Load Differential Analysis Results — load_differential_analysis • MotrpacHumanPreSuspensionAnalysis

Load Differential Analysis Results

Usage

load_differential_analysis(
  selected_omes = "all",
  selected_tissues = "all",
  single_matrix = FALSE,
  epigen = FALSE,
  combine_with_featgene = FALSE,
  verbose = TRUE
)

Arguments

selected_omes: character; one of ome_available_list.
selected_tissues: character; one of tissue_available_list.
single_matrix: logical; if TRUE, returns a single data.frame containing all results. Otherwise, returns a list of data.frame objects (default).
epigen: logical; a toggle of TRUE/FALSE if epigenetics data is desired. Loading epigenetic data files is through AWS and is very slow due to file sizes.
combine_with_featgene: logical; whether to include columns from HUMAN_FEATURE_TO_GENE in the output.
verbose: logical; whether or not to display messages for some warnings.

Value

A nested list of data.table objects. The top level names are the tissues, while the second level names are the omes. Each table may possess the following columns:

tissue: factor; the tissue.
assay: factor; the ome.
platform: factor; (metabolomics only) metabolomics platform.
full_model: factor; full model containing predictors and any covariates.
contrast: factor; full contrast (up to 33).
contrast_short: factor; shortened version of the contrasts.
contrast_type: factor; one of "exercise_with_controls", "exercise_no_controls", "Endur_vs_Resist", "baseline", or "control_only".
contrast_category: factor; one of "EE-CON", "RE-CON", "EE-EE", "RE-RE", "EE-RE", or "CON-CON".
feature_id: factor; feature identifier (may be proteins, phosphosites, transcripts, metabolites/lipids, peaks, or GpGs).
logFC: numeric; difference between the group means in the contrast.
CI.L: numeric; lower confidence limit.
CI.R: numeric; upper confidence limit.
degrees_of_freedom: numeric; degrees of freedom.
logLik: numeric; log likelihood of differential expression.
AveExpr: numeric; mean of all sample-level values for that feature.
methylation_diff: numeric; methylation difference.
t: numeric; moderated t-statistic.
z.std: numeric; standard normal equivalent of the t-statistic (z-scores).
p_value: numeric; p-value.
adj_p_value: numeric; p-values adjusted within each combination of tissue, assay, platform, and contrast using the Benjamini-Hochberg method to control the false discovery rate.

Author

Tyler Sagendorf Christopher Jin

Examples

DA_list <- load_differential_analysis() # default behavior
#> Please remember that the lowest CV Metabolite is chosen and the
#>             relevant refmet name is used. If you're not able to find your desired
#>             metabolite, look through the METABOLOMICS_CV object for the relevant
#>             refmet/feature name.

# Structure of a single object
str(DA_list[["adipose"]][["prot-pr"]])
#> Classes ‘data.table’ and 'data.frame':	63504 obs. of  20 variables:
#>  $ tissue            : chr  "adipose" "adipose" "adipose" "adipose" ...
#>  $ assay             : chr  "prot-pr" "prot-pr" "prot-pr" "prot-pr" ...
#>  $ full_model        : Factor w/ 1 level "~ 0 + group_timepoint +  BMI + calculatedAge + Sex + (1 | pid)": 1 1 1 1 1 1 1 1 1 1 ...
#>  $ contrast          : Factor w/ 9 levels "group_timepointADUEndur.post_3.5_4_hr - group_timepointADUEndur.pre_exercise - group_timepointADUControl.post_3"| __truncated__,..: 1 1 1 1 1 1 1 1 1 1 ...
#>  $ contrast_short    : Factor w/ 33 levels "Endur.during_20_min - Control.during_20_min (delta-delta)",..: 5 5 5 5 5 5 5 5 5 5 ...
#>  $ contrast_type     : Factor w/ 5 levels "exercise_with_controls",..: 1 1 1 1 1 1 1 1 1 1 ...
#>  $ contrast_category : Factor w/ 6 levels "EE-CON","RE-CON",..: 1 1 1 1 1 1 1 1 1 1 ...
#>  $ randomGroupCode   : chr  "ADUEndur" "ADUEndur" "ADUEndur" "ADUEndur" ...
#>  $ Timepoint         : Factor w/ 7 levels "pre_exercise",..: 6 6 6 6 6 6 6 6 6 6 ...
#>  $ feature_id        : chr  "O00287" "Q8TE02" "Q8NHG8" "P10912" ...
#>  $ logFC             : num  0.74 0.578 0.609 -0.857 -0.71 ...
#>  $ CI.L              : num  0.533 0.414 0.463 -1.087 -0.933 ...
#>  $ CI.R              : num  0.946 0.742 0.756 -0.627 -0.487 ...
#>  $ degrees_of_freedom: num  30.3 19.5 21.2 19.4 12.7 ...
#>  $ logLik            : num  -15.5 -20.1 -40.5 -32.2 -32.7 ...
#>  $ t                 : num  7.31 7.21 8.75 -7.73 -6.52 ...
#>  $ AveExpr           : num  0.0188 -1.1554 -0.2189 -0.205 -0.5905 ...
#>  $ z.std             : num  5.53 5.38 5.35 -5.3 -5.04 ...
#>  $ p_value           : num  3.22e-08 7.53e-08 8.96e-08 1.14e-07 4.62e-07 ...
#>  $ adj_p_value       : num  0.000202 0.000202 0.000202 0.000202 0.000651 ...
#>  - attr(*, ".internal.selfref")=<externalptr> 
#>  - attr(*, "sorted")= chr [1:4] "full_model" "contrast" "p_value" "feature_id"

# Un-nest list
DA_list <- unlist(DA_list, recursive = FALSE)
names(DA_list)
#>  [1] "adipose.metab"              "adipose.prot-ph"           
#>  [3] "adipose.prot-pr"            "adipose.transcript-rna-seq"
#>  [5] "blood.metab"                "blood.prot-ol"             
#>  [7] "blood.transcript-rna-seq"   "muscle.metab"              
#>  [9] "muscle.prot-ph"             "muscle.prot-pr"            
#> [11] "muscle.transcript-rna-seq" 

# Include epigen data
if (FALSE) { # \dontrun{
repo_local_dir <- "path/to/some/directory"
DA_list <- load_differential_analysis(
  repo_local_dir = repo_local_dir,
  epigen = TRUE
)
} # }