fixed README

README.Rmd

@@ -142,7 +142,8 @@ scLANE_models_gee <- testDynamic(sim_data,
                                  is.gee = TRUE, 
                                  id.vec = sim_data$subject, 
                                  cor.structure = "ar1", 
-                                 n.cores = 4)
+                                 n.cores = 4, 
+                                 verbose = FALSE)
 ```
 
 We again generate the table of DE test results. The variance of the estimated coefficients is determined using [the sandwich estimator](https://online.stat.psu.edu/stat504/lesson/12/12.3), and a Wald test is used to compare the null & alternate models. 
@@ -169,7 +170,8 @@ scLANE_models_glmm <- testDynamic(sim_data,
                                   is.glmm = TRUE, 
                                   glmm.adaptive = TRUE, 
                                   id.vec = sim_data$subject, 
-                                  n.cores = 4)
+                                  n.cores = 4, 
+                                  verbose = FALSE)
 ```
 
 **Note:** The GLMM mode is still under development, as we are working on further reducing runtime and increasing the odds of the underlying optimization process converging successfully. As such, updates will be frequent and functionality / results may shift slightly. 
@@ -193,7 +195,7 @@ We can use the `plotModels()` to visually compare different types of models. It
 
 ```{r plot-models-glm}
 plotModels(scLANE_models_glm, 
-           gene = "JARID2", 
+           gene = scLANE_res_glm$Gene[1], 
            pt = order_df, 
            expr.mat = sim_data, 
            size.factor.offset = cell_offset, 
@@ -207,7 +209,7 @@ Model comparison using the GEE mode is similar, with the only change being that
 
 ```{r plot-models-gee}
 plotModels(scLANE_models_gee, 
-           gene = "DGUOK", 
+           gene = scLANE_res_gee$Gene[1], 
            is.gee = TRUE, 
            id.vec = sim_data$subject, 
            pt = order_df, 
@@ -223,7 +225,7 @@ When plotting the models generated using the GLMM mode, we split by lineage & co
 
 ```{r plot-models-glmm, fig.width=9, fig.height=9}
 plotModels(scLANE_models_glmm, 
-           gene = "FLOT2", 
+           gene = scLANE_res_glmm$Gene[1], 
            pt = order_df, 
            expr.mat = sim_data, 
            size.factor.offset = cell_offset, 
@@ -246,6 +248,16 @@ scLANE_models_glm[["JARID2"]]$Lineage_A$Gene_Dynamics %>%
                col.names = c("Gene", "Lineage", "Breakpoint", "First Slope", "Second Slope", "First Trend", "Second Trend"))
 ```
 
+Coefficients can also be plotted like so:
+
+```{r}
+plotModelCoefs(scLANE_models_glm, 
+               gene = "JARID2", 
+               pt = order_df, 
+               expr.mat = sim_data,
+               size.factor.offset = cell_offset)
+```
+
 ### Knot distribution
 
 Lastly, we can pull the locations in pseudotime of all the knots fitted by `scLANE`. Visualizing this distribution gives us some idea of where transcriptional switches are occurring in the set of genes classified as dynamic.