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stemangiolastemangiola
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update code
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‎README.Rmd‎

Lines changed: 12 additions & 10 deletions
Original file line numberDiff line numberDiff line change
@@ -56,13 +56,15 @@ library(stringr)
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### Load the metadata
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```{r}
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get_metadata()
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metadata = get_metadata()
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metadata
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```
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### Explore the number of datasets per tissue
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```{r}
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get_metadata() |>
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metadata |>
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dplyr::distinct(tissue, dataset_id) |>
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dplyr::count(tissue)
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```
@@ -76,7 +78,7 @@ get_metadata() |>
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```{r}
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single_cell_counts =
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get_metadata() |>
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metadata |>
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dplyr::filter(
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ethnicity == "African" &
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stringr::str_like(assay, "%10x%") &
@@ -94,7 +96,7 @@ This is helpful if just few genes are of interest, as they can be compared acros
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```{r}
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single_cell_counts =
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get_metadata() |>
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metadata |>
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dplyr::filter(
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ethnicity == "African" &
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stringr::str_like(assay, "%10x%") &
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```{r}
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single_cell_counts =
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get_metadata() |>
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metadata |>
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dplyr::filter(
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ethnicity == "African" &
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stringr::str_like(assay, "%10x%") &
@@ -129,7 +131,7 @@ This convert the H5 SingleCellExperiment to Seurat so it might take long time an
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```{r}
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single_cell_counts =
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get_metadata() |>
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metadata |>
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dplyr::filter(
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ethnicity == "African" &
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stringr::str_like(assay, "%10x%") &
@@ -152,7 +154,7 @@ library(ggplot2)
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# Plots with styling
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# Plot by disease
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get_metadata() |>
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metadata |>
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# Filter and subset
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filter(cell_type_harmonised=="cd14 mono") |>
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filter(file_id_db != "c5a05f23f9784a3be3bfa651198a48eb") |>
@@ -180,7 +182,7 @@ get_metadata() |>
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ggtitle("HLA-A in CD14 monocytes by disease")
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# Plot by tissue
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get_metadata() |>
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metadata |>
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# Filter and subset
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filter(cell_type_harmonised=="cd14 mono") |>
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filter(file_id_db != "c5a05f23f9784a3be3bfa651198a48eb") |>
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library(tidySingleCellExperiment)
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library(ggplot2)
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get_metadata() |>
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metadata |>
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# Filter and subset
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filter(cell_type_harmonised=="cd14 mono") |>
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@@ -232,7 +234,7 @@ knitr::include_graphics("man/figures/HLA_A_disease_plot.png")
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```{r, eval=FALSE}
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get_metadata() |>
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metadata |>
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# Filter and subset
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filter(cell_type_harmonised=="nk") |>

‎README.md‎

Lines changed: 12 additions & 11 deletions
Original file line numberDiff line numberDiff line change
@@ -38,7 +38,9 @@ library(stringr)
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### Load the metadata
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``` r
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get_metadata()
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metadata = get_metadata()
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metadata
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#> # Source: table</stornext/Home/data/allstaff/m/mangiola.s/.cache/R/CuratedAtlasQueryR/metadata.0.2.2.parquet> [?? x 56]
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#> # Database: DuckDB 0.7.0 [unknown@Linux 3.10.0-1160.81.1.el7.x86_64:R 4.2.0/:memory:]
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#> `_cell` _samp…¹ cell_…² cell_…³ confi…⁴ cell_…⁵ cell_…⁶ cell_…⁷ sampl…⁸
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### Explore the number of datasets per tissue
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``` r
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get_metadata() |>
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metadata |>
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dplyr::distinct(tissue, dataset_id) |>
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dplyr::count(tissue)
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#> # Source: SQL [?? x 2]
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#> 8 heart right ventricle 16
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#> 9 left cardiac atrium 7
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#> 10 interventricular septum 16
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#> # … with more rows
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```
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``` r
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single_cell_counts =
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get_metadata() |>
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metadata |>
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dplyr::filter(
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ethnicity == "African" &
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stringr::str_like(assay, "%10x%") &
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``` r
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single_cell_counts =
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get_metadata() |>
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metadata |>
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dplyr::filter(
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ethnicity == "African" &
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stringr::str_like(assay, "%10x%") &
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``` r
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single_cell_counts =
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get_metadata() |>
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metadata |>
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dplyr::filter(
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ethnicity == "African" &
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stringr::str_like(assay, "%10x%") &
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``` r
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single_cell_counts =
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get_metadata() |>
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metadata |>
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dplyr::filter(
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ethnicity == "African" &
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stringr::str_like(assay, "%10x%") &
@@ -226,7 +227,7 @@ We can gather all natural killer cells and plot the distribution of CD56
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library(tidySingleCellExperiment)
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library(ggplot2)
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get_metadata() |>
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metadata |>
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# Filter and subset
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filter(cell_type_harmonised=="cd14 mono") |>
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geom_jitter(shape=".")
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```
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<img src="man/figures/HLA_A_disease_plot.png" width="497" />
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<img src="man/figures/HLA_A_disease_plot.png" width="525" />
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``` r
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get_metadata() |>
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metadata |>
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# Filter and subset
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filter(cell_type_harmonised=="nk") |>
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geom_jitter(shape=".")
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```
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<img src="man/figures/HLA_A_tissue_plot.png" width="499" />
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<img src="man/figures/HLA_A_tissue_plot.png" width="525" />
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# Cell metadata
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‎vignettes/Introduction.Rmd‎

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Original file line numberDiff line numberDiff line change
@@ -57,15 +57,15 @@ library(stringr)
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### Load the metadata
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```{r}
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meta <- get_metadata()
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metadata <- get_metadata()
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```
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The `meta` variable can then be re-used for all subsequent queries.
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The `metadata` variable can then be re-used for all subsequent queries.
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### Explore the tissue
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```{r, eval=FALSE}
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meta |>
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metadata |>
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dplyr::distinct(tissue, file_id)
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```
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```{r}
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single_cell_counts =
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meta |>
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metadata |>
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dplyr::filter(
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ethnicity == "African" &
102102
stringr::str_like(assay, "%10x%") &
@@ -114,7 +114,7 @@ This is helpful if just few genes are of interest, as they can be compared acros
114114

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```{r}
116116
single_cell_counts =
117-
meta |>
117+
metadata |>
118118
dplyr::filter(
119119
ethnicity == "African" &
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stringr::str_like(assay, "%10x%") &
@@ -130,7 +130,7 @@ single_cell_counts
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```{r}
132132
single_cell_counts =
133-
meta |>
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metadata |>
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dplyr::filter(
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ethnicity == "African" &
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stringr::str_like(assay, "%10x%") &
@@ -149,7 +149,7 @@ This convert the H5 SingleCellExperiment to Seurat so it might take long time an
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150150
```{r}
151151
single_cell_counts =
152-
meta |>
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metadata |>
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dplyr::filter(
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ethnicity == "African" &
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stringr::str_like(assay, "%10x%") &
@@ -172,7 +172,7 @@ library(ggplot2)
172172
# Plots with styling
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# Plot by disease
175-
get_metadata() |>
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metadata |>
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# Filter and subset
177177
filter(cell_type_harmonised=="cd14 mono") |>
178178
filter(file_id_db != "c5a05f23f9784a3be3bfa651198a48eb") |>
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200200
ggtitle("HLA-A in CD14 monocytes by disease")
201201
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# Plot by tissue
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get_metadata() |>
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metadata |>
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# Filter and subset
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filter(cell_type_harmonised=="cd14 mono") |>
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filter(file_id_db != "c5a05f23f9784a3be3bfa651198a48eb") |>
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library(tidySingleCellExperiment)
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library(ggplot2)
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get_metadata() |>
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metadata |>
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# Filter and subset
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filter(cell_type_harmonised=="cd14 mono") |>
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@@ -252,7 +252,7 @@ knitr::include_graphics("../man/figures/HLA_A_disease_plot.png")
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253253
```{r, eval=FALSE}
254254
255-
get_metadata() |>
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metadata |>
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# Filter and subset
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filter(cell_type_harmonised=="nk") |>

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