hd_de_ttest() performs differential expression analysis using t-test.
The output tibble includes the logFC, p-values, as well as the FDR adjusted p-values.
The function removes the rows with NAs in the variables that are used to correct for as
well as the variable of interest.
Usage
hd_de_ttest(
dat,
metadata = NULL,
variable = "Disease",
case,
control = NULL,
log_transform = FALSE
)Arguments
- dat
An HDAnalyzeR object or a dataset in wide format and sample ID as its first column.
- metadata
A dataset containing the metadata information with the sample ID as the first column. If a HDAnalyzeR object is provided, this parameter is not needed.
- variable
The name of the metadata variable containing the case and control groups.
- case
The case group.
- control
The control groups. If NULL, it will be set to all other unique values of the variable that are not the case.
- log_transform
If the data should be log transformed. Default is FALSE.
Details
The variable of interest should be categorical and present in the metadata.
In case your data are not already log transformed, you can set log_transform = TRUE to log
transform the data with base 2 before the analysis start.
When using this function you cannot correct for other variables or run it against a continuous variable.
If you need to correct for other variables or run the analysis against a continuous variable, use hd_de_limma().
Examples
# Initialize an HDAnalyzeR object
hd_object <- hd_initialize(example_data, example_metadata)
# Run differential expression analysis for AML vs all others
hd_de_ttest(hd_object, case = "AML")
#> $de_res
#> # A tibble: 100 × 8
#> Feature logFC CI.L CI.R t P.Value adj.P.Val Disease
#> <chr> <dbl> <chr> <chr> <chr> <dbl> <dbl> <chr>
#> 1 ANGPT2 0.795 0.55 1.04 6.58 0.0000000147 0.00000147 AML
#> 2 ADA 1.46 1 1.91 6.42 0.0000000409 0.00000205 AML
#> 3 ANGPT1 -1.70 -2.28 -1.12 -5.87 0.000000312 0.0000104 AML
#> 4 APEX1 1.30 0.79 1.82 5.1 0.00000455 0.000114 AML
#> 5 ADGRG1 1.27 0.76 1.77 5 0.00000736 0.000147 AML
#> 6 APP -0.764 -1.09 -0.44 -4.69 0.0000178 0.000255 AML
#> 7 AZU1 1.64 0.95 2.32 4.78 0.0000154 0.000255 AML
#> 8 ALPP -1.15 -1.68 -0.61 -4.31 0.0000688 0.000777 AML
#> 9 ARTN 1.09 0.58 1.59 4.34 0.0000699 0.000777 AML
#> 10 APBB1IP 1.16 0.61 1.72 4.24 0.000102 0.00102 AML
#> # ℹ 90 more rows
#>
#> attr(,"class")
#> [1] "hd_de"
# Run differential expression analysis for AML vs CLL
hd_de_ttest(hd_object, case = "AML", control = "CLL")
#> $de_res
#> # A tibble: 100 × 8
#> Feature logFC CI.L CI.R t P.Value adj.P.Val Disease
#> <chr> <dbl> <chr> <chr> <chr> <dbl> <dbl> <chr>
#> 1 ADA 1.41 0.91 1.91 5.57 0.000000386 0.0000193 AML
#> 2 ADAM8 -1.36 -1.86 -0.87 -5.48 0.000000363 0.0000193 AML
#> 3 AZU1 1.93 1.2 2.66 5.3 0.00000154 0.0000513 AML
#> 4 ANGPT1 -1.74 -2.47 -1.01 -4.76 0.00000754 0.000162 AML
#> 5 ARID4B -1.53 -2.16 -0.89 -4.79 0.00000809 0.000162 AML
#> 6 ACAN -0.679 -0.97 -0.39 -4.64 0.0000116 0.000193 AML
#> 7 ARTN 1.32 0.76 1.89 4.67 0.0000135 0.000193 AML
#> 8 ADGRG2 -0.641 -0.93 -0.35 -4.35 0.0000353 0.000441 AML
#> 9 ACP6 -0.814 -1.19 -0.44 -4.31 0.0000411 0.000457 AML
#> 10 ADAMTS8 -0.758 -1.12 -0.4 -4.18 0.0000687 0.000687 AML
#> # ℹ 90 more rows
#>
#> attr(,"class")
#> [1] "hd_de"