplyr::filter(!is.na(variableA)) %>% #ribo_toy (!is.na(comp1))
  dplyr::pull(samplename)

#create a new RiboClass including the subdata variableA
ribo_adj_annot_variableA <- keep_ribo_samples(ribo_adj_annot,kept_samples) #ribo_toy ribo_adj_annot_comp1 <- ...

Create mandatory comparison table for the report_diff_sites

comparisons <- tibble::tibble(
  comp = c("comp1", "comp2"), #name of the 2 comparisons, add as many as required
  ctrl = c("ctrl", "ctrl"), #name of the ctrl as appearing in your metadata for each comparison, in the order, add as many name than comparison that will be performed
  cases = c("P1", "P2") #name of the cases as appearing in your metadata for each comparison, in the order, add as many name than comparison that will be performed
)

Generate a report to compare 2’Ome profile

report_2ome_sites(ribo = ribo_adj_annot_variableA, specie = "human", condition_col = "variableA", project_name = "name", comments = "./path/to/comment_2ome_variableA.Rmd") 
#condition_col: change the name of the column to perform new analysis. ribo_toy condition_col = "comp1"
#comments: change the path and name of your files with your comments if required

Generate a report to perform site-by-site 2’Ome comparison

report_diff_sites(ribo = ribo_adj_annot_variableA, specie = "human", condition_col = "variableA", project_name = "name", comparisons = comparisons, comments = "./comment_diff_site_variableA.Rmd")
#ribo_toy : ribo = ribo_adj_annot_comp1
#condition_col: change the name of the column to perform new analysis . ribo_toy condition_col = "comp1"
#comparisons: name of the comparison table
#comments: change the name of your files with your comments if required

Reports for comparisons explained in column 2

Re-do all the steps of Reports for comparisons explained in column 1 but using the name of your column of interest

Export data

ribo_df <- extract_data(ribo_adj_annot, #change for the annotated RiboClass if needed
                        col = "cscore",
                        only_annotated = TRUE)

write.csv(ribo_df, "/path/to/folder/cscore_df.csv") #give path

Check local environments of sites of interest (optionnal)

If you want to verify a site that shows up as significant in the last report

For all samples

plot_counts_env(ribo_adj_annot, rna= "18S", pos = 1442, samples = "all", condition = "condition")

For some samples

plot_counts_env(ribo_adj_annot, rna = "18S", pos = 1442, samples = c("sample1", "sample2"))

Session info

sessionInfo()
#> R version 4.5.1 (2025-06-13)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.3 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
#>  [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
#>  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
#> [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   
#> 
#> time zone: UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> loaded via a namespace (and not attached):
#>  [1] digest_0.6.37     desc_1.4.3        R6_2.6.1          fastmap_1.2.0    
#>  [5] xfun_0.53         cachem_1.1.0      knitr_1.50        htmltools_0.5.8.1
#>  [9] rmarkdown_2.29    lifecycle_1.0.4   cli_3.6.5         sass_0.4.10      
#> [13] pkgdown_2.1.3     textshaping_1.0.3 jquerylib_0.1.4   systemfonts_1.2.3
#> [17] compiler_4.5.1    tools_4.5.1       ragg_1.5.0        evaluate_1.0.5   
#> [21] bslib_0.9.0       yaml_2.3.10       jsonlite_2.0.0    rlang_1.1.6      
#> [25] fs_1.6.6          htmlwidgets_1.6.4