Group-specific fixed effects model
cytogroup( df_samples_subset, protein_names, condition, group = "donor", cell_n_min = Inf, cell_n_subsample = 0 )
| df_samples_subset | Data frame or tibble with proteins counts, cell condition, and group information |
|---|---|
| protein_names | A vector of column names of protein to use in the analysis |
| condition | The column name of the condition variable |
| group | The column name of the group variable |
| cell_n_min | Remove samples that are below this cell counts threshold |
| cell_n_subsample | Subsample samples to have this maximum cell count |
A list of class cytoglm containing
speedglm object
possibly subsampled df_samples_subset table
input protein names
input condition variable
input group names
input cell_n_min
input cell_n_subsample
set.seed(23) df <- generate_data() protein_names <- names(df)[3:12] df <- dplyr::mutate_at(df, protein_names, function(x) asinh(x/5)) group_fit <- CytoGLMM::cytogroup(df, protein_names = protein_names, condition = "condition", group = "donor") #> Warning: `as.tibble()` was deprecated in tibble 2.0.0. #> Please use `as_tibble()` instead. #> The signature and semantics have changed, see `?as_tibble`. #> This warning is displayed once every 8 hours. #> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated. group_fit #> $groupfit #> Generalized Linear Model of class 'speedglm': #> #> Call: speedglm(formula = formula_str, data = df_samples_subset, family = binomial()) #> #> Coefficients: #> (Intercept) m01 m02 m03 m04 m05 #> -9.090e-01 8.698e-02 1.545e-01 7.363e-02 -5.642e-03 1.077e-02 #> m06 m07 m08 m09 m10 X1 #> 2.152e-03 4.735e-02 4.610e-02 -1.773e-04 2.919e-02 1.016e+00 #> X2 X3 X4 X5 X6 X7 #> 3.993e-01 6.928e-01 5.071e-01 7.220e-01 1.251e-01 4.852e-01 #> X8 m01:X1 m01:X2 m01:X3 m01:X4 m01:X5 #> NA 1.843e-01 -4.158e-02 -5.075e-03 -1.402e-02 -9.011e-02 #> m01:X6 m01:X7 m01:X8 m02:X1 m02:X2 m02:X3 #> -2.758e-03 -7.174e-02 NA -1.089e-01 -9.177e-02 -1.093e-01 #> m02:X4 m02:X5 m02:X6 m02:X7 m02:X8 m03:X1 #> -1.058e-01 -1.124e-01 -7.804e-02 -4.882e-02 NA 2.120e-02 #> m03:X2 m03:X3 m03:X4 m03:X5 m03:X6 m03:X7 #> 1.178e-01 -1.858e-02 4.345e-03 -4.760e-02 3.601e-02 3.170e-02 #> m03:X8 m04:X1 m04:X2 m04:X3 m04:X4 m04:X5 #> NA -8.086e-02 1.334e-02 3.134e-02 -5.542e-02 2.500e-02 #> m04:X6 m04:X7 m04:X8 m05:X1 m05:X2 m05:X3 #> 3.327e-02 -8.551e-03 NA 1.530e-02 -4.858e-02 -1.451e-02 #> m05:X4 m05:X5 m05:X6 m05:X7 m05:X8 m06:X1 #> 1.958e-03 3.823e-02 -5.835e-02 2.097e-03 NA -8.318e-02 #> m06:X2 m06:X3 m06:X4 m06:X5 m06:X6 m06:X7 #> 1.141e-01 -4.265e-02 -3.411e-02 -1.946e-02 -5.368e-02 -6.319e-02 #> m06:X8 m07:X1 m07:X2 m07:X3 m07:X4 m07:X5 #> NA -1.102e-01 -1.820e-02 -8.430e-02 -3.744e-02 -1.059e-01 #> m07:X6 m07:X7 m07:X8 m08:X1 m08:X2 m08:X3 #> -3.105e-03 -1.263e-02 NA -9.409e-02 -9.344e-02 -3.130e-02 #> m08:X4 m08:X5 m08:X6 m08:X7 m08:X8 m09:X1 #> 3.927e-03 -4.415e-03 -5.756e-02 -6.987e-05 NA 6.399e-02 #> m09:X2 m09:X3 m09:X4 m09:X5 m09:X6 m09:X7 #> -5.561e-02 -3.413e-02 -4.475e-03 -7.922e-03 -1.096e-02 -1.039e-02 #> m09:X8 m10:X1 m10:X2 m10:X3 m10:X4 m10:X5 #> NA -6.815e-02 2.977e-02 -4.956e-02 -3.438e-02 -1.950e-02 #> m10:X6 m10:X7 m10:X8 #> 3.076e-02 -1.028e-01 NA #> #> #> $df_samples_subset #> # A tibble: 16,000 × 20 #> donor condition m01 m02 m03 m04 m05 m06 m07 m08 m09 m10 #> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 1 treatment 0.390 3.93 3.84 1.94 1.94 1.94 3.11 3.18 1.14 3.44 #> 2 1 treatment 0 3.65 3.07 2.46 4.21 2.56 2.75 1.53 2.05 3.09 #> 3 1 treatment 0.199 1.75 3.04 4.29 2.35 1.61 1.14 1.02 2.70 4.70 #> 4 1 treatment 1.02 2.09 2.62 2.14 2.82 0.569 2.49 2.05 1.14 3.30 #> 5 1 treatment 1.99 2.49 1.82 4.02 2.19 0.733 1.44 0.881 2.56 2.05 #> 6 1 treatment 0.199 1.94 2.73 4.10 1.75 0.881 2.35 2.46 2.35 3.50 #> 7 1 treatment 2.27 4.27 1.44 1.25 5.29 2.14 2.35 2.59 1.25 2.35 #> 8 1 treatment 0.199 3.50 1.88 3.16 3.53 0.390 1.53 2.42 1.53 2.42 #> 9 1 treatment 0.390 2.27 2.96 1.68 2.39 1.94 3.02 4.35 2.42 4.50 #> 10 1 treatment 0.390 2.75 1.68 0.733 2.67 2.42 2.75 3.73 0.199 3.18 #> # … with 15,990 more rows, and 8 more variables: X1 <dbl>, X2 <dbl>, X3 <dbl>, #> # X4 <dbl>, X5 <dbl>, X6 <dbl>, X7 <dbl>, X8 <dbl> #> #> $protein_names #> [1] "m01" "m02" "m03" "m04" "m05" "m06" "m07" "m08" "m09" "m10" #> #> $condition #> [1] "condition" #> #> $group #> [1] "donor" #> #> $cell_n_min #> [1] Inf #> #> $cell_n_subsample #> [1] 0 #> #> attr(,"class") #> [1] "cytogroup"