Differential analysis of RNA-seq datasets — TRNSCRPT

Timewise summary statistics and training FDR from differential analysis (DA) that tests the effect of training on the expression of each gene within each sex. One data frame per tissue.

Usage

TRNSCRPT_BLOOD_DA

TRNSCRPT_HIPPOC_DA

TRNSCRPT_CORTEX_DA

TRNSCRPT_HYPOTH_DA

TRNSCRPT_SKMGN_DA

TRNSCRPT_SKMVL_DA

TRNSCRPT_HEART_DA

TRNSCRPT_KIDNEY_DA

TRNSCRPT_ADRNL_DA

TRNSCRPT_COLON_DA

TRNSCRPT_SPLEEN_DA

TRNSCRPT_TESTES_DA

TRNSCRPT_OVARY_DA

TRNSCRPT_VENACV_DA

TRNSCRPT_LUNG_DA

TRNSCRPT_SMLINT_DA

TRNSCRPT_LIVER_DA

TRNSCRPT_BAT_DA

TRNSCRPT_WATSC_DA

Format

A data frame with 22 variables:

feature: character, unique feature identifier in the format 'ASSAY_ABBREV;TISSUE_ABBREV;feature_ID' only for training-regulated features at 5% IHW FDR. For redundant differential features, 'feature_ID' is prepended with the specific platform to make unique identifiers. See REPEATED_FEATURES for details.
assay: character, assay abbreviation, one of ASSAY_ABBREV
assay_code: character, assay code used in data release. See MotrpacBicQC::assay_codes.
tissue: character, tissue abbreviation, one of TISSUE_ABBREV
tissue_code: character, tissue code used in data release. See MotrpacBicQC::bic_animal_tissue_code.
feature_ID: character, MoTrPAC feature identifier
sex: character, one of 'male' or 'female'
comparison_group: character, time point of trained animals compared to the sex-matched sedentary control animals, one of '1w', '2w', '4w', '8w'
p_value: double, unadjusted p-value for the difference between 'comparison_group' and the group of sex-matched sedentary control animals
adj_p_value: double, adjusted p-value from 'p_value' column. P-values are BY-adjusted across all datasets within a given assay/ome.
logFC: double, log fold-change where the numerator is 'comparison_group', e.g., '1w', and the denominator is the group of sex-matched sedentary control animals
logFC_se: double, standard error of the log fold-change
shrunk_logFC: double, log fold-change with shrinkage applied
shrunk_logFC_se: double, standard error of the shrunken log fold-change
zscore: double, z statistic
covariates: character, comma-separated list of adjustment variables or NA
removed_samples: character, comma-separated list of vial labels excluded from differential analysis or NA
comparison_average_intensity: double, average intensity among the replicates in 'comparison_group'
comparison_average_intensity_se: double, standard error of 'comparison_average_intensity'
reference_average_intensity: double, average intensity among the replicates in the group of sex-matched sedentary control animals
reference_average_intensity_se: double, standard error of 'reference_average_intensity'
selection_fdr: double, adjusted training p-value used to select training-regulated analytes. P-values are IHW-adjusted across all datasets within a given assay with tissue as a covariate.

An object of class data.frame with 132864 rows and 22 columns.

An object of class data.frame with 131544 rows and 22 columns.

An object of class data.frame with 135640 rows and 22 columns.

An object of class data.frame with 110192 rows and 22 columns.

An object of class data.frame with 111784 rows and 22 columns.

An object of class data.frame with 115560 rows and 22 columns.

An object of class data.frame with 127848 rows and 22 columns.

An object of class data.frame with 126472 rows and 22 columns.

An object of class data.frame with 132448 rows and 22 columns.

An object of class data.frame with 130552 rows and 22 columns.

An object of class data.frame with 69436 rows and 22 columns.

An object of class data.frame with 68140 rows and 22 columns.

An object of class data.frame with 98028 rows and 22 columns.

An object of class data.frame with 132040 rows and 22 columns.

An object of class data.frame with 131288 rows and 22 columns.

An object of class data.frame with 115496 rows and 22 columns.

An object of class data.frame with 129232 rows and 22 columns.

An object of class data.frame with 134112 rows and 22 columns.

Details

Reproduce our analysis with MotrpacRatTraining6mo::transcript_timewise_da() and MotrpacRatTraining6mo::transcript_training_da().