Merge pull request #141 from MangiolaLaboratory/pass-foldchange

update README and vignette

NAMESPACE

@@ -15,8 +15,8 @@ S3method(sccomp_remove_unwanted_variation,sccomp_tbl)
 S3method(sccomp_replicate,sccomp_tbl)
 S3method(sccomp_test,sccomp_tbl)
 S3method(simulate_data,tbl)
-export(plot_1d_intervals)
-export(plot_2d_intervals)
+export(plot_1D_intervals)
+export(plot_2D_intervals)
 export(plot_summary)
 export(sccomp_boxplot)
 export(sccomp_estimate)

R/methods.R

@@ -1589,10 +1589,10 @@ else {
 }
 
 # 1D intervals
-plots$credible_intervals_1D = plot_1d_intervals(.data = x, .cell_group = !!.cell_group, significance_threshold = significance_threshold)
+plots$credible_intervals_1D = plot_1D_intervals(.data = x, significance_threshold = significance_threshold)
 
 # 2D intervals
-if("v_effect" %in% colnames(x) && (x |> filter(!is.na(v_effect)) |> nrow()) > 0)  plots$credible_intervals_2D = plot_2d_intervals(.data = x, .cell_group = !!.cell_group, significance_threshold = significance_threshold)
+if("v_effect" %in% colnames(x) && (x |> filter(!is.na(v_effect)) |> nrow()) > 0)  plots$credible_intervals_2D = plot_2D_intervals(.data = x, significance_threshold = significance_threshold)
 
 plots
 

R/utilities.R

@@ -1706,7 +1706,6 @@ get_FDR = function(x){
 #' This function creates a series of 1D interval plots for cell-group effects, highlighting significant differences based on a given significance threshold.
 #'
 #' @param .data Data frame containing the main data.
-#' @param .cell_group The cell group to be analysed.
 #' @param significance_threshold Numeric value specifying the significance threshold for highlighting differences. Default is 0.025.
 #' @param test_composition_above_logit_fold_change A positive integer. It is the effect threshold used for the hypothesis test. A value of 0.2 correspond to a change in cell proportion of 10% for a cell type with baseline proportion of 50%. That is, a cell type goes from 45% to 50%. When the baseline proportion is closer to 0 or 1 this effect thrshold has consistent value in the logit uncontrained scale.
 #' @importFrom patchwork wrap_plots
@@ -1718,15 +1717,15 @@ get_FDR = function(x){
 #' @return A combined plot of 1D interval plots.
 #' @examples
 #' # Example usage:
-#' # plot_1d_intervals(.data, "cell_group", 0.025, theme_minimal())
-plot_1d_intervals = function(.data, .cell_group, significance_threshold = 0.05, test_composition_above_logit_fold_change = .data |> attr("test_composition_above_logit_fold_change")){
+#' # plot_1D_intervals(.data, "cell_group", 0.025, theme_minimal())
+plot_1D_intervals = function(.data, significance_threshold = 0.05, test_composition_above_logit_fold_change = .data |> attr("test_composition_above_logit_fold_change")){
 
   # Define the variables as NULL to avoid CRAN NOTES
   parameter <- NULL
   estimate <- NULL
   value <- NULL
 
-  .cell_group = enquo(.cell_group)
+  .cell_group = attr(.data, ".cell_group")
 
   # Check if test have been done
   if(.data |> select(ends_with("FDR")) |> ncol() |> equals(0))
@@ -1782,7 +1781,6 @@ plot_1d_intervals = function(.data, .cell_group, significance_threshold = 0.05,
 #' This function creates a 2D interval plot for mean-variance association, highlighting significant differences based on a given significance threshold.
 #'
 #' @param .data Data frame containing the main data.
-#' @param .cell_group The cell group to be analysed.
 #' @param significance_threshold Numeric value specifying the significance threshold for highlighting differences. Default is 0.025.
 #' @param test_composition_above_logit_fold_change A positive integer. It is the effect threshold used for the hypothesis test. A value of 0.2 correspond to a change in cell proportion of 10% for a cell type with baseline proportion of 50%. That is, a cell type goes from 45% to 50%. When the baseline proportion is closer to 0 or 1 this effect thrshold has consistent value in the logit uncontrained scale.
 #' 
@@ -1799,8 +1797,8 @@ plot_1d_intervals = function(.data, .cell_group, significance_threshold = 0.05,
 #' @return A ggplot object representing the 2D interval plot.
 #' @examples
 #' # Example usage:
-#' # plot_2d_intervals(.data, "cell_group", theme_minimal(), 0.025)
-plot_2d_intervals = function(.data, .cell_group, significance_threshold = 0.05, test_composition_above_logit_fold_change = .data |> attr("test_composition_above_logit_fold_change")){
+#' # plot_2D_intervals(.data, "cell_group", theme_minimal(), 0.025)
+plot_2D_intervals = function(.data, significance_threshold = 0.05, test_composition_above_logit_fold_change = .data |> attr("test_composition_above_logit_fold_change")){
 
   # Define the variables as NULL to avoid CRAN NOTES
   v_effect <- NULL
@@ -1816,7 +1814,7 @@ plot_2d_intervals = function(.data, .cell_group, significance_threshold = 0.05,
   multipanel_theme <- NULL
 
 
-  .cell_group = enquo(.cell_group)
+  .cell_group = attr(.data, ".cell_group")
 
   # Check if test have been done
   if(.data |> select(ends_with("FDR")) |> ncol() |> equals(0))
@@ -1875,11 +1873,7 @@ plot_2d_intervals = function(.data, .cell_group, significance_threshold = 0.05,
         facet_wrap(~parameter, scales = "free") +
 
         # Apply custom theme
-        multipanel_theme +
-        theme(
-          panel.grid.major = element_blank(),
-          panel.grid.minor = element_blank()
-        )
+        multipanel_theme 
     }
 }
 

README.md

@@ -112,13 +112,6 @@ sccomp_result =
 
 ## Summary plots
 
-``` r
-plots = sccomp_result |> plot() 
-```
-
-    ## Joining with `by = join_by(cell_group, sample)`
-    ## Joining with `by = join_by(cell_group, type)`
-
 A plot of group proportion, faceted by groups. The blue boxplots
 represent the posterior predictive check. If the model is likely to be
 descriptively adequate to the data, the blue box plot should roughly
@@ -129,12 +122,14 @@ represents the significant associations for composition and/or
 variability.
 
 ``` r
-plots$boxplot
+sccomp_result |> 
+  sccomp_boxplot(factor = "type")
 ```
 
-    ## [[1]]
+    ## Joining with `by = join_by(cell_group, sample)`
+    ## Joining with `by = join_by(cell_group, type)`
 
-![](inst/figures/unnamed-chunk-11-1.png)<!-- -->
+![](inst/figures/unnamed-chunk-10-1.png)<!-- -->
 
 A plot of estimates of differential composition (c\_) on the x-axis and
 differential variability (v\_) on the y-axis. The error bars represent
@@ -144,10 +139,11 @@ significant if bigger than the minimal effect according to the 95%
 credible interval. Facets represent the covariates in the model.
 
 ``` r
-plots$credible_intervals_1D
+sccomp_result |> 
+  plot_1D_intervals()
 ```
 
-![](inst/figures/unnamed-chunk-12-1.png)<!-- -->
+![](inst/figures/unnamed-chunk-11-1.png)<!-- -->
 
 We can plot the relationship between abundance and variability. As we
 can see below, they are positively correlated, you also appreciate that
@@ -158,10 +154,17 @@ estimates of both the abundance and the variability. This shrinkage is
 adaptive as it is modelled jointly, thanks for Bayesian inference.
 
 ``` r
-plots$credible_intervals_2D
+sccomp_result |> 
+  plot_2D_intervals()
 ```
 
-![](inst/figures/unnamed-chunk-13-1.png)<!-- -->
+![](inst/figures/unnamed-chunk-12-1.png)<!-- -->
+
+You can produce the series of plots calling the `plot` method.
+
+``` r
+sccomp_result |> plot() 
+```
 
 ## Contrasts
 
@@ -180,16 +183,16 @@ seurat_obj |>
     ## # A tibble: 60 × 18
     ##    cell_group  parameter factor c_lower c_effect c_upper   c_pH0   c_FDR c_n_eff
     ##    <chr>       <chr>     <chr>    <dbl>    <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
-    ##  1 B immature  typecanc… <NA>    -1.79    -1.24   -0.676 0       0            NA
-    ##  2 B immature  typeheal… <NA>     0.676    1.24    1.79  0       0            NA
-    ##  3 B mem       typecanc… <NA>    -2.44    -1.78   -1.16  0       0            NA
-    ##  4 B mem       typeheal… <NA>     1.16     1.78    2.44  0       0            NA
-    ##  5 CD4 cm S10… typecanc… <NA>    -1.32    -0.927  -0.535 0       0            NA
-    ##  6 CD4 cm S10… typeheal… <NA>     0.535    0.927   1.32  0       0            NA
-    ##  7 CD4 cm hig… typecanc… <NA>     0.780    1.73    2.71  0.00100 1.67e-4      NA
-    ##  8 CD4 cm hig… typeheal… <NA>    -2.71    -1.73   -0.780 0.00100 1.67e-4      NA
-    ##  9 CD4 cm rib… typecanc… <NA>     0.398    1.05    1.66  0.00200 5.00e-4      NA
-    ## 10 CD4 cm rib… typeheal… <NA>    -1.66    -1.05   -0.398 0.00200 5.00e-4      NA
+    ##  1 B immature  typecanc… <NA>    -1.94     -1.40  -0.869 0       0            NA
+    ##  2 B immature  typeheal… <NA>     0.869     1.40   1.94  0       0            NA
+    ##  3 B mem       typecanc… <NA>    -2.32     -1.80  -1.24  0       0            NA
+    ##  4 B mem       typeheal… <NA>     1.24      1.80   2.32  0       0            NA
+    ##  5 CD4 cm S10… typecanc… <NA>    -1.54     -1.10  -0.648 0       0            NA
+    ##  6 CD4 cm S10… typeheal… <NA>     0.648     1.10   1.54  0       0            NA
+    ##  7 CD4 cm hig… typecanc… <NA>     0.833     1.87   2.94  2.50e-4 5.00e-5      NA
+    ##  8 CD4 cm hig… typeheal… <NA>    -2.94     -1.87  -0.833 2.50e-4 5.00e-5      NA
+    ##  9 CD4 cm rib… typecanc… <NA>     0.467     1.11   1.78  2.00e-3 5.23e-4      NA
+    ## 10 CD4 cm rib… typeheal… <NA>    -1.78     -1.11  -0.467 2.00e-3 5.23e-4      NA
     ## # ℹ 50 more rows
     ## # ℹ 9 more variables: c_R_k_hat <dbl>, v_lower <dbl>, v_effect <dbl>,
     ## #   v_upper <dbl>, v_pH0 <dbl>, v_FDR <dbl>, v_n_eff <dbl>, v_R_k_hat <dbl>,
@@ -267,16 +270,16 @@ res
     ## # A tibble: 60 × 14
     ##    cell_group        parameter factor c_lower c_effect c_upper c_n_eff c_R_k_hat
     ##    <chr>             <chr>     <chr>    <dbl>    <dbl>   <dbl>   <dbl>     <dbl>
-    ##  1 B immature        (Interce… <NA>    0.225     0.634   1.03      NaN      3.17
-    ##  2 B immature        typeheal… type    1.20      1.80    2.36      NaN      3.15
-    ##  3 B mem             (Interce… <NA>   -1.26     -0.864  -0.466     NaN      3.14
-    ##  4 B mem             typeheal… type    1.26      1.81    2.46      NaN      3.14
-    ##  5 CD4 cm S100A4     (Interce… <NA>    1.48      1.74    2.02      NaN      3.09
-    ##  6 CD4 cm S100A4     typeheal… type    0.575     0.967   1.38      NaN      3.12
-    ##  7 CD4 cm high cyto… (Interce… <NA>   -1.17     -0.655  -0.182     NaN      3.03
-    ##  8 CD4 cm high cyto… typeheal… type   -1.97     -1.27   -0.610     NaN      3.20
-    ##  9 CD4 cm ribosome   (Interce… <NA>   -0.0221    0.411   0.848     NaN      3.17
-    ## 10 CD4 cm ribosome   typeheal… type   -1.60     -0.974  -0.420     NaN      3.20
+    ##  1 B immature        (Interce… <NA>    0.459     0.791  1.12       NaN      2.43
+    ##  2 B immature        typeheal… type    0.946     1.41   1.89       NaN      2.42
+    ##  3 B mem             (Interce… <NA>   -1.25     -0.828 -0.401      NaN      2.51
+    ##  4 B mem             typeheal… type    1.33      1.95   2.56       NaN      2.51
+    ##  5 CD4 cm S100A4     (Interce… <NA>    1.36      1.62   1.88       NaN      2.43
+    ##  6 CD4 cm S100A4     typeheal… type    0.757     1.15   1.53       NaN      2.38
+    ##  7 CD4 cm high cyto… (Interce… <NA>   -1.08     -0.495  0.0909     NaN      2.44
+    ##  8 CD4 cm high cyto… typeheal… type   -2.32     -1.58  -0.852      NaN      2.45
+    ##  9 CD4 cm ribosome   (Interce… <NA>   -0.0578    0.339  0.724      NaN      2.46
+    ## 10 CD4 cm ribosome   typeheal… type   -1.57     -1.05  -0.516      NaN      2.43
     ## # ℹ 50 more rows
     ## # ℹ 6 more variables: v_lower <dbl>, v_effect <dbl>, v_upper <dbl>,
     ## #   v_n_eff <dbl>, v_R_k_hat <dbl>, count_data <list>

man/fragments/intro.Rmd

@@ -104,20 +104,19 @@ sccomp_result =
 
 ## Summary plots
 
-```{r, out.height="200%"}
-plots = sccomp_result |> plot() 
-```
 
 A plot of group proportion, faceted by groups. The blue boxplots represent the posterior predictive check. If the model is likely to be descriptively adequate to the data, the blue box plot should roughly overlay with the black box plot, which represents the observed data. The outliers are coloured in red. A box plot will be returned for every (discrete) covariate present in `formula_composition`. The colour coding represents the significant associations for composition and/or variability.
 
 ```{r}
-plots$boxplot
+sccomp_result |> 
+  sccomp_boxplot(factor = "type")
 ```
 
 A plot of estimates of differential composition (c\_) on the x-axis and differential variability (v\_) on the y-axis. The error bars represent 95% credible intervals. The dashed lines represent the minimal effect that the hypothesis test is based on. An effect is labelled as significant if bigger than the minimal effect according to the 95% credible interval. Facets represent the covariates in the model.
 
 ```{r}
-plots$credible_intervals_1D
+sccomp_result |> 
+  plot_1D_intervals()
 ```
 
 
@@ -127,7 +126,14 @@ We can plot the relationship between abundance and variability. As we can see be
 
 
 ```{r}
-plots$credible_intervals_2D
+sccomp_result |> 
+  plot_2D_intervals()
+```
+
+You can produce the series of plots calling the `plot` method.
+
+```{r, out.height="200%", eval=FALSE}
+sccomp_result |> plot() 
 ```
 
 

tests/testthat/test-sccomp_.R

@@ -418,8 +418,7 @@ test_that("plot_1d_intervals function works correctly", {
 
     my_estimate |> 
     sccomp_test() |> 
-    plot_1d_intervals(
-    .cell_group = cell_group,
+    plot_1D_intervals(
     significance_threshold = 0.025
   ) |> 
   expect_s3_class("patchwork")
@@ -429,8 +428,7 @@ test_that("plot_1d_intervals function works correctly", {
 test_that("plot_2d_intervals function works correctly", {
   my_estimate |> 
     sccomp_test() |> 
-    plot_2d_intervals(
-      .cell_group = cell_group,
+    plot_2D_intervals(
       significance_threshold = 0.025
     ) |>
   expect_s3_class("ggplot")

vignettes/introduction.Rmd

@@ -113,20 +113,19 @@ sccomp_result =
 
 ## Summary plots
 
-```{r, out.height="200%"}
-plots = sccomp_result |> plot() 
-```
 
 A plot of group proportion, faceted by groups. The blue boxplots represent the posterior predictive check. If the model is likely to be descriptively adequate to the data, the blue box plot should roughly overlay with the black box plot, which represents the observed data. The outliers are coloured in red. A box plot will be returned for every (discrete) covariate present in `formula_composition`. The colour coding represents the significant associations for composition and/or variability.
 
 ```{r}
-plots$boxplot
+sccomp_result |> 
+  sccomp_boxplot(factor = "type")
 ```
 
 A plot of estimates of differential composition (c\_) on the x-axis and differential variability (v\_) on the y-axis. The error bars represent 95% credible intervals. The dashed lines represent the minimal effect that the hypothesis test is based on. An effect is labelled as significant if bigger than the minimal effect according to the 95% credible interval. Facets represent the covariates in the model.
 
 ```{r}
-plots$credible_intervals_1D
+sccomp_result |> 
+  plot_1D_intervals()
 ```
 
 
@@ -136,9 +135,15 @@ We can plot the relationship between abundance and variability. As we can see be
 
 
 ```{r}
-plots$credible_intervals_2D
+sccomp_result |> 
+  plot_2D_intervals()
 ```
 
+You can produce the series of plots calling the `plot` method.
+
+```{r, out.height="200%", eval=FALSE}
+sccomp_result |> plot() 
+```
 
 ## Contrasts