The goal of omiprep is to:
- Read in and processes various ’omics data, saving datasets in tab-delimited format for use elsewhere
- Provide useful summary data in the form of tab-delimited text file and a html report.
- Perform data filtering on the data set using a standard pipeline and according to user-defined thresholds.
You can install the latest version of omiprep from GitHub with:
# install.packages("pak")
pak::pak("MRCIEU/omiprep")
This is a basic example which shows you how to load data and run the
omiprep quality control pipeline.
library(omiprep)
# import data
mydata <- read_metabolon(system.file("extdata", "metabolon_v1.1_example.xlsx", package = "omiprep"),
sheet = "OrigScale", ## The name of the sheet in the excel file to read in
return_Omiprep = FALSE ## Whether to return a Omiprep object (TRUE) or a list (FALSE)
)
# create omiprep object
mydata <- Omiprep(data = mydata$data,
features = mydata$features,
samples = mydata$samples)# run QC
mydata <- mydata |> quality_control( source_layer = "input",
sample_missingness = 0.2,
feature_missingness = 0.2,
feature_skewness_threshold = NULL,
feature_skewness_direction = "left",
total_sum_abundance_sd = 5,
outlier_udist = 5,
outlier_treatment = "leave_be",
winsorize_quantile = 1.0,
tree_cut_height = 0.5,
pc_outlier_sd = 5,
sample_ids = NULL,
feature_ids = NULL)
#>
#> ── Starting Omics QC Process ───────────────────────────────────────────────────
#> ℹ Validating input parameters
#> ℹ Validating input parameters ── Starting 'Omics QC Process ──────────────────────────────────────────────────
#> ℹ Validating input parameters✔ Validating input parameters [9ms]
#> ℹ Validating input parameters✔ Validating input parameters [7ms]
#> ℹ Sample & Feature Summary Statistics for raw data
#> AF = 2
#> ✔ Sample & Feature Summary Statistics for raw data [272ms]
#> ℹ Copying input data to new 'qc' data layer✔ Copying input data to new 'qc' data layer [16ms]
#> ℹ Assessing for extreme sample missingness >=80% - excluding 0 sample(s)✔ Assessing for extreme sample missingness >=80% - excluding 0 sample(s) [10ms]
#> ℹ Assessing for extreme feature missingness >=80% - excluding 0 feature(s)✔ Assessing for extreme feature missingness >=80% - excluding 0 feature(s) [9ms]
#> ℹ Assessing for sample missingness at specified level of >=20% - excluding 0 sa…✔ Assessing for sample missingness at specified level of >=20% - excluding 2 sa…
#> ℹ Assessing for feature missingness at specified level of >=20% - excluding 0 f…✔ Assessing for feature missingness at specified level of >=20% - excluding 0 f…
#> ℹ Calculating total peak abundance outliers at +/- 5 Sdev - excluding 0 sample(…✔ Calculating total peak abundance outliers at +/- 5 Sdev - excluding 0 sample(…
#> 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…
#> AF = 2
#> 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...
#> AF = 2
#>
#> ℹ Creating final QC dataset... ── Step timings ──
#> ℹ Creating final QC dataset...
#> ℹ Creating final QC dataset...
#> step seconds pct
#> validation 0.01 1.2
#> summarise_raw 0.26 31.0
#> copy_layer 0.00 0.0
#> extreme_sample_missingness 0.00 0.0
#> extreme_feature_missingness 0.00 0.0
#> sample_missingness 0.00 0.0
#> total_sum_abundance 0.00 0.0
#> summarise_pca 0.26 31.0
#> summarise_final 0.20 23.9
#> total 0.84 100.2
#> ✔ Creating final QC dataset... [217ms]
#> ℹ 'Omics QC Process Completed✔ 'Omics QC Process Completed [11ms]# view summary
summary(mydata)
#> Omiprep Object Summary
#> --------------------------
#> Samples : 100
#> Features : 100
#> Data Layers : 2
#> Layer Names : input, qc
#>
#> Sample Summary Layers : input, qc
#> Feature Summary Layers: input, qc
#>
#> Sample Annotation (metadata):
#> Columns: 8
#> Names : sample_id, neg, pos, run_day, box_id, lot, reason_excluded, excluded
#>
#> Feature Annotation (metadata):
#> Columns: 9
#> Names : feature_id, metabolite_id, comp_id, platform, pathway, kegg, group_hmdb, reason_excluded, excluded
#>
#> Exclusion Codes Summary:
#>
#> Sample Exclusions:
#> Exclusion | Count
#> -----------------
#> user_excluded | 0
#> extreme_sample_missingness | 0
#> user_defined_sample_missingness | 2
#> 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 | 0
#> user_defined_feature_skewness | 0# view feature tree
tree <- attr(mydata@feature_summary, "qc_tree")
par(mar = c(1,3,5,1) )
plot(tree, hang = -1, cex = 0.75, main = "Example Dataset Feature Tree", sub = "", xlab = "")