omiprep

The goal of omiprep is to:

1. Read in and processes various ’omics data, saving datasets in tab-delimited format for use elsewhere
2. Provide useful summary data in the form of tab-delimited text file and a html report.
3. Perform data filtering on the data set using a standard pipeline and according to user-defined thresholds.

Installation

You can install the latest version of omiprep from GitHub with:

# install.packages("pak")
pak::pak("MRCIEU/omiprep")

Cheatsheet

Example

This is a basic example which shows you how to load data and run the omiprep quality control pipeline.

Read data into R and create the Omiprep object

library(omiprep)

# import data 
datain <- read_nightingale(system.file("extdata", "nightingale_v2_example.xlsx", package = "omiprep"), 
                         return_Omiprep = FALSE    ## Whether to return a Omiprep object (TRUE) or a list (FALSE)
                         )

# create omiprep object
mydata <-  Omiprep(data     = datain$data, 
                   features = datain$features, 
                   samples  = datain$samples)

Run the quality control pipeline

# run QC
mydata <- mydata |> quality_control()
#> 
#> ── Starting Omics QC Process ───────────────────────────────────────────────────
#> ℹ Validating input parameters
#> ✔ Validating input parameters [4ms]
#> 
#> ℹ Sample & Feature Summary Statistics for raw data
#> ℹ Number of informative PCs (Scree acceleration factor): 2
#> ℹ Sample & Feature Summary Statistics for raw data✔ Sample & Feature Summary Statistics for raw data [794ms]
#> 
#> ℹ Copying input data to new 'qc' data layer
#> ✔ Copying input data to new 'qc' data layer [12ms]
#> 
#> ℹ Assessing for extreme sample missingness >=80% - excluding 0 sample(s)
#> ✔ Assessing for extreme sample missingness >=80% - excluding 0 sample(s) [9ms]
#> 
#> ℹ Assessing for extreme feature missingness >=80% - excluding 0 feature(s)
#> ✔ Assessing for extreme feature missingness >=80% - excluding 0 feature(s) [13m…
#> 
#> ℹ Assessing for sample missingness at specified level of >=20% - excluding 0 sa…
#> ✔ Assessing for sample missingness at specified level of >=20% - excluding 0 sa…
#> 
#> ℹ Assessing for feature missingness at specified level of >=20% - excluding 0 f…
#> ✔ Assessing for feature missingness at specified level of >=20% - excluding 7 f…
#> 
#> ℹ Calculating total sum abundance outliers at +/- 5 Sdev - excluding 0 sample(s)
#> ✔ Calculating total sum abundance outliers at +/- 5 Sdev - excluding 0 sample(s…
#> 
#> ℹ Running sample data PCA outlier analysis at +/- 5 Sdev
#> ✔ Running sample data PCA outlier analysis at +/- 5 Sdev [8ms]
#> 
#> ℹ Sample PCA outlier analysis - re-identify feature independence and PC outlier…
#> ℹ Number of informative PCs (Scree acceleration factor): 2
#> ℹ Sample PCA outlier analysis - re-identify feature independence and PC outlier…ℹ Sample PCA outlier analysis - re-identify feature independence and PC outlier…
#> ! The stated max PCs [max_num_pcs=10] to use in PCA outlier assessment is greater than the number of available informative PCs [2]
#> ℹ Sample PCA outlier analysis - re-identify feature independence and PC outlier…✔ Sample PCA outlier analysis - re-identify feature independence and PC outlier…
#> 
#> ℹ Creating final QC dataset...
#> ℹ Number of informative PCs (Scree acceleration factor): 2
#> ℹ Creating final QC dataset...
#> ℹ Creating final QC dataset...── Step timings ──
#> ℹ Creating final QC dataset...
#> ℹ Creating final QC dataset...
#>                         step seconds   pct
#>                   validation    0.00   0.0
#>                summarise_raw    0.79  32.9
#>                   copy_layer    0.00   0.0
#>   extreme_sample_missingness    0.00   0.0
#>  extreme_feature_missingness    0.01   0.4
#>           sample_missingness    0.00   0.0
#>          total_sum_abundance    0.00   0.0
#>                summarise_pca    0.77  32.1
#>              summarise_final    0.73  30.4
#>                        total    2.40 100.0
#> ✔ Creating final QC dataset... [757ms]
#> 
#> ℹ 'Omics QC Process Completed
#> ✔ 'Omics QC Process Completed [9ms]

View a summary of the Omiprep object

# view summary
summary(mydata)
#> Omiprep Object Summary
#> --------------------------
#> Samples      : 150
#> Features     : 229
#> Data Layers  : 2
#> Layer Names  : input, qc
#> 
#> Sample Summary Layers : input, qc
#> Feature Summary Layers: input, qc
#> 
#> Sample Annotation (metadata):
#>   Columns: 7
#>   Names  : sample_id, high_pyruvate, high_lactate, low_glutamine__high_glutamate, plasma_sample, reason_excluded, excluded
#> 
#> Feature Annotation (metadata):
#>   Columns: 3
#>   Names  : feature_id, reason_excluded, excluded
#> 
#> Exclusion Codes Summary:
#> 
#>   Sample Exclusions:
#> Exclusion | Count
#> -----------------
#> user_excluded                     | 0
#> extreme_sample_missingness        | 0
#> user_defined_sample_missingness   | 0
#> user_defined_sample_totalpeakarea | 0
#> user_defined_sample_pca_outlier   | 0
#> 
#>   Feature Exclusions:
#> Exclusion | Count
#> -----------------
#> user_excluded                    | 0
#> extreme_feature_missingness      | 0
#> user_defined_feature_missingness | 7
#> user_defined_feature_skewness    | 0

Plot a dendrogram of the feature tree

# view feature tree
tree <- attr(mydata@feature_summary, "qc_tree")
indfeatcount = sum( mydata@feature_summary["independent_features", , 2], na.rm = TRUE )
par(mar = c(2,3,5,1) )
plot(tree, hang = -1, cex = 0.5, 
     main = paste0("Example NH Dataset Feature Tree\n# of ind. features = ",indfeatcount ), 
     xlab = "")