diff --git a/articles/DeconvoBuddies.html b/articles/DeconvoBuddies.html index 4a02825..a9acf75 100644 --- a/articles/DeconvoBuddies.html +++ b/articles/DeconvoBuddies.html @@ -98,7 +98,7 @@

Louise Campus
lahuuki@gmail.com -

7 August 2024

+

12 August 2024

Source: vignettes/DeconvoBuddies.Rmd @@ -220,7 +220,7 @@

Access Data
 ## Access and snRNA-seq example data
 if (!exists("sce_DLPFC_example")) sce_DLPFC_example <- fetch_deconvo_data("sce_DLPFC_example")
-#> 2024-08-07 17:33:45.703235 loading file /github/home/.cache/R/BiocFileCache/4a1ec11882_sce_DLPFC_example.Rdata%3Frlkey%3Dv3z4u8ru0d2y12zgdl1az07q9%26st%3D1dcfqc1i%26dl%3D1
+#> 2024-08-12 15:08:35.815188 loading file /github/home/.cache/R/BiocFileCache/146b342eafd5_sce_DLPFC_example.Rdata%3Frlkey%3Dv3z4u8ru0d2y12zgdl1az07q9%26st%3D1dcfqc1i%26dl%3D1
 
 ## Explore snRNA-seq data in sce_DLPFC_example
 sce_DLPFC_example
@@ -239,7 +239,7 @@ 
Access Data## Access Bulk RNA-seq data
 if (!exists("rse_gene")) rse_gene <- fetch_deconvo_data("rse_gene")
-#> 2024-08-07 17:33:47.478342 loading file /github/home/.cache/R/BiocFileCache/4a1de5efa9_rse_gene.Rdata%3Frlkey%3Dsw2djr71y954yw4o3xrmjv59b%26dl%3D1
+#> 2024-08-12 15:08:37.663146 loading file /github/home/.cache/R/BiocFileCache/146b2dbc692f_rse_gene.Rdata%3Frlkey%3Dsw2djr71y954yw4o3xrmjv59b%26dl%3D1
 
 ## Explore bulk data in rse_gene
 rse_gene
@@ -470,9 +470,9 @@ 
Reproducibilitylibrary("knitr")
 knit("DeconvoBuddies.Rmd", tangle = TRUE)

Date the vignette was generated.

-
#> [1] "2024-08-07 17:33:51 UTC"
+
#> [1] "2024-08-12 15:08:43 UTC"

Wallclock time spent generating the vignette.

-
#> Time difference of 18.582 secs
+
#> Time difference of 19.447 secs

R session information.

#> ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────
 #>  setting  value
@@ -484,8 +484,8 @@ 
Reproducibility#>  collate  en_US.UTF-8
 #>  ctype    en_US.UTF-8
 #>  tz       UTC
-#>  date     2024-08-07
-#>  pandoc   3.2 @ /usr/bin/ (via rmarkdown)
+#>  date     2024-08-12
+#>  pandoc   3.3 @ /usr/bin/ (via rmarkdown)
 #> 
 #> ─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────
 #>  package              * version    date (UTC) lib source
@@ -529,7 +529,7 @@ 
Reproducibility#>  data.table             1.15.4     2024-03-30 [1] RSPM (R 4.4.0)
 #>  DBI                    1.2.3      2024-06-02 [1] RSPM (R 4.4.0)
 #>  dbplyr                 2.5.0      2024-03-19 [1] RSPM (R 4.4.0)
-#>  DeconvoBuddies       * 0.99.0     2024-08-07 [1] Bioconductor
+#>  DeconvoBuddies       * 0.99.0     2024-08-12 [1] Bioconductor
 #>  DelayedArray           0.31.11    2024-08-04 [1] Bioconductor 3.20 (R 4.4.1)
 #>  desc                   1.4.3      2023-12-10 [2] RSPM (R 4.4.0)
 #>  digest                 0.6.36     2024-06-23 [2] RSPM (R 4.4.0)
@@ -594,7 +594,7 @@ 
Reproducibility#>  paletteer              1.6.0      2024-01-21 [1] RSPM (R 4.4.0)
 #>  pillar                 1.9.0      2023-03-22 [2] RSPM (R 4.4.0)
 #>  pkgconfig              2.0.3      2019-09-22 [2] RSPM (R 4.4.0)
-#>  pkgdown                2.1.0.9000 2024-08-05 [1] Github (r-lib/pkgdown@1d40a80)
+#>  pkgdown                2.1.0.9000 2024-08-12 [1] Github (r-lib/pkgdown@1d40a80)
 #>  plotly                 4.10.4     2024-01-13 [1] RSPM (R 4.4.0)
 #>  plyr                   1.8.9      2023-10-02 [1] RSPM (R 4.4.0)
 #>  png                    0.1-8      2022-11-29 [1] RSPM (R 4.4.0)
diff --git a/articles/Deconvolution_Benchmark_DLPFC.html b/articles/Deconvolution_Benchmark_DLPFC.html
index b842b41..d83f1e8 100644
--- a/articles/Deconvolution_Benchmark_DLPFC.html
+++ b/articles/Deconvolution_Benchmark_DLPFC.html
@@ -98,7 +98,7 @@ 
Louise
 Campus

       
                   
-            
7 August 2024

+            
12 August 2024

       
       Source: vignettes/Deconvolution_Benchmark_DLPFC.Rmd
       
@@ -138,46 +138,46 @@ 
Deconvolution Methods
 
 
-
+
 Approach
 Method
 Citation
 Availability
 
 
-
+
 weighted least squares
 DWLS
 Tsoucas et al, Nature Comm, 2019
 R
 Package Cran
 
-
+
 Bias correction: Assay
 Bisque
 Jew et al, Nature Comm, 2020
 R Package github
 
-
+
 Bias correction: Sourse
 MuSiC
 Wang et al, Nature Communications, 2019
 R Package github
 
-
+
 Machine Learning
 CIBERSORTx
 Newman et al., Nature BioTech, 2019
 Webtool
 
-
+
 Bayesian
 BayesPrism
 Chu et al., Nature Cancer, 2022
 Webtool/R
 Package
 
-
+
 linear
 Hspe
 Hunt et al., Ann. Appl. Stat, 2021
@@ -261,7 +261,7 @@ 
Bulk RNA-seq data
 ## use fetch deconvon data to load rse_gene
 rse_gene <- fetch_deconvo_data("rse_gene")
-#> 2024-08-07 17:34:07.847847 loading file /github/home/.cache/R/BiocFileCache/4a1de5efa9_rse_gene.Rdata%3Frlkey%3Dsw2djr71y954yw4o3xrmjv59b%26dl%3D1
+#> 2024-08-12 15:08:59.333378 loading file /github/home/.cache/R/BiocFileCache/146b2dbc692f_rse_gene.Rdata%3Frlkey%3Dsw2djr71y954yw4o3xrmjv59b%26dl%3D1
 rse_gene
 #> class: RangedSummarizedExperiment 
 #> dim: 21745 110 
@@ -297,7 +297,7 @@ 
Reference snRNA-seq data
 ## Use spatialLIBD to fetch the snRNA-seq dataset
 sce_path_zip <- fetch_deconvo_data("sce")
-#> 2024-08-07 17:34:09.528637 loading file /github/home/.cache/R/BiocFileCache/4a1415db437_sce_DLPFC_annotated.zip%3Fdl%3D1
+#> 2024-08-12 15:09:01.898358 loading file /github/home/.cache/R/BiocFileCache/146b2a8cb8a7_sce_DLPFC_annotated.zip%3Fdl%3D1
 
 ## unzip and load the data
 sce_path <- unzip(sce_path_zip, exdir = tempdir())
@@ -388,10 +388,12 @@ 
3. Select Marker Genes
 
We have developed a method for finding marker genes called the “Mean
-Ratio”. We calculate the Mean Ratio for a target cell type
+Ratio”. We calculate the MeanRatio for a target cell type
 for each gene by dividing the mean expression of the target cell by the
 mean expression of the next highest non-target cell type. Genes with the
-highest Mean Ratio values are selected as marker genes.

+highest MeanRatio values are selected as marker genes.

+
For a tutorial on marker gene selection check out Vignette:
+Deconvolution Benchmark in Human DLPFC.

 

 

 

@@ -762,9 +764,9 @@ 
Reproducibilitylibrary("knitr")
 knit("Deconvolution_Benchmark_DLPFC.Rmd", tangle = TRUE)

 
Date the vignette was generated.

-
#> [1] "2024-08-07 17:35:08 UTC"

+
#> [1] "2024-08-12 15:10:03 UTC"

 
Wallclock time spent generating the vignette.

-
#> Time difference of 1.214 mins

+
#> Time difference of 1.27 mins

 
R session information.

 
#> ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────
 #>  setting  value
@@ -776,8 +778,8 @@ 
Reproducibility#>  collate  en_US.UTF-8
 #>  ctype    en_US.UTF-8
 #>  tz       UTC
-#>  date     2024-08-07
-#>  pandoc   3.2 @ /usr/bin/ (via rmarkdown)
+#>  date     2024-08-12
+#>  pandoc   3.3 @ /usr/bin/ (via rmarkdown)
 #> 
 #> ─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────
 #>  package              * version    date (UTC) lib source
@@ -822,7 +824,7 @@ 
Reproducibility#>  data.table             1.15.4     2024-03-30 [1] RSPM (R 4.4.0)
 #>  DBI                    1.2.3      2024-06-02 [1] RSPM (R 4.4.0)
 #>  dbplyr                 2.5.0      2024-03-19 [1] RSPM (R 4.4.0)
-#>  DeconvoBuddies       * 0.99.0     2024-08-07 [1] Bioconductor
+#>  DeconvoBuddies       * 0.99.0     2024-08-12 [1] Bioconductor
 #>  DelayedArray           0.31.11    2024-08-04 [1] Bioconductor 3.20 (R 4.4.1)
 #>  desc                   1.4.3      2023-12-10 [2] RSPM (R 4.4.0)
 #>  digest                 0.6.36     2024-06-23 [2] RSPM (R 4.4.0)
@@ -891,7 +893,7 @@ 
Reproducibility#>  paletteer              1.6.0      2024-01-21 [1] RSPM (R 4.4.0)
 #>  pillar                 1.9.0      2023-03-22 [2] RSPM (R 4.4.0)
 #>  pkgconfig              2.0.3      2019-09-22 [2] RSPM (R 4.4.0)
-#>  pkgdown                2.1.0.9000 2024-08-05 [1] Github (r-lib/pkgdown@1d40a80)
+#>  pkgdown                2.1.0.9000 2024-08-12 [1] Github (r-lib/pkgdown@1d40a80)
 #>  plotly                 4.10.4     2024-01-13 [1] RSPM (R 4.4.0)
 #>  plyr                   1.8.9      2023-10-02 [1] RSPM (R 4.4.0)
 #>  png                    0.1-8      2022-11-29 [1] RSPM (R 4.4.0)
diff --git a/articles/Marker_Finding.html b/articles/Marker_Finding.html
index 54c0154..8626235 100644
--- a/articles/Marker_Finding.html
+++ b/articles/Marker_Finding.html
@@ -98,7 +98,7 @@ 
Louise
 Campus

       
                   
-            
7 August 2024

+            
12 August 2024

       
       Source: vignettes/Marker_Finding.Rmd
       
@@ -113,21 +113,74 @@ 
Introduction
 
What are Marker Genes?
 

+
Cell type marker genes have cell type specific expression, that is
+high expression in the target cell type, and low expression in all other
+cell types. Sub-setting the genes considered in a cell type
+deconvolution analysis helps reduce noise and can improve the accuracy
+of a deconvolution method.

 

 
How can we select marker genes?
 

+
There are several approaches to select marker genes.

+
One popular method is “1 vs. All” differential expression (Lun et
+al., 2016, F1000Res) (TODO update citation), where genes are tested for
+differential expression between the target cell type, and a combined
+group of all “other” cell types. Statistically
+significant differntially expressed genes (DEGs) can be selected as a
+set of marker genes, DEGs can be ranked by high log fold change.

+
However in some cases 1vAll can select genes with high
+expression in non-target cell types, especially in cell types related to
+the target cell types (such as Neuron sub-types), or when there is a
+smaller number of cells in the cell type and the signal is disguised
+within the other group.

+
For example, in our snRNA-seq dataset from Human DLPFC selecting
+marker gene for the cell type Oligodendrocyte (Oligo), MBP has
+a high log fold change when testing by 1vALL (see illustration
+below). But, when the expression of MBP is observed by individual cell
+types there is also expression in the related cell types Microglia
+(Micro) and Oligodendrocyte precursor cells (OPC).

+

+1vALL Marker Gene differential expression vs. MeanRatio method for marker gene selection

+1vALL Marker Gene differential
+expression vs. MeanRatio method for marker gene selection

+

+
 

 
 

-
The MeanRatio Method
+
The Mean Ratio Method
 

+
To capture genes with more cell type specific expression and less
+noise, we developed the Mean Ratio method. The
+Mean Ratio method works by selecting genes with large
+differences between gene expression in the target cell type and the
+closest non-target cell type, by evaluating genes by their
+MeanRatio metric.

+
We calculate the MeanRatio for a target cell type for
+each gene by dividing the mean expression of the target cell by
+the mean expression of the next highest non-target cell type.
+Genes with the highest MeanRatio values are selected as
+marker genes.

+
In the above example, Oligo is the target cell type.
+Micro has the highest mean expression out of the other non-target (not
+Oligo) cell types. The
+MeanRatio = mean expression Oligo/mean expression Micro,
+for MBP MeanRatio = 2.68 for gene FOLH1
+MeanRatio is much higher 21.6 showing FOLH1 is the better
+marker gene (in contrast to ranking by 1vALL log FC). In the
+violin plots you can see that expression of FOLH1 is much more
+specific to Oligo than MBP, supporting the ranking by
+MeanRatio.

+
We have implemented the Mean Ratio method
+in this R package with the function get_mean_ratio() this
+Vignette will cover our process for marker gene selection.

 

 

 
Goals of this Vignette
 

 
We will be demonstrating how to use DeconvoBuddies tools
 when finding cell type marker genes in single cell RNA-seq data via the
-MeanRatio method.

+MeanRatio method.

 
    
 
  1. Install and load required packages
    2. 
 
  2. Download DLPFC snRNA-seq data
    3. 
@@ -172,21 +225,25 @@ 
    Load Other Packages
     library("spatialLIBD")
 library("DeconvoBuddies")
-library("SummarizedExperiment")
 library("SingleCellExperiment")
 library("dplyr")
-library("tidyr")
-library("tibble")
-library("ggplot2")

+# library("tidyr")
+# library("tibble")
+# library("ggplot2")

 
 
 

 
2. Download DLPFC snRNA-seq data.
 

+
Here we will download single nucleus RNA-seq data from the Human
+DLPFC with 77k nuclei x 36k genes (TODO cite). This data is stored in a
+SingleCellExperiment object. The nuclei in this dataset are
+labled by cell types at a few resolutions, we will focus on the “broad”
+resolution that contains seven cell types.

 
 ## Use spatialLIBD to fetch the snRNA-seq dataset
 sce_path_zip <- fetch_deconvo_data("sce")
-#> 2024-08-07 17:35:26.553214 loading file /github/home/.cache/R/BiocFileCache/4a1415db437_sce_DLPFC_annotated.zip%3Fdl%3D1
+#> 2024-08-12 15:10:22.281689 loading file /github/home/.cache/R/BiocFileCache/146b2a8cb8a7_sce_DLPFC_annotated.zip%3Fdl%3D1
 
 ## unzip and load the data
 sce_path <- unzip(sce_path_zip, exdir = tempdir())
@@ -201,8 +258,6 @@ 
2. Download DLPFC snRNA-seq data.sce <- sce[, sce$cellType_broad_hc != "Ambiguous"]
 sce$cellType_broad_hc <- droplevels(sce$cellType_broad_hc)
 
-dim(sce)
-#> [1] 36601 56447
 ## Check the broad cell type distribution
 table(sce$cellType_broad_hc)
 #> 
@@ -210,7 +265,200 @@ 
2. Download DLPFC snRNA-seq data.#>      3979      2157      1601     10894      1940     24809     11067
 
 ## We're going to subset to the first 5k genes to save memory
-sce <- sce[seq_len(5000), ]

+## In a real application you'll want to use the full dataset
+sce <- sce[seq_len(5000), ]
+
+## check the final dimensions of the dataset
+dim(sce)
+#> [1]  5000 56447

+
+

+
3. Find MeanRatio marker genes
+

+
To find Mean Ratio marker genes for the data in
+sce we’ll use the function
+DeconvoBuddies::get_mean_ratio(), this function takes a
+SingleCellExperiment object sce the name of
+the column in the colData(sce) that contains the cell type
+annotations of interest (here we’ll use cellType_broad_hc),
+and optionally you can also supply additional column names from the
+rowData(sce) to add the gene_name and/or
+gene_ensembl information to the table output of
+get_mean_ratio.

+
+# calculate the Mean Ratio of genes for each cell type in sce
+mean_ratio_marker_stats <- get_mean_ratio(
+    sce = sce, # sce is the SingleCellExperiment with our data
+    assay_name = "logcounts", ## assay to use, we recommend logcounts [default]
+    cellType_col = "cellType_broad_hc", # column in colData with cell type info
+    gene_ensembl = "gene_id", # column in rowData with ensembl gene ids
+    gene_name = "gene_name" # column in rowData with gene names/symbols
+)

+
The function get_mean_ratio() returns a
+tibble with the following columns:

+
    
+
  • 
+gene is the name of the gene (from
+rownames(sce)).
    • 
+
  • 
+cellType.target is the cell type we’re finding marker
+genes for.
    • 
+
  • 
+mean.target is the mean expression of gene
+for cellType.target.
    • 
+
  • 
+cellType.2nd is the second highest non-target cell
+type.
    • 
+
  • 
+mean.2nd is the mean expression of gene
+for cellType.2nd.
    • 
+
  • 
+MeanRatio is the ratio of
+mean.target/mean.2nd.
    • 
+
  • 
+MeanRatio.rank is the rank of MeanRatio
+for the cell type.
    • 
+
  • 
+MeanRatio.anno is an annotation of the
+MeanRatio calculation helpful for plotting.
    • 
+
  • 
+gene_ensembl & gene_name optional cols
+from rowData(sce) specified by the user to add gene
+information
    • 
+

+
+## Explore the tibble output
+mean_ratio_marker_stats
+#> # A tibble: 1,721 × 10
+#>    gene     cellType.target mean.target cellType.2nd mean.2nd MeanRatio
+#>    <chr>    <fct>                 <dbl> <fct>           <dbl>     <dbl>
+#>  1 LYPD6B   Inhib                 1.20  Excit          0.0967     12.4 
+#>  2 GREM2    Inhib                 1.20  Excit          0.271       4.44
+#>  3 IGSF3    Inhib                 0.899 Excit          0.241       3.72
+#>  4 GALNT14  Inhib                 1.86  Excit          0.503       3.69
+#>  5 LYPD6    Inhib                 1.09  Astro          0.435       2.51
+#>  6 SLC35D1  Inhib                 0.781 OPC            0.375       2.08
+#>  7 FLVCR1   Inhib                 0.576 Excit          0.336       1.72
+#>  8 RAVER2   Inhib                 1.09  OPC            0.651       1.68
+#>  9 ARHGEF11 Inhib                 1.81  Excit          1.10        1.64
+#> 10 VAV3     Inhib                 1.68  Astro          1.03        1.63
+#> # ℹ 1,711 more rows
+#> # ℹ 4 more variables: MeanRatio.rank <int>, MeanRatio.anno <chr>,
+#> #   gene_ensembl <chr>, gene_name <chr>
+
+## genes with the highest MeanRatio are the best marker genes for each cell type
+mean_ratio_marker_stats |>
+    filter(MeanRatio.rank == 1)
+#> # A tibble: 7 × 10
+#>   gene       cellType.target mean.target cellType.2nd mean.2nd MeanRatio
+#>   <chr>      <fct>                 <dbl> <fct>           <dbl>     <dbl>
+#> 1 LYPD6B     Inhib                  1.20 Excit          0.0967     12.4 
+#> 2 AC012494.1 Oligo                  2.37 OPC            0.147      16.1 
+#> 3 MIR3681HG  OPC                    1.58 Excit          0.205       7.69
+#> 4 AC011995.2 Excit                  1.01 Inhib          0.135       7.51
+#> 5 PRDM16     Astro                  1.97 EndoMural      0.142      13.9 
+#> 6 SLC2A1     EndoMural              1.49 Astro          0.146      10.2 
+#> 7 LINC01141  Micro                  1.57 Excit          0.0640     24.5 
+#> # ℹ 4 more variables: MeanRatio.rank <int>, MeanRatio.anno <chr>,
+#> #   gene_ensembl <chr>, gene_name <chr>

+

+

+
4. Find 1vALL marker genes
+

+
To further explore cell type marker genes it can be helpful to also
+calculate the 1vALL stats for the dataset. To help with this we have
+included the function DeconvoBuddies::findMarkers_1vALL,
+which is a wrapper for scran::findMarkers() that iterates
+through cell types and creates an table output in a compatible with the
+output get_mean_ratio().

+
Similarity

+
Note this function can take a bit of time to run.

+
+
+marker_stats_1vAll <- findMarkers_1vAll(
+ sce = sce, # sce is the SingleCellExperiment with our data
+ assay_name = "counts",
+ cellType_col = "cellType_broad_hc", # column in colData with cell type info
+ mod = "~BrNum" # Control for donor stored in "BrNum" with mod
+)
+#> 2024-08-12 15:11:00.718289 - Find markers for: Inhib
+#> 2024-08-12 15:12:35.089049 - Find markers for: Oligo
+#> 2024-08-12 15:14:09.079623 - Find markers for: OPC
+#> 2024-08-12 15:15:43.553514 - Find markers for: Excit
+#> 2024-08-12 15:17:17.52289 - Find markers for: Astro
+#> 2024-08-12 15:18:52.162451 - Find markers for: EndoMural
+#> 2024-08-12 15:20:26.036891 - Find markers for: Micro
+#> Building Table - 2024-08-12 15:21:59.434288
+#> ** Done! **

+
The function findMarkers_1vALL() returns a
+tibble with the following columns:

+
    
+
  • 
+gene is the name of the gene (from
+rownames(sce)).
    • 
+
  • 
+logFC the log fold change from the DE test
    • 
+
  • 
+log.p.value the log of the p-value of the DE test
    • 
+
  • 
+log.FDR the log of the False Discovery Rate adjusted
+p.value
    • 
+
  • 
+std.logFC the standard logFC
    • 
+
  • 
+cellType.target the cell type we’re finding marker
+genes for
    • 
+
  • 
+std.logFC.rank the rank of std.logFC for
+each cell type
    • 
+
  • 
+std.logFC.anno is an annotation of the
+std.logFC value helpful for plotting.
    • 
+

+
+## Explore the tibble output
+marker_stats_1vAll
+#> # A tibble: 35,000 × 8
+#> # Groups:   cellType.target [7]
+#>    gene    logFC log.p.value log.FDR std.logFC cellType.target std.logFC.rank
+#>    <chr>   <dbl>       <dbl>   <dbl>     <dbl> <fct>                    <int>
+#>  1 SILC1   2.77       -6747.  -6739.      1.42 Inhib                        1
+#>  2 LYPD6B  2.48       -6338.  -6330.      1.37 Inhib                        2
+#>  3 LYPD6   2.18       -6147.  -6140.      1.35 Inhib                        3
+#>  4 ALK     4.56       -5707.  -5700.      1.30 Inhib                        4
+#>  5 WLS     3.26       -5111.  -5104.      1.22 Inhib                        5
+#>  6 PLD5    5.85       -4711.  -4704.      1.17 Inhib                        6
+#>  7 KIRREL1 1.04       -4539.  -4532.      1.14 Inhib                        7
+#>  8 TRIM67  0.622      -4326.  -4319.      1.11 Inhib                        8
+#>  9 GREM2   2.11       -4048.  -4042.      1.07 Inhib                        9
+#> 10 IGSF3   1.27       -3783.  -3777.      1.04 Inhib                       10
+#> # ℹ 34,990 more rows
+#> # ℹ 1 more variable: std.logFC.anno <chr>
+
+## genes with the highest MeanRatio are the best marker genes for each cell type
+marker_stats_1vAll |>
+    filter(std.logFC.rank == 1)
+#> # A tibble: 7 × 8
+#> # Groups:   cellType.target [7]
+#>   gene       logFC log.p.value log.FDR std.logFC cellType.target std.logFC.rank
+#>   <chr>      <dbl>       <dbl>   <dbl>     <dbl> <fct>                    <int>
+#> 1 SILC1      2.77       -6747.  -6739.      1.42 Inhib                        1
+#> 2 RNF220    10.7        -8373.  -8365.      1.52 Oligo                        1
+#> 3 BX284613   7.42      -12763. -12754.      4.20 OPC                          1
+#> 4 KIAA1211L  7.89       -8371.  -8362.      1.23 Excit                        1
+#> 5 PRDM16     1.90       -8853.  -8844.      2.38 Astro                        1
+#> 6 EPAS1      6.50       -9480.  -9471.      3.31 EndoMural                    1
+#> 7 CSF3R      0.808      -9670.  -9661.      3.86 Micro                        1
+#> # ℹ 1 more variable: std.logFC.anno <chr>

+

+

+
5. Compare marker gene selection
+

+
Let’s join the

+

+

+
6. Visualize marker genes expression with
+

 

 

 
Reproducibility
@@ -242,7 +490,7 @@ 
Reproducibility
 
This package was developed using biocthis.

 
Code for creating the vignette

-
+
 ## Create the vignette
 library("rmarkdown")
 system.time(render("Marker_Finding.Rmd", "BiocStyle::html_document"))
@@ -251,9 +499,9 @@ 
Reproducibilitylibrary("knitr")
 knit("Marker_Finding.Rmd", tangle = TRUE)

 
Date the vignette was generated.

-
#> [1] "2024-08-07 17:35:41 UTC"

+
#> [1] "2024-08-12 15:21:59 UTC"

 
Wallclock time spent generating the vignette.

-
#> Time difference of 27.362 secs

+
#> Time difference of 11.842 mins

 
R session information.

 
#> ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────
 #>  setting  value
@@ -265,8 +513,8 @@ 
Reproducibility#>  collate  en_US.UTF-8
 #>  ctype    en_US.UTF-8
 #>  tz       UTC
-#>  date     2024-08-07
-#>  pandoc   3.2 @ /usr/bin/ (via rmarkdown)
+#>  date     2024-08-12
+#>  pandoc   3.3 @ /usr/bin/ (via rmarkdown)
 #> 
 #> ─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────
 #>  package              * version    date (UTC) lib source
@@ -310,7 +558,7 @@ 
Reproducibility#>  data.table             1.15.4     2024-03-30 [1] RSPM (R 4.4.0)
 #>  DBI                    1.2.3      2024-06-02 [1] RSPM (R 4.4.0)
 #>  dbplyr                 2.5.0      2024-03-19 [1] RSPM (R 4.4.0)
-#>  DeconvoBuddies       * 0.99.0     2024-08-07 [1] Bioconductor
+#>  DeconvoBuddies       * 0.99.0     2024-08-12 [1] Bioconductor
 #>  DelayedArray           0.31.11    2024-08-04 [1] Bioconductor 3.20 (R 4.4.1)
 #>  desc                   1.4.3      2023-12-10 [2] RSPM (R 4.4.0)
 #>  digest                 0.6.36     2024-06-23 [2] RSPM (R 4.4.0)
@@ -334,7 +582,7 @@ 
Reproducibility#>  GenomicAlignments      1.41.0     2024-05-01 [1] Bioconductor 3.20 (R 4.4.0)
 #>  GenomicRanges        * 1.57.1     2024-06-12 [1] Bioconductor 3.20 (R 4.4.0)
 #>  ggbeeswarm             0.7.2      2023-04-29 [1] RSPM (R 4.4.0)
-#>  ggplot2              * 3.5.1      2024-04-23 [1] RSPM (R 4.4.0)
+#>  ggplot2                3.5.1      2024-04-23 [1] RSPM (R 4.4.0)
 #>  ggrepel                0.9.5      2024-01-10 [1] RSPM (R 4.4.0)
 #>  glue                   1.7.0      2024-01-09 [2] RSPM (R 4.4.0)
 #>  golem                  0.4.1      2023-06-05 [1] RSPM (R 4.4.0)
@@ -373,7 +621,7 @@ 
Reproducibility#>  paletteer              1.6.0      2024-01-21 [1] RSPM (R 4.4.0)
 #>  pillar                 1.9.0      2023-03-22 [2] RSPM (R 4.4.0)
 #>  pkgconfig              2.0.3      2019-09-22 [2] RSPM (R 4.4.0)
-#>  pkgdown                2.1.0.9000 2024-08-05 [1] Github (r-lib/pkgdown@1d40a80)
+#>  pkgdown                2.1.0.9000 2024-08-12 [1] Github (r-lib/pkgdown@1d40a80)
 #>  plotly                 4.10.4     2024-01-13 [1] RSPM (R 4.4.0)
 #>  plyr                   1.8.9      2023-10-02 [1] RSPM (R 4.4.0)
 #>  png                    0.1-8      2022-11-29 [1] RSPM (R 4.4.0)
@@ -421,8 +669,8 @@ 
Reproducibility#>  SummarizedExperiment * 1.35.1     2024-06-28 [1] Bioconductor 3.20 (R 4.4.1)
 #>  systemfonts            1.1.0      2024-05-15 [2] RSPM (R 4.4.0)
 #>  textshaping            0.4.0      2024-05-24 [2] RSPM (R 4.4.0)
-#>  tibble               * 3.2.1      2023-03-20 [2] RSPM (R 4.4.0)
-#>  tidyr                * 1.3.1      2024-01-24 [1] RSPM (R 4.4.0)
+#>  tibble                 3.2.1      2023-03-20 [2] RSPM (R 4.4.0)
+#>  tidyr                  1.3.1      2024-01-24 [1] RSPM (R 4.4.0)
 #>  tidyselect             1.2.1      2024-03-11 [1] RSPM (R 4.4.0)
 #>  timechange             0.3.0      2024-01-18 [1] RSPM (R 4.4.0)
 #>  UCSC.utils             1.1.0      2024-05-01 [1] Bioconductor 3.20 (R 4.4.0)
diff --git a/pkgdown.yml b/pkgdown.yml
index 69bd4aa..9c8847f 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -1,8 +1,8 @@
-pandoc: '3.2'
+pandoc: '3.3'
 pkgdown: 2.1.0.9000
 pkgdown_sha: 1d40a80e6b3564a6d7da0ce467b0a4570aa5665e
 articles:
   DeconvoBuddies: DeconvoBuddies.html
   Deconvolution_Benchmark_DLPFC: Deconvolution_Benchmark_DLPFC.html
   Marker_Finding: Marker_Finding.html
-last_built: 2024-08-07T17:32Z
+last_built: 2024-08-12T15:07Z
diff --git a/reference/dot-get_mean_ratio2.html b/reference/dot-get_mean_ratio2.html
index a2ba3ce..4b8a2dd 100644
--- a/reference/dot-get_mean_ratio2.html
+++ b/reference/dot-get_mean_ratio2.html
@@ -117,7 +117,7 @@ 
Details

     
Examples

     
#' ## load example SingleCellExperiment
 if (!exists("sce_DLPFC_example")) sce_DLPFC_example <- fetch_deconvo_data("sce_DLPFC_example")
-#> 2024-08-07 17:32:53.195728 loading file /github/home/.cache/R/BiocFileCache/4a1ec11882_sce_DLPFC_example.Rdata%3Frlkey%3Dv3z4u8ru0d2y12zgdl1az07q9%26st%3D1dcfqc1i%26dl%3D1
+#> 2024-08-12 15:07:39.587022 loading file /github/home/.cache/R/BiocFileCache/146b342eafd5_sce_DLPFC_example.Rdata%3Frlkey%3Dv3z4u8ru0d2y12zgdl1az07q9%26st%3D1dcfqc1i%26dl%3D1
 
 ## Get the mean ratio for each gene for each cell type defined in `cellType_broad_hc`
 .get_mean_ratio2(sce_DLPFC_example, cellType_col = "cellType_broad_hc")
diff --git a/reference/fetch_deconvo_data.html b/reference/fetch_deconvo_data.html
index 9eb8fbc..806519d 100644
--- a/reference/fetch_deconvo_data.html
+++ b/reference/fetch_deconvo_data.html
@@ -132,9 +132,9 @@ 
Examples

 ## A RangedSummarizedExperiment (41.16 MB)
 
 if (!exists("rse-gene")) rse_gene <- fetch_deconvo_data("rse_gene")
-#> 2024-08-07 17:32:55.846502 loading file /github/home/.cache/R/BiocFileCache/4a1de5efa9_rse_gene.Rdata%3Frlkey%3Dsw2djr71y954yw4o3xrmjv59b%26dl%3D1
+#> 2024-08-12 15:07:42.170264 loading file /github/home/.cache/R/BiocFileCache/146b2dbc692f_rse_gene.Rdata%3Frlkey%3Dsw2djr71y954yw4o3xrmjv59b%26dl%3D1
 
-## explore data
+## explore bulk data
 rse_gene
 #> class: RangedSummarizedExperiment 
 #> dim: 21745 110 
@@ -147,22 +147,13 @@ 
Examples

 #>   2107UNHS-0291_Br2720_Mid_Cyto ... AN00000906_Br8667_Mid_Cyto
 #>   AN00000906_Br8667_Mid_Nuc
 #> colData names(80): SAMPLE_ID Sample ... diagnosis qc_class
-# class: RangedSummarizedExperiment
-# dim: 21745 110
-# metadata(1): SPEAQeasy_settings
-# assays(2): counts logcounts
-# rownames(21745): ENSG00000227232.5 ENSG00000278267.1 ... ENSG00000210195.2 ENSG00000210196.2
-# rowData names(11): Length gencodeID ... gencodeTx passExprsCut
-# colnames(110): 2107UNHS-0291_Br2720_Mid_Bulk 2107UNHS-0291_Br2720_Mid_Cyto ... AN00000906_Br8667_Mid_Cyto
-# AN00000906_Br8667_Mid_Nuc
-# colData names(78): SAMPLE_ID Sample ... diagnosis qc_class
 
 ## load example snRNA-seq data
 ## A SingleCellExperiment (4.79 MB)
 if (!exists("sce_DLPFC_example")) sce_DLPFC_example <- fetch_deconvo_data("sce_DLPFC_example")
-#> 2024-08-07 17:32:57.693436 loading file /github/home/.cache/R/BiocFileCache/4a1ec11882_sce_DLPFC_example.Rdata%3Frlkey%3Dv3z4u8ru0d2y12zgdl1az07q9%26st%3D1dcfqc1i%26dl%3D1
-
+#> 2024-08-12 15:07:43.289595 loading file /github/home/.cache/R/BiocFileCache/146b342eafd5_sce_DLPFC_example.Rdata%3Frlkey%3Dv3z4u8ru0d2y12zgdl1az07q9%26st%3D1dcfqc1i%26dl%3D1
 
+## explore example sce data
 sce_DLPFC_example
 #> class: SingleCellExperiment 
 #> dim: 557 10000 
@@ -176,17 +167,6 @@ 
Examples

 #> reducedDimNames(0):
 #> mainExpName: NULL
 #> altExpNames(0):
-# class: SingleCellExperiment
-# dim: 557 10000
-# metadata(3): Samples cell_type_colors cell_type_colors_broad
-# assays(2): counts logcounts
-# rownames(557): GABRD PRDM16 ... AFF2 MAMLD1
-# rowData names(7): source type ... gene_type binomial_deviance
-# colnames(10000): 16_CGAAGTTTCGCACGAC-1 11_TTGGGATCAACCGCTG-1 ... 8_CGCATAAGTTAAACCC-1 16_AGCTACATCCCGAGAC-1
-# colData names(32): Sample Barcode ... cellType_layer layer_annotation
-# reducedDimNames(0):
-#   mainExpName: NULL
-# altExpNames(0):
 
 ## check the logcounts
 SingleCellExperiment::logcounts(sce_DLPFC_example)[1:5, 1:5]
@@ -204,6 +184,7 @@ 
Examples

 #> ADGRB2                 2.253454             0.0000000
 
 if (FALSE) { # \dontrun{
+## download the full sce experiment object
 sce_path_zip <- fetch_deconvo_data("sce")
 sce_path <- unzip(sce_path_zip, exdir = tempdir())
 sce <- HDF5Array::loadHDF5SummarizedExperiment(
diff --git a/reference/findMarkers_1vAll.html b/reference/findMarkers_1vAll.html
index cb26561..14cec90 100644
--- a/reference/findMarkers_1vAll.html
+++ b/reference/findMarkers_1vAll.html
@@ -76,7 +76,7 @@ 
Calculate 1 vs. All standard fold change for each gene x cell type, wrapper
   assay_name = "counts",
   cellType_col = "cellType",
   add_symbol = FALSE,
-  mod = "~donor",
+  mod = NULL,
   verbose = TRUE
 )

     

@@ -102,7 +102,9 @@ 
Arguments

 
 
 
mod

-
String specifying the model used as design in findMarkers. Can be NULL if there are no blocking terms with uninteresting factors as documented at pairwiseTTests.

+
String specifying the model used as design in findMarkers.
+Can be NULL (default) if there are no blocking terms with uninteresting
+factors as documented at pairwiseTTests.

 
 
 
verbose

@@ -111,28 +113,88 @@ 
Arguments

 

     

     
Value

-    
Table of 1 vs. ALL std log fold change + p-values for each gene x cell type

-    

+    
Tibble of 1 vs. ALL std log fold change + p-values for each gene x cell type
  • gene is the name of the gene (from rownames(sce)).
    • 
+
  • logFC the log fold change from the DE test
    • 
+
  • log.p.value the log of the p-value of the DE test
    • 
+
  • log.FDR the log of the False Discovery Rate adjusted p.value
    • 
+
  • std.logFC the standard logFC
    • 
+
  • cellType.target the cell type we're finding marker genes for
    • 
+
  • std.logFC.rank the rank of std.logFC for each cell type
    • 
+
  • std.logFC.anno is an annotation of the std.logFC value
+helpful for plotting.
    • 
+

 
     

     
Examples

-    
markers_1vAll <- findMarkers_1vAll(sce_ab)
-#> A - '2024-08-07 17:32:58.690142
-#> B - '2024-08-07 17:32:59.008849
-#> Building Table - 2024-08-07 17:32:59.164719
+    
## load example SingleCellExperiment
+if (!exists("sce_DLPFC_example")) sce_DLPFC_example <- fetch_deconvo_data("sce_DLPFC_example")
+#> 2024-08-12 15:07:45.774661 loading file /github/home/.cache/R/BiocFileCache/146b342eafd5_sce_DLPFC_example.Rdata%3Frlkey%3Dv3z4u8ru0d2y12zgdl1az07q9%26st%3D1dcfqc1i%26dl%3D1
+## Explore properties of the sce object
+sce_DLPFC_example
+#> class: SingleCellExperiment 
+#> dim: 557 10000 
+#> metadata(3): Samples cell_type_colors cell_type_colors_broad
+#> assays(1): logcounts
+#> rownames(557): GABRD PRDM16 ... AFF2 MAMLD1
+#> rowData names(7): source type ... gene_type binomial_deviance
+#> colnames(10000): 8_AGTGACTGTAGTTACC-1 17_GCAGCCAGTGAGTCAG-1 ...
+#>   12_GGACGTCTCTGACAGT-1 1_GGTTAACTCTCTCTAA-1
+#> colData names(32): Sample Barcode ... cellType_layer layer_annotation
+#> reducedDimNames(0):
+#> mainExpName: NULL
+#> altExpNames(0):
+
+## this data contains logcounts of gene expression
+SummarizedExperiment::assays(sce_DLPFC_example)$logcounts[1:5, 1:5]
+#>           8_AGTGACTGTAGTTACC-1 17_GCAGCCAGTGAGTCAG-1 3_CTGGACGAGCTTCATG-1
+#> GABRD                        0             0.9249246             0.000000
+#> PRDM16                       0             0.0000000             0.000000
+#> MICOS10                      0             0.0000000             0.000000
+#> LINC01141                    0             0.0000000             0.000000
+#> ADGRB2                       0             0.9249246             2.253612
+#>           13_CCCTCAAAGTCTAGCT-1 11_TGTAAGCCATTCTGTT-1
+#> GABRD                  0.000000             0.0000000
+#> PRDM16                 0.000000             0.0000000
+#> MICOS10                0.000000             0.6528615
+#> LINC01141              0.000000             0.0000000
+#> ADGRB2                 2.253454             0.0000000
+
+## nuclei are classified in to cell types
+table(sce_DLPFC_example$cellType_broad_hc)
+#> 
+#>     Astro EndoMural     Micro     Oligo       OPC     Excit     Inhib 
+#>       692       417       316      1970       350      4335      1920 
+
+## Get the 1vALL stats for each gene for each cell type defined in `cellType_broad_hc`
+marker_stats_1vAll <- findMarkers_1vAll(
+ sce = sce_DLPFC_example,
+ assay_name = "logcounts",
+ cellType_col = "cellType_broad_hc",
+ mod = "~BrNum"
+)
+#> 2024-08-12 15:07:46.02474 - Find markers for: Oligo
+#> 2024-08-12 15:07:46.488802 - Find markers for: Excit
+#> 2024-08-12 15:07:46.874934 - Find markers for: Inhib
+#> 2024-08-12 15:07:47.995957 - Find markers for: Micro
+#> 2024-08-12 15:07:48.331533 - Find markers for: OPC
+#> 2024-08-12 15:07:48.667361 - Find markers for: EndoMural
+#> 2024-08-12 15:07:49.036291 - Find markers for: Astro
+#> Building Table - 2024-08-12 15:07:49.415034
 #> ** Done! **
-head(markers_1vAll)
+
+## explore output, top markers have high logFC
+head(marker_stats_1vAll)
 #> # A tibble: 6 × 8
-#> # Groups:   cellType.target [2]
-#>   gene   logFC log.p.value   log.FDR std.logFC cellType.target rank_marker
-#>   <chr>  <dbl>       <dbl>     <dbl>     <dbl> <chr>                 <int>
-#> 1 G-D1_A   0.5   -3.65e+ 1 -3.58e+ 1      1.97 A                         1
-#> 2 G-D2_A   0.5   -3.65e+ 1 -3.58e+ 1      1.97 A                         2
-#> 3 G-D1_B  -0.5   -1.42e-16 -1.42e-16     -1.97 A                         3
-#> 4 G-D2_B  -0.5   -1.42e-16 -1.42e-16     -1.97 A                         4
-#> 5 G-D2_B   0.5   -3.65e+ 1 -3.58e+ 1      1.97 B                         1
-#> 6 G-D1_B   0.5   -3.65e+ 1 -3.58e+ 1      1.97 B                         2
-#> # ℹ 1 more variable: anno_logFC <chr>
+#> # Groups:   cellType.target [1]
+#>   gene   logFC log.p.value log.FDR std.logFC cellType.target std.logFC.rank
+#>   <chr>  <dbl>       <dbl>   <dbl>     <dbl> <fct>                    <int>
+#> 1 ST18    4.46      -6697.  -6691.      4.32 Oligo                        1
+#> 2 PLP1    4.25      -5233.  -5227.      3.50 Oligo                        2
+#> 3 MOBP    3.18      -4821.  -4816.      3.28 Oligo                        3
+#> 4 ENPP2   2.72      -4521.  -4516.      3.12 Oligo                        4
+#> 5 TF      3.00      -4466.  -4462.      3.09 Oligo                        5
+#> 6 RNF220  3.80      -4390.  -4385.      3.05 Oligo                        6
+#> # ℹ 1 more variable: std.logFC.anno <chr>
 
 

     

diff --git a/reference/get_mean_ratio.html b/reference/get_mean_ratio.html
index e96dc51..c99630d 100644
--- a/reference/get_mean_ratio.html
+++ b/reference/get_mean_ratio.html
@@ -121,8 +121,10 @@ 
Value

 
  • mean.2nd is the mean expression of gene for cellType.2nd.
    • 
 
  • MeanRatio is the ratio of mean.target/mean.2nd.
    • 
 
  • MeanRatio.rank is the rank of MeanRatio for the cell type.
    • 
-
  • MeanRatio.anno is an annotation of the MeanRatio calculation helpful for plotting.
    • 
-
  • gene_ensembl & gene_name optional cols spcified by the user to add gene infomation
    • 
+
  • MeanRatio.anno is an annotation of the MeanRatio calculation helpful
+for plotting.
    • 
+
  • gene_ensembl & gene_name optional cols from rowData(sce)specified by
+the user to add gene information
    • 
 
    
     
    
     
    Details
    
@@ -134,7 +136,7 @@ 
    Details
    
     
    Examples
    
     
    ## load example SingleCellExperiment
 if (!exists("sce_DLPFC_example")) sce_DLPFC_example <- fetch_deconvo_data("sce_DLPFC_example")
-#> 2024-08-07 17:33:00.419412 loading file /github/home/.cache/R/BiocFileCache/4a1ec11882_sce_DLPFC_example.Rdata%3Frlkey%3Dv3z4u8ru0d2y12zgdl1az07q9%26st%3D1dcfqc1i%26dl%3D1
+#> 2024-08-12 15:07:50.863967 loading file /github/home/.cache/R/BiocFileCache/146b342eafd5_sce_DLPFC_example.Rdata%3Frlkey%3Dv3z4u8ru0d2y12zgdl1az07q9%26st%3D1dcfqc1i%26dl%3D1
 ## Explore properties of the sce object
 sce_DLPFC_example
 #> class: SingleCellExperiment 
@@ -221,7 +223,10 @@ 
    Examples
    
 #> MAMLD1    protein_coding           75492.1
 
 ## specify rowData col names for gene_name and gene_ensembl
-get_mean_ratio(sce_DLPFC_example, cellType_col = "cellType_broad_hc", gene_name = "gene_name", gene_ensembl = "gene_id")
+ get_mean_ratio(sce_DLPFC_example, 
+                cellType_col = "cellType_broad_hc", 
+                gene_name = "gene_name", 
+                gene_ensembl = "gene_id")
 #> # A tibble: 762 × 10
 #>    gene       cellType.target mean.target cellType.2nd mean.2nd MeanRatio
 #>    <chr>      <fct>                 <dbl> <fct>           <dbl>     <dbl>
diff --git a/reference/plot_gene_express.html b/reference/plot_gene_express.html
index 413056c..1b3d700 100644
--- a/reference/plot_gene_express.html
+++ b/reference/plot_gene_express.html
@@ -144,7 +144,7 @@ 
    Examples
    
 
 
 if (!exists("sce_DLPFC_example")) sce_DLPFC_example <- fetch_deconvo_data("sce_DLPFC_example")
-#> 2024-08-07 17:33:06.529501 loading file /github/home/.cache/R/BiocFileCache/4a1ec11882_sce_DLPFC_example.Rdata%3Frlkey%3Dv3z4u8ru0d2y12zgdl1az07q9%26st%3D1dcfqc1i%26dl%3D1
+#> 2024-08-12 15:07:56.587053 loading file /github/home/.cache/R/BiocFileCache/146b342eafd5_sce_DLPFC_example.Rdata%3Frlkey%3Dv3z4u8ru0d2y12zgdl1az07q9%26st%3D1dcfqc1i%26dl%3D1
 ## plot expression of two genes
 plot_gene_express(sce = sce_DLPFC_example, category = "cellType_broad_hc", genes = c("GAD2", "CD22"))
 #> No summary function supplied, defaulting to `mean_se()`
diff --git a/reference/plot_marker_express.html b/reference/plot_marker_express.html
index 9dc7169..dc08ee0 100644
--- a/reference/plot_marker_express.html
+++ b/reference/plot_marker_express.html
@@ -155,7 +155,7 @@ 
    See also
    
     
    
     
    Examples
    
     
    if (!exists("sce_DLPFC_example")) sce_DLPFC_example <- fetch_deconvo_data("sce_DLPFC_example")
-#> 2024-08-07 17:33:09.904147 loading file /github/home/.cache/R/BiocFileCache/4a1ec11882_sce_DLPFC_example.Rdata%3Frlkey%3Dv3z4u8ru0d2y12zgdl1az07q9%26st%3D1dcfqc1i%26dl%3D1
+#> 2024-08-12 15:07:59.949517 loading file /github/home/.cache/R/BiocFileCache/146b342eafd5_sce_DLPFC_example.Rdata%3Frlkey%3Dv3z4u8ru0d2y12zgdl1az07q9%26st%3D1dcfqc1i%26dl%3D1
 ## plot the top markers for Astrocytes in
 plot_marker_express(
     sce = sce_DLPFC_example,
diff --git a/reference/plot_marker_express_ALL.html b/reference/plot_marker_express_ALL.html
index 6749dc8..1e360ad 100644
--- a/reference/plot_marker_express_ALL.html
+++ b/reference/plot_marker_express_ALL.html
@@ -153,7 +153,7 @@ 
    See also
    
     
    Examples
    
     
    #' ## Fetch sce example data
 if (!exists("sce_DLPFC_example")) sce_DLPFC_example <- fetch_deconvo_data("sce_DLPFC_example")
-#> 2024-08-07 17:33:12.916615 loading file /github/home/.cache/R/BiocFileCache/4a1ec11882_sce_DLPFC_example.Rdata%3Frlkey%3Dv3z4u8ru0d2y12zgdl1az07q9%26st%3D1dcfqc1i%26dl%3D1
+#> 2024-08-12 15:08:02.934026 loading file /github/home/.cache/R/BiocFileCache/146b342eafd5_sce_DLPFC_example.Rdata%3Frlkey%3Dv3z4u8ru0d2y12zgdl1az07q9%26st%3D1dcfqc1i%26dl%3D1
 
 # Plot marker gene expression to PDF, one page per cell type in stats
 pdf_file <- tempfile("test_marker_expression_ALL", fileext = ".pdf")
diff --git a/reference/plot_marker_express_List.html b/reference/plot_marker_express_List.html
index afdac08..1ce1204 100644
--- a/reference/plot_marker_express_List.html
+++ b/reference/plot_marker_express_List.html
@@ -134,7 +134,7 @@ 
    See also
    
     
    Examples
    
     
    ## Fetch sce example data
 if (!exists("sce_DLPFC_example")) sce_DLPFC_example <- fetch_deconvo_data("sce_DLPFC_example")
-#> 2024-08-07 17:33:29.315545 loading file /github/home/.cache/R/BiocFileCache/4a1ec11882_sce_DLPFC_example.Rdata%3Frlkey%3Dv3z4u8ru0d2y12zgdl1az07q9%26st%3D1dcfqc1i%26dl%3D1
+#> 2024-08-12 15:08:19.448584 loading file /github/home/.cache/R/BiocFileCache/146b342eafd5_sce_DLPFC_example.Rdata%3Frlkey%3Dv3z4u8ru0d2y12zgdl1az07q9%26st%3D1dcfqc1i%26dl%3D1
 
 ## Create list-of-lists of genes to plot, names of sub-list become title of page
 my_gene_list <- list(Inhib = c("GAD2", "SAMD5"), Astro = c("RGS20", "PRDM16"))