From 8db0a4f0cf31386b988cf1dd045c9f25e663963a Mon Sep 17 00:00:00 2001 From: Gene233 <44050457+Gene233@users.noreply.github.com> Date: Mon, 20 May 2024 11:39:05 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20DavisLab?= =?UTF-8?q?oratory/smartid@1c678fbb233b03648a1dfe5633b07f1231ababf0=20?= =?UTF-8?q?=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- 404.html | 6 +- LICENSE-text.html | 6 +- LICENSE.html | 6 +- articles/index.html | 6 +- articles/smartid_Demo.html | 178 +++++++++--------- .../figure-html/unnamed-chunk-10-1.png | Bin 37285 -> 36976 bytes .../figure-html/unnamed-chunk-13-1.png | Bin 71041 -> 70834 bytes .../figure-html/unnamed-chunk-6-1.png | Bin 47639 -> 47227 bytes .../figure-html/unnamed-chunk-8-1.png | Bin 40297 -> 39995 bytes .../figure-html/unnamed-chunk-9-1.png | Bin 56394 -> 57566 bytes .../figure-html/unnamed-chunk-9-2.png | Bin 50485 -> 51440 bytes .../figure-html/unnamed-chunk-9-3.png | Bin 78826 -> 79313 bytes .../figure-html/unnamed-chunk-9-4.png | Bin 36657 -> 39027 bytes authors.html | 12 +- index.html | 16 +- news/index.html | 6 +- pkgdown.yml | 6 +- reference/cal_score.html | 6 +- reference/cal_score_init.html | 6 +- reference/gs_score.html | 6 +- reference/gs_score_init.html | 6 +- reference/iae.html | 6 +- reference/iae_hdb.html | 6 +- reference/iae_igm.html | 6 +- reference/iae_m.html | 6 +- reference/iae_prob.html | 6 +- reference/iae_rf.html | 6 +- reference/iae_sd.html | 6 +- reference/idf.html | 6 +- reference/idf_hdb.html | 6 +- reference/idf_iae_methods.html | 6 +- reference/idf_igm.html | 6 +- reference/idf_m.html | 6 +- reference/idf_prob.html | 6 +- reference/idf_rf.html | 6 +- reference/idf_sd.html | 6 +- reference/index.html | 6 +- reference/markers_hdbscan.html | 6 +- reference/markers_mclust.html | 6 +- reference/markers_mixmdl.html | 6 +- reference/ova_score_boxplot-1.png | Bin 24639 -> 24486 bytes reference/ova_score_boxplot.html | 6 +- reference/scale_mgm.html | 6 +- reference/score_barplot.html | 6 +- reference/sim_sce_test.html | 6 +- reference/sin_score_boxplot-1.png | Bin 53471 -> 53198 bytes reference/sin_score_boxplot.html | 6 +- reference/smartid_Package.html | 12 +- reference/tf.html | 6 +- reference/top_markers.html | 6 +- reference/top_markers_abs.html | 6 +- reference/top_markers_glm.html | 6 +- reference/top_markers_init.html | 6 +- 53 files changed, 229 insertions(+), 223 deletions(-) diff --git a/404.html b/404.html index 35a8f9f..8e9885f 100644 --- a/404.html +++ b/404.html @@ -6,7 +6,7 @@
vignettes/smartid_Demo.Rmd
smartid_Demo.Rmd
library(smartid)
library(SummarizedExperiment)
-library(splatter)
+library(splatter)
library(ggplot2)
library(scater)
@@ -181,8 +181,9 @@ Prepare Data#> [1] "Gene26" "Gene28" "Gene357" "Gene405" "Gene462" "Gene518" "Gene833"
#>
#> $DEFacGroup4
-#> character(0)
-data_sim
+#> character(0)
+data_sim #> class: SingleCellExperiment #> dim: 1000 3000 #> metadata(2): Params up_markers @@ -219,7 +220,7 @@
to see available methods for IDF/IAE term. More details of each term can be seen in help page of each function, e.g.Score Samplesidf_iae_methods()
?idf
. -+## show available methods idf_iae_methods() #> unlabel HDBSCAN label IGM unlabel max @@ -258,7 +259,7 @@
Score Samples
++## compute score system.time( data_sim <- cal_score( @@ -271,13 +272,15 @@
Score Samples) ) #> user system elapsed -#> 0.201 0.004 0.205 - +#> 0.206 0.003 0.210
++## score and tf,idf,iae all saved assays(data_sim) #> List of length 7 -#> names(7): BatchCellMeans BaseCellMeans BCV CellMeans TrueCounts counts score -names(metadata(data_sim)) +#> names(7): BatchCellMeans BaseCellMeans BCV CellMeans TrueCounts counts score
+names(metadata(data_sim)) #> [1] "Params" "up_markers" "tf" "idf" "iae"
@@ -293,7 +296,7 @@Scale and Transform Score\[z=\frac{x-\frac{\sum_k^{n_D}(\mu_k)}{n_D}}{sd}\]
The score will be transformed using softmax before passing to EM algorithm.
-+top_m <- top_markers( data = data_sim, label = "Group", @@ -323,7 +326,7 @@
Scale and Transform Score -
+score_barplot( top_markers = top_m, column = ".dot", @@ -338,7 +341,7 @@
Scale and Transform ScoreThis can also be confirmed in data simulation information, where the scale factor is higher in Group2, but the GeneMean is too small to be confident. Thus this gene won’t be selected by
smartid
. -+-sin_score_boxplot( metadata(data_sim)$tf, features = "Gene76", @@ -354,7 +357,7 @@
Scale and Transform Score#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was #> generated.
+## sim gene info SummarizedExperiment::elementMetadata(data_sim)[76, ] @@ -385,7 +388,7 @@
Marker Selection -
+-set.seed(123) marker_ls <- markers_mixmdl( top_markers = top_m, @@ -406,7 +409,7 @@
Marker Selection
#> number of iterations= 507 #> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
+marker_ls #> $Group1 #> [1] "Gene352" "Gene696" "Gene425" "Gene391" "Gene225" "Gene893" "Gene913" @@ -426,7 +429,7 @@
Marker Selection -
+ @@ -442,7 +445,7 @@Marker Selection
Similarly, this method also allows to plot the mixture distribution for each component, but separately.
-+-set.seed(123) marker_ls_new <- markers_mclust( top_markers = top_m, @@ -451,12 +454,12 @@
Marker Selection plot = TRUE )
+We can compare the marker list with the previous one. The overlap result shows 2 methods can achieve almost the same marker list for each group.
- @@ -480,7 +483,7 @@Score SamplesSimilarly, the first step is to score samples/cells using the specified method. This step also starts with raw counts data, without need for data pre-processing, which is quite convenient and fast. -
++## compute score without label system.time( data_sim <- cal_score( @@ -492,13 +495,15 @@
Score Samples ) ) #> user system elapsed -#> 0.167 0.032 0.200 - +#> 0.205 0.000 0.206
++## new score is saved and tf,idf,iae all updated assays(data_sim) #> List of length 8 -#> names(8): BatchCellMeans BaseCellMeans BCV ... counts score score_unlabel -names(metadata(data_sim)) +#> names(8): BatchCellMeans BaseCellMeans BCV ... counts score score_unlabel
+names(metadata(data_sim)) #> [1] "Params" "up_markers" "tf" "idf" "iae"
@@ -508,7 +513,7 @@Compute Overall Score for Gene-setgs_score() can easily compute the overall score for each cell based on the given gene-set list. -
+diff --git a/articles/smartid_Demo_files/figure-html/unnamed-chunk-10-1.png b/articles/smartid_Demo_files/figure-html/unnamed-chunk-10-1.png index 2ecb73cce1d11abe010a5047e1842ee478ce8e29..ce3885defba697d0de8877611d3fd1230333de16 100644 GIT binary patch delta 24787 zcmbrm2Rznq-#>gxr6E$$GD_Pjl@S?Dsf@BiDp6TQX2w^O5{e{bW$$E## compute score for each group marker list data_sim <- gs_score( data = data_sim, @@ -527,7 +532,7 @@
Compute Overall Score for Gene-set
It’s evident that the score can sufficiently separate the target group from all others.
-@@ -655,7 +659,7 @@+as.data.frame(colData(data_sim)) |> tidyr::pivot_longer("Group1.score.unlabel":"Group3.score.unlabel", names_to = "group markers", @@ -543,9 +548,9 @@
Compute Overall Score for Gene-set
SessionInfo
-++#> [65] generics_0.1.3 sparseMatrixStats_1.17.0 +#> [67] munsell_0.5.1 scales_1.3.0 +#> [69] glue_1.7.0 lazyeval_0.2.2 +#> [71] tools_4.4.0 data.table_1.15.4 +#> [73] BiocNeighbors_1.23.0 tokenizers_0.3.0 +#> [75] ScaledMatrix_1.13.0 ggsignif_0.6.4 +#> [77] locfit_1.5-9.9 fs_1.6.4 +#> [79] grid_4.4.0 tidyr_1.3.1 +#> [81] colorspace_2.1-0 nlme_3.1-164 +#> [83] GenomeInfoDbData_1.2.12 beeswarm_0.4.0 +#> [85] BiocSingular_1.21.0 vipor_0.4.7 +#> [87] cli_3.6.2 rsvd_1.0.5 +#> [89] textshaping_0.3.7 fansi_1.0.6 +#> [91] segmented_2.1-0 S4Arrays_1.5.0 +#> [93] viridisLite_0.4.2 dplyr_1.1.4 +#> [95] gtable_0.3.5 rstatix_0.7.2 +#> [97] sass_0.4.9 digest_0.6.35 +#> [99] SparseArray_1.5.4 ggrepel_0.9.5 +#> [101] htmlwidgets_1.6.4 farver_2.1.2 +#> [103] memoise_2.0.1 htmltools_0.5.8.1 +#> [105] pkgdown_2.0.9 lifecycle_1.0.4 +#> [107] httr_1.4.7 MASS_7.3-60.2sessionInfo() -#> R Under development (unstable) (2024-03-24 r86185) +#> R version 4.4.0 (2024-04-24) #> Platform: x86_64-pc-linux-gnu #> Running under: Ubuntu 22.04.4 LTS #> @@ -569,72 +574,71 @@
SessionInfo#> [8] base #> #> other attached packages: -#> [1] UpSetR_1.4.0 scater_1.31.2 -#> [3] scuttle_1.13.1 ggplot2_3.5.0 -#> [5] splatter_1.27.0 SingleCellExperiment_1.25.1 -#> [7] SummarizedExperiment_1.33.3 Biobase_2.63.0 -#> [9] GenomicRanges_1.55.4 GenomeInfoDb_1.39.9 -#> [11] IRanges_2.37.1 S4Vectors_0.41.5 -#> [13] BiocGenerics_0.49.1 MatrixGenerics_1.15.0 -#> [15] matrixStats_1.2.0 smartid_0.99.5 -#> [17] BiocStyle_2.31.0 +#> [1] UpSetR_1.4.0 scater_1.33.0 +#> [3] scuttle_1.15.0 ggplot2_3.5.1 +#> [5] splatter_1.29.0 SingleCellExperiment_1.27.0 +#> [7] SummarizedExperiment_1.35.0 Biobase_2.65.0 +#> [9] GenomicRanges_1.57.0 GenomeInfoDb_1.41.0 +#> [11] IRanges_2.39.0 S4Vectors_0.43.0 +#> [13] BiocGenerics_0.51.0 MatrixGenerics_1.17.0 +#> [15] matrixStats_1.3.0 smartid_1.1.0 +#> [17] BiocStyle_2.33.0 #> #> loaded via a namespace (and not attached): -#> [1] bitops_1.0-7 gridExtra_2.3 -#> [3] rlang_1.1.3 magrittr_2.0.3 -#> [5] tidytext_0.4.1 compiler_4.4.0 -#> [7] DelayedMatrixStats_1.25.1 systemfonts_1.0.6 -#> [9] vctrs_0.6.5 pkgconfig_2.0.3 -#> [11] crayon_1.5.2 fastmap_1.1.1 -#> [13] backports_1.4.1 XVector_0.43.1 -#> [15] labeling_0.4.3 utf8_1.2.4 -#> [17] rmarkdown_2.26 ggbeeswarm_0.7.2 -#> [19] ragg_1.3.0 purrr_1.0.2 -#> [21] xfun_0.43 zlibbioc_1.49.3 -#> [23] cachem_1.0.8 beachmat_2.19.2 -#> [25] jsonlite_1.8.8 SnowballC_0.7.1 -#> [27] highr_0.10 DelayedArray_0.29.9 -#> [29] BiocParallel_1.37.1 broom_1.0.5 +#> [1] gridExtra_2.3 rlang_1.1.3 +#> [3] magrittr_2.0.3 tidytext_0.4.2 +#> [5] compiler_4.4.0 DelayedMatrixStats_1.27.0 +#> [7] systemfonts_1.1.0 vctrs_0.6.5 +#> [9] pkgconfig_2.0.3 crayon_1.5.2 +#> [11] fastmap_1.2.0 backports_1.4.1 +#> [13] XVector_0.45.0 labeling_0.4.3 +#> [15] utf8_1.2.4 rmarkdown_2.27 +#> [17] UCSC.utils_1.1.0 ggbeeswarm_0.7.2 +#> [19] ragg_1.3.2 purrr_1.0.2 +#> [21] xfun_0.44 zlibbioc_1.51.0 +#> [23] cachem_1.1.0 beachmat_2.21.0 +#> [25] jsonlite_1.8.8 highr_0.10 +#> [27] SnowballC_0.7.1 DelayedArray_0.31.1 +#> [29] BiocParallel_1.39.0 broom_1.0.6 #> [31] irlba_2.3.5.1 parallel_4.4.0 #> [33] R6_2.5.1 bslib_0.7.0 -#> [35] stringi_1.8.3 car_3.1-2 +#> [35] stringi_1.8.4 car_3.1-2 #> [37] jquerylib_0.1.4 Rcpp_1.0.12 -#> [39] bookdown_0.38 knitr_1.45 +#> [39] bookdown_0.39 knitr_1.46 #> [41] mixtools_2.0.0 splines_4.4.0 #> [43] Matrix_1.7-0 tidyselect_1.2.1 #> [45] abind_1.4-5 yaml_2.3.8 -#> [47] viridis_0.6.5 codetools_0.2-19 +#> [47] viridis_0.6.5 codetools_0.2-20 #> [49] plyr_1.8.9 lattice_0.22-6 #> [51] tibble_3.2.1 withr_3.0.0 -#> [53] evaluate_0.23 survival_3.5-8 -#> [55] desc_1.4.3 mclust_6.1 -#> [57] kernlab_0.9-32 pillar_1.9.0 -#> [59] BiocManager_1.30.22 ggpubr_0.6.0 +#> [53] evaluate_0.23 survival_3.6-4 +#> [55] desc_1.4.3 mclust_6.1.1 +#> [57] kernlab_0.9-32 ggpubr_0.6.0 +#> [59] pillar_1.9.0 BiocManager_1.30.23 #> [61] carData_3.0-5 janeaustenr_1.0.0 #> [63] checkmate_2.3.1 plotly_4.10.4 -#> [65] generics_0.1.3 RCurl_1.98-1.14 -#> [67] sparseMatrixStats_1.15.0 munsell_0.5.0 -#> [69] scales_1.3.0 glue_1.7.0 -#> [71] lazyeval_0.2.2 tools_4.4.0 -#> [73] data.table_1.15.2 BiocNeighbors_1.21.2 -#> [75] tokenizers_0.3.0 ScaledMatrix_1.11.1 -#> [77] ggsignif_0.6.4 locfit_1.5-9.9 -#> [79] fs_1.6.3 grid_4.4.0 -#> [81] tidyr_1.3.1 colorspace_2.1-0 -#> [83] nlme_3.1-164 GenomeInfoDbData_1.2.12 -#> [85] beeswarm_0.4.0 BiocSingular_1.19.0 -#> [87] vipor_0.4.7 cli_3.6.2 -#> [89] rsvd_1.0.5 textshaping_0.3.7 -#> [91] fansi_1.0.6 segmented_2.0-3 -#> [93] S4Arrays_1.3.6 viridisLite_0.4.2 -#> [95] dplyr_1.1.4 gtable_0.3.4 -#> [97] rstatix_0.7.2 sass_0.4.9 -#> [99] digest_0.6.35 SparseArray_1.3.4 -#> [101] ggrepel_0.9.5 htmlwidgets_1.6.4 -#> [103] farver_2.1.1 memoise_2.0.1 -#> [105] htmltools_0.5.8 pkgdown_2.0.7 -#> [107] lifecycle_1.0.4 httr_1.4.7 -#> [109] MASS_7.3-60.2
SessionInfo -
Site built with pkgdown 2.0.7.
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