The `MidarExperiment` data object
Source:vignettes/articles/03_midarexperiment.Rmd
03_midarexperiment.RmdOverview
The MidarExperiment object is the main data
container used in the MiDAR workflow, see also Data and Metadata in MiDAR. It holds
all the experimental and processed data and metadata, as well as other
details. MidarExperiment is an S4 object with following
slots:
MidarExperiment
├─title: chr "My LCMS Assay"
├─analysis_type: chr NA
├─feature_intensity_var: chr "feature_area"
├─dataset_orig: tibble [400 × 26] (S3: tbl_df/tbl/data.frame)
├─dataset: tibble [400 × 26] (S3: tbl_df/tbl/data.frame)
├─dataset_filtered: tibble [0 × 14] (S3: tbl_df/tbl/data.frame)
├─annot_analyses: tibble [25 × 13] (S3: tbl_df/tbl/data.frame)
├─annot_features: tibble [16 × 16] (S3: tbl_df/tbl/data.frame)
├─annot_istds: tibble [8 × 4] (S3: tbl_df/tbl/data.frame)
├─annot_responsecurves: tibble [0 × 3] (S3: tbl_df/tbl/data.frame)
├─annot_qcconcentrations: tibble [32 × 5] (S3: tbl_df/tbl/data.frame)
├─annot_studysamples: tibble [0 × 0] (S3: tbl_df/tbl/data.frame)
├─annot_batches: tibble [1 × 4] (S3: tbl_df/tbl/data.frame)
├─metrics_qc: tibble [0 × 0] (S3: tbl_df/tbl/data.frame)
├─metrics_calibration: tibble [4 × 15] (S3: tbl_df/tbl/data.frame)
├─parameters_processing: tibble [0 × 1] (S3: tbl_df/tbl/data.frame)
├─status_processing: chr "Calibration-quantitated data"
├─is_istd_normalized: logi TRUE
├─is_quantitated: logi TRUE
├─is_filtered: logi FALSE
├─has_outliers_tech: logi FALSE\
├─is_isotope_corr: logi FALSE
├─analyses_excluded: logi NA
├─features_excluded: logi NA
├─var_drift_corrected: Named logi [1:3] FALSE FALSE FALSE
├─var_batch_corrected: Named logi [1:3] FALSE FALSE FALSECreating a MidarExperiment object
library(midar)
myexp <- MidarExperiment()Using MidarExperiment objects
Most MiDAR functions take an MidarExperiment object as
input. Data processing functions return a modified
MidarExperiment, which can be in subsequent step.
myexp <- MidarExperiment()
myexp <- data_load_example(myexp, 1)
myexp <- normalize_by_istd(myexp)
#> ! Interfering features defined in metadata, but no correction was applied. Use `correct_interferences()` to correct.
#> ✔ 20 features normalized with 9 ISTDs in 499 analyses.
save_dataset_csv(myexp, "mydata.csv", "norm_intensity", FALSE)
#> ✔ Norm_intensity values for 499 analyses and 20 features have been exported to 'mydata.csv'.R pipes can also be used, allowing to chain multiple functions together. This can more clearly indicate the processing workflow and make the code more easier to read.
myexp |>
MidarExperiment() |>
data_load_example(1) |>
normalize_by_istd() |>
save_dataset_csv("mydata.csv", "norm_intensity", FALSE)
#> ! Interfering features defined in metadata, but no correction was applied. Use `correct_interferences()` to correct.
#> ✔ 20 features normalized with 9 ISTDs in 499 analyses.
#> ✔ Norm_intensity values for 499 analyses and 20 features have been exported to 'mydata.csv'.Multiple MidarExperiment objects
Multiple MidarExperiment objects can be created and
processed independently within the same script.
m_polars <- MidarExperiment(title = "Polar metabolites")
m_lipids <- MidarExperiment(title = "Non-polar metabolites")Accessing data and metadata
Functions starting with data_get_ allow to retrieve data
and metadat from MidarExperiment object.
myexp <- data_load_example(myexp, 1)
dataset <- get_analyticaldata(myexp, annotated = TRUE)
print(dataset)
#> # A tibble: 14,471 × 19
#> analysis_order analysis_id acquisition_time_stamp qc_type batch_id sample_id
#> <int> <chr> <dttm> <chr> <chr> <chr>
#> 1 1 Longit_BLAN… 2017-10-20 14:15:36 SBLK 1 Longit_B…
#> 2 1 Longit_BLAN… 2017-10-20 14:15:36 SBLK 1 Longit_B…
#> 3 1 Longit_BLAN… 2017-10-20 14:15:36 SBLK 1 Longit_B…
#> 4 1 Longit_BLAN… 2017-10-20 14:15:36 SBLK 1 Longit_B…
#> 5 1 Longit_BLAN… 2017-10-20 14:15:36 SBLK 1 Longit_B…
#> 6 1 Longit_BLAN… 2017-10-20 14:15:36 SBLK 1 Longit_B…
#> 7 1 Longit_BLAN… 2017-10-20 14:15:36 SBLK 1 Longit_B…
#> 8 1 Longit_BLAN… 2017-10-20 14:15:36 SBLK 1 Longit_B…
#> 9 1 Longit_BLAN… 2017-10-20 14:15:36 SBLK 1 Longit_B…
#> 10 1 Longit_BLAN… 2017-10-20 14:15:36 SBLK 1 Longit_B…
#> # ℹ 14,461 more rows
#> # ℹ 13 more variables: replicate_no <int>, specimen <chr>, feature_id <chr>,
#> # feature_class <chr>, feature_label <chr>, is_istd <lgl>,
#> # is_quantifier <lgl>, analyte_id <chr>, feature_rt <dbl>,
#> # feature_area <dbl>, feature_height <dbl>, feature_fwhm <dbl>,
#> # feature_intensity <dbl>Alternatively, the $ syntax can be used access data and
metadata tables in MidarExperiment objects.
analyses <- myexp$annot_analyses
features <- myexp$annot_features
print(features)
#> # A tibble: 29 × 16
#> feature_id feature_class analyte_id is_istd istd_feature_id
#> <chr> <chr> <chr> <lgl> <chr>
#> 1 CE 18:1 CE NA FALSE CE 18:1 d7 (ISTD)
#> 2 CE 18:1 d7 (ISTD) CE NA TRUE CE 18:1 d7 (ISTD)
#> 3 Cer d18:1/16:0 Cer 18:1;O2 NA FALSE Cer d18:1/25:0 (IST…
#> 4 Cer d18:1/24:0 Cer 18:1;O2 NA FALSE Cer d18:1/25:0 (IST…
#> 5 Cer d18:1/25:0 (ISTD) Cer 18:1;O2 NA TRUE Cer d18:1/25:0 (IST…
#> 6 LPC 18:1 (a) LPC NA FALSE LPC 18:1 (ab) d7 (I…
#> 7 LPC 18:1 (ab) d7 (ISTD) LPC NA TRUE LPC 18:1 (ab) d7 (I…
#> 8 LPC 18:1 (b) LPC NA FALSE LPC 18:1 (ab) d7 (I…
#> 9 PC 28:0|SM 32:1 M+3 PC NA FALSE PC 33:1 d7 (ISTD)
#> 10 PC 32:1 PC NA FALSE PC 33:1 d7 (ISTD)
#> # ℹ 19 more rows
#> # ℹ 11 more variables: quant_istd_feature_id <chr>, is_quantifier <lgl>,
#> # valid_feature <lgl>, response_factor <dbl>, interference_feature_id <chr>,
#> # interference_contribution <dbl>, curve_fit_model <chr>,
#> # fit_weighting <chr>, remarks <chr>, feature_label <chr>,
#> # curve_fit_weighting <chr>Saving and Reading MidarExperiment objects
myexp <- MidarExperiment()
myexp <- data_load_example(myexp, 1)
saveRDS(myexp, file = "myexp-midar.rds", compress = TRUE)
my_saved_exp <- readRDS(file = "myexp-midar.rds")Status of an MidarExperiment
A detailed summary of the dataset and processing status can be printed any time
print(my_saved_exp)