R/methods.R, R/methods_SE.R
test_differential_cellularity-methods.Rdtest_differential_cellularity() takes as input A `tbl` (with at least three columns for sample, feature and transcript abundance) or `SummarizedExperiment` (more convenient if abstracted to tibble with library(tidySummarizedExperiment)) and returns a consistent object (to the input) with additional columns for the statistics from the hypothesis test.
test_differential_cellularity(
.data,
.formula,
.sample = NULL,
.transcript = NULL,
.abundance = NULL,
method = "cibersort",
reference = X_cibersort,
significance_threshold = 0.05,
...
)
# S4 method for class 'spec_tbl_df'
test_differential_cellularity(
.data,
.formula,
.sample = NULL,
.transcript = NULL,
.abundance = NULL,
method = "cibersort",
reference = X_cibersort,
significance_threshold = 0.05,
...
)
# S4 method for class 'tbl_df'
test_differential_cellularity(
.data,
.formula,
.sample = NULL,
.transcript = NULL,
.abundance = NULL,
method = "cibersort",
reference = X_cibersort,
significance_threshold = 0.05,
...
)
# S4 method for class 'tidybulk'
test_differential_cellularity(
.data,
.formula,
.sample = NULL,
.transcript = NULL,
.abundance = NULL,
method = "cibersort",
reference = X_cibersort,
significance_threshold = 0.05,
...
)
# S4 method for class 'SummarizedExperiment'
test_differential_cellularity(
.data,
.formula,
.sample = NULL,
.transcript = NULL,
.abundance = NULL,
method = "cibersort",
reference = X_cibersort,
significance_threshold = 0.05,
...
)
# S4 method for class 'RangedSummarizedExperiment'
test_differential_cellularity(
.data,
.formula,
.sample = NULL,
.transcript = NULL,
.abundance = NULL,
method = "cibersort",
reference = X_cibersort,
significance_threshold = 0.05,
...
)A `tbl` (with at least three columns for sample, feature and transcript abundance) or `SummarizedExperiment` (more convenient if abstracted to tibble with library(tidySummarizedExperiment))
A formula representing the desired linear model. The formula can be of two forms: multivariable (recommended) or univariable Respectively: \"factor_of_interest ~ .\" or \". ~ factor_of_interest\". The dot represents cell-type proportions, and it is mandatory. If censored regression is desired (coxph) the formula should be of the form \"survival::Surv\(y, dead\) ~ .\"
The name of the sample column
The name of the transcript/gene column
The name of the transcript/gene abundance column
A string character. Either \"cibersort\", \"epic\" or \"llsr\". The regression method will be chosen based on being multivariable: lm or cox-regression (both on logit-transformed proportions); or univariable: beta or cox-regression (on logit-transformed proportions). See .formula for multi- or univariable choice.
A data frame. The transcript/cell_type data frame of integer transcript abundance
A real between 0 and 1 (usually 0.05).
Further parameters passed to the method deconvolve_cellularity
A consistent object (to the input) with additional columns for the statistics from the hypothesis test (e.g., log fold change, p-value and false discovery rate).
A `SummarizedExperiment` object
A `SummarizedExperiment` object
`r lifecycle::badge("maturing")`
This routine applies a deconvolution method (e.g., Cibersort; DOI: 10.1038/nmeth.3337) and passes the proportions inferred into a generalised linear model (DOI:dx.doi.org/10.1007/s11749-010-0189-z) or a cox regression model (ISBN: 978-1-4757-3294-8)
Underlying method for the generalised linear model: data |> deconvolve_cellularity( !!.sample, !!.transcript, !!.abundance, method=method, reference = reference, action="get", ... ) [..] betareg::betareg(.my_formula, .)
Underlying method for the cox regression: data |> deconvolve_cellularity( !!.sample, !!.transcript, !!.abundance, method=method, reference = reference, action="get", ... ) [..] mutate(.proportion_0_corrected = .proportion_0_corrected |> boot::logit()) survival::coxph(.my_formula, .)
# Regular regression
test_differential_cellularity(
tidybulk::se_mini ,
. ~ condition,
cores = 1
)
#> # A tibble: 22 × 7
#> .cell_type cell_type_proportions `estimate_(Intercept)`
#> <chr> <list> <dbl>
#> 1 cibersort.B.cells.naive <tibble [5 × 8]> -2.37
#> 2 cibersort.B.cells.memory <tibble [5 × 8]> -2.75
#> 3 cibersort.Plasma.cells <tibble [5 × 8]> NA
#> 4 cibersort.T.cells.CD8 <tibble [5 × 8]> NA
#> 5 cibersort.T.cells.CD4.naive <tibble [5 × 8]> NA
#> 6 cibersort.T.cells.CD4.memory.re… <tibble [5 × 8]> -3.81
#> 7 cibersort.T.cells.CD4.memory.ac… <tibble [5 × 8]> NA
#> 8 cibersort.T.cells.follicular.he… <tibble [5 × 8]> -6.01
#> 9 cibersort.T.cells.regulatory..T… <tibble [5 × 8]> NA
#> 10 cibersort.T.cells.gamma.delta <tibble [5 × 8]> NA
#> # ℹ 12 more rows
#> # ℹ 4 more variables: estimate_conditionTRUE <dbl>,
#> # std.error_conditionTRUE <dbl>, statistic_conditionTRUE <dbl>,
#> # p.value_conditionTRUE <dbl>
# Cox regression - multiple
tidybulk::se_mini |>
# Test
test_differential_cellularity(
survival::Surv(days, dead) ~ .,
cores = 1
)
#> Warning: Ran out of iterations and did not converge
#> # A tibble: 22 × 6
#> .cell_type cell_type_proportions estimate std.error statistic p.value
#> <chr> <list> <dbl> <dbl> <dbl> <dbl>
#> 1 cibersort.B.cells… <tibble [5 × 7]> -0.616 3148. -1.96e-4 1.00
#> 2 cibersort.B.cells… <tibble [5 × 7]> -100. 2796. -3.58e-2 0.971
#> 3 cibersort.Plasma.… <tibble [5 × 7]> 0 0 NaN NaN
#> 4 cibersort.T.cells… <tibble [5 × 7]> 118. 4318. 2.74e-2 0.978
#> 5 cibersort.T.cells… <tibble [5 × 7]> -164. 3555. -4.61e-2 0.963
#> 6 cibersort.T.cells… <tibble [5 × 7]> -1.93 2430. -7.96e-4 0.999
#> 7 cibersort.T.cells… <tibble [5 × 7]> 0.935 13450. 6.95e-5 1.00
#> 8 cibersort.T.cells… <tibble [5 × 7]> 13.3 5004. 2.67e-3 0.998
#> 9 cibersort.T.cells… <tibble [5 × 7]> -302. 49862. -6.07e-3 0.995
#> 10 cibersort.T.cells… <tibble [5 × 7]> 0 0 NaN NaN
#> # ℹ 12 more rows