Merge branch 'main' of https://github.com/jr-leary7/scLANE into dev

jr-leary7 · Nov 13, 2023 · 19acd5f · 19acd5f
2 parents 6fc5a5a + 4766369
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diff --git a/README.md b/README.md
@@ -24,6 +24,7 @@
 <!-- badges: start -->
 
 [![R-CMD-check](https://github.com/jr-leary7/scLANE/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/jr-leary7/scLANE/actions/workflows/R-CMD-check.yaml)
+[![Bioc-check](https://github.com/jr-leary7/scLANE/actions/workflows/bioc-check.yaml/badge.svg)](https://github.com/jr-leary7/scLANE/actions/workflows/bioc-check.yaml)
 ![release](https://img.shields.io/github/v/release/jr-leary7/scLANE?color=purple)
 ![last
 commit](https://img.shields.io/github/last-commit/jr-leary7/scLANE/main?color=darkgreen)
@@ -94,11 +95,8 @@ conflict.
 
 ``` r
 library(dplyr)
-library(scater)
 library(scLANE)
 library(ggplot2)
-select <- dplyr::select
-filter <- dplyr::filter
 ```
 
 ## Input data
@@ -117,8 +115,101 @@ The PCA embeddings show us a pretty simple trajectory that’s strongly
 correlated with the first principal component.
 
 ``` r
-plotPCA(sim_data, colour_by = "cell_time_normed") + 
+data.frame(sim_data@int_colData$reducedDims@listData$PCA[, 1:2]) %>% 
+  mutate(pseudotime = sim_data$cell_time_normed) %>% 
+  ggplot(aes(x = PC1, y = PC2, color = pseudotime)) + 
+  geom_point(size = 2, alpha = 0.75, stroke = 0) + 
+  scale_color_gradientn(colors = viridisLite::plasma(n = 20)) + 
+  labs(x = "PC 1", y = "PC 2", color = "Pseudotime") + 
   theme_scLANE(umap = TRUE)
+#> Loading required package: S4Vectors
+#> Loading required package: stats4
+#> Loading required package: BiocGenerics
+#> 
+#> Attaching package: 'BiocGenerics'
+#> The following objects are masked from 'package:dplyr':
+#> 
+#>     combine, intersect, setdiff, union
+#> The following objects are masked from 'package:stats':
+#> 
+#>     IQR, mad, sd, var, xtabs
+#> The following objects are masked from 'package:base':
+#> 
+#>     anyDuplicated, aperm, append, as.data.frame, basename, cbind,
+#>     colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,
+#>     get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,
+#>     match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
+#>     Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort,
+#>     table, tapply, union, unique, unsplit, which.max, which.min
+#> 
+#> Attaching package: 'S4Vectors'
+#> The following objects are masked from 'package:dplyr':
+#> 
+#>     first, rename
+#> The following object is masked from 'package:utils':
+#> 
+#>     findMatches
+#> The following objects are masked from 'package:base':
+#> 
+#>     expand.grid, I, unname
+#> Loading required package: SingleCellExperiment
+#> Loading required package: SummarizedExperiment
+#> Loading required package: MatrixGenerics
+#> Loading required package: matrixStats
+#> 
+#> Attaching package: 'matrixStats'
+#> The following object is masked from 'package:dplyr':
+#> 
+#>     count
+#> 
+#> Attaching package: 'MatrixGenerics'
+#> The following objects are masked from 'package:matrixStats':
+#> 
+#>     colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
+#>     colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
+#>     colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
+#>     colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
+#>     colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
+#>     colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
+#>     colWeightedMeans, colWeightedMedians, colWeightedSds,
+#>     colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
+#>     rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
+#>     rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
+#>     rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
+#>     rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
+#>     rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
+#>     rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
+#>     rowWeightedSds, rowWeightedVars
+#> Loading required package: GenomicRanges
+#> Loading required package: IRanges
+#> 
+#> Attaching package: 'IRanges'
+#> The following objects are masked from 'package:dplyr':
+#> 
+#>     collapse, desc, slice
+#> Loading required package: GenomeInfoDb
+#> 
+#> Attaching package: 'GenomicRanges'
+#> The following object is masked from 'package:magrittr':
+#> 
+#>     subtract
+#> Loading required package: Biobase
+#> Welcome to Bioconductor
+#> 
+#>     Vignettes contain introductory material; view with
+#>     'browseVignettes()'. To cite Bioconductor, see
+#>     'citation("Biobase")', and for packages 'citation("pkgname")'.
+#> 
+#> Attaching package: 'Biobase'
+#> The following object is masked from 'package:MatrixGenerics':
+#> 
+#>     rowMedians
+#> The following objects are masked from 'package:matrixStats':
+#> 
+#>     anyMissing, rowMedians
+#> Registered S3 method overwritten by 'SparseArray':
+#>   method           from        
+#>   rowsum.dgCMatrix DelayedArray
 ```
 
 <img src="man/figures/README-plot-sims-pt-1.png" width="100%" />
@@ -127,7 +218,11 @@ We also see that the data are not clustered by subject, which indicates
 that gene dynamics are mostly homogeneous across subjects.
 
 ``` r
-plotPCA(sim_data, colour_by = "subject") + 
+data.frame(sim_data@int_colData$reducedDims@listData$PCA[, 1:2]) %>% 
+  mutate(subject = sim_data$subject) %>% 
+  ggplot(aes(x = PC1, y = PC2, color = subject)) + 
+  geom_point(size = 2, alpha = 0.75, stroke = 0) + 
+  labs(x = "PC 1", y = "PC 2", color = "Subject ID") + 
   theme_scLANE(umap = TRUE)
 ```
 
@@ -181,7 +276,7 @@ scLANE_models_glm <- testDynamic(sim_data,
 #> Registered S3 method overwritten by 'bit':
 #>   method   from  
 #>   print.ri gamlss
-#> scLANE testing completed for 100 genes across 1 lineage in 35.114 secs
+#> scLANE testing completed for 100 genes across 1 lineage in 29.977 secs
 ```
 
 After the function finishes running, we use `getResultsDE()` to generate
@@ -200,13 +295,13 @@ select(scLANE_res_glm, Gene, Lineage, Test_Stat, P_Val, P_Val_Adj, Gene_Dynamic_
                col.names = c("Gene", "Lineage", "LRT stat.", "P-value", "Adj. p-value", "Predicted dynamic status"))
 ```
 
-| Gene     | Lineage | LRT stat. | P-value | Adj. p-value | Predicted dynamic status |
-|:---------|:--------|----------:|--------:|-------------:|-------------------------:|
-| MFSD2B   | A       |   209.755 |   0.000 |        0.000 |                        1 |
-| GOLGA8EP | A       |     4.390 |   0.036 |        0.537 |                        0 |
-| NOP14    | A       |     7.712 |   0.005 |        0.132 |                        0 |
-| TMCO3    | A       |   167.582 |   0.000 |        0.000 |                        1 |
-| BATF2    | A       |     5.216 |   0.074 |        0.537 |                        0 |
+| Gene   | Lineage | LRT stat. | P-value | Adj. p-value | Predicted dynamic status |
+|:-------|:--------|----------:|--------:|-------------:|-------------------------:|
+| MFSD2B | A       |   209.755 |   0.000 |        0.000 |                        1 |
+| SMG1   | A       |     5.062 |   0.080 |        0.419 |                        0 |
+| UAP1L1 | A       |     9.118 |   0.010 |        0.199 |                        0 |
+| TMCO3  | A       |   167.582 |   0.000 |        0.000 |                        1 |
+| MYOF   | A       |     4.015 |   0.045 |        0.419 |                        0 |
 
 ### GEE mode
 
@@ -229,7 +324,7 @@ scLANE_models_gee <- testDynamic(sim_data,
                                  id.vec = sim_data$subject, 
                                  cor.structure = "ar1", 
                                  n.cores = 4)
-#> scLANE testing completed for 100 genes across 1 lineage in 2.201 mins
+#> scLANE testing completed for 100 genes across 1 lineage in 3.076 mins
 ```
 
 We again generate the table of DE test results. The variance of the
@@ -248,11 +343,11 @@ select(scLANE_res_gee, Gene, Lineage, Test_Stat, P_Val, P_Val_Adj, Gene_Dynamic_
 
 | Gene     | Lineage | Wald stat. | P-value | Adj. p-value | Predicted dynamic status |
 |:---------|:--------|-----------:|--------:|-------------:|-------------------------:|
-| DGUOK    | A       | 200675.460 |   0.000 |        0.000 |                        1 |
-| VDAC1    | A       |     13.025 |   0.001 |        0.025 |                        0 |
+| CKAP4    | A       |  48922.644 |       0 |            0 |                        1 |
+| TBCC     | A       |     32.151 |       0 |            0 |                        1 |
 | GOLGA8EP | A       |         NA |      NA |           NA |                        0 |
-| WAPAL    | A       |   3254.351 |   0.000 |        0.000 |                        1 |
-| IDH3G    | A       |    863.479 |   0.000 |        0.000 |                        1 |
+| PFDN2    | A       |   2131.763 |       0 |            0 |                        1 |
+| IDH3G    | A       |    863.479 |       0 |            0 |                        1 |
 
 ### GLMM mode
 
@@ -276,7 +371,7 @@ scLANE_models_glmm <- testDynamic(sim_data,
                                   glmm.adaptive = TRUE, 
                                   id.vec = sim_data$subject, 
                                   n.cores = 4)
-#> scLANE testing completed for 100 genes across 1 lineage in 3.427 mins
+#> scLANE testing completed for 100 genes across 1 lineage in 4.863 mins
 ```
 
 {% note %}
@@ -306,8 +401,8 @@ select(scLANE_res_glmm, Gene, Lineage, Test_Stat, P_Val, P_Val_Adj, Gene_Dynamic
 
 | Gene    | Lineage | LRT stat. | P-value | Adj. p-value | Predicted dynamic status |
 |:--------|:--------|----------:|--------:|-------------:|-------------------------:|
-| DDX1    | A       |   131.707 |   0.000 |            0 |                        1 |
-| TSPAN1  | A       |    78.616 |   0.000 |            0 |                        1 |
+| PGLS    | A       |   129.086 |   0.000 |            0 |                        1 |
+| TSPAN1  | A       |    85.987 |   0.000 |            0 |                        1 |
 | WDSUB1  | A       |        NA |      NA |           NA |                        0 |
 | FAM135B | A       |        NA |      NA |           NA |                        0 |
 | NLGN4Y  | A       |     9.878 |   0.627 |            1 |                        0 |

diff --git a/man/figures/README-plot-models-gee-1.png b/man/figures/README-plot-models-gee-1.png
diff --git a/man/figures/README-plot-models-glm-1.png b/man/figures/README-plot-models-glm-1.png
diff --git a/man/figures/README-plot-models-glmm-1.png b/man/figures/README-plot-models-glmm-1.png
diff --git a/man/figures/README-plot-sims-pt-1.png b/man/figures/README-plot-sims-pt-1.png
diff --git a/man/figures/README-plot-sims-subj-1.png b/man/figures/README-plot-sims-subj-1.png
diff --git a/man/figures/README-unnamed-chunk-2-1.png b/man/figures/README-unnamed-chunk-2-1.png