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raysinensis committed Aug 29, 2024
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28 changes: 16 additions & 12 deletions README.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -83,44 +83,48 @@ plot_best_call(

```{r example_seurat, warning=F, message=F}
# for SingleCellExperiment
sce_small <- sce_pbmc()
clustify(
input = sce_small, # an SCE object
ref_mat = cbmc_ref, # matrix of RNA-seq expression data for each cell type
cluster_col = "cell_type1", # name of column in meta.data containing cell clusters
cluster_col = "cell_type", # name of column in meta.data containing cell clusters
obj_out = TRUE # output SCE object with cell type inserted as "type" column
)
# for Seurat
library(Seurat)
# for Seurat3/4
s_small <- so_pbmc()
clustify(
input = s_small3,
cluster_col = "RNA_snn_res.1",
input = s_small,
cluster_col = "RNA_snn_res.0.5",
ref_mat = cbmc_ref,
seurat_out = TRUE
)
# New output option, directly as a vector (in the order of the metadata), which can then be inserted into metadata dataframes and other workflows
clustify(
input = s_small3,
cluster_col = "RNA_snn_res.1",
input = s_small,
cluster_col = "RNA_snn_res.0.5",
ref_mat = cbmc_ref,
vec_out = TRUE
)
)[1:10]
```

New reference matrix can be made directly from `SingleCellExperiment` and `Seurat` objects as well. Other scRNAseq experiment object types are supported as well.

```{r example_ref_matrix}
# make reference from SingleCellExperiment objects
sce_small <- sce_pbmc()
sce_ref <- object_ref(
input = sce_small, # SCE object
cluster_col = "cell_type1" # name of column in colData containing cell identities
cluster_col = "cell_type" # name of column in colData containing cell identities
)
# make reference from seurat objects
s_small <- so_pbmc()
s_ref <- seurat_ref(
seurat_object = s_small3,
cluster_col = "RNA_snn_res.1"
seurat_object = s_small,
cluster_col = "RNA_snn_res.0.5"
)
head(s_ref)
Expand All @@ -138,10 +142,10 @@ clustify_lists(
)
clustify_lists(
input = s_small3,
input = s_small,
marker = pbmc_markers,
marker_inmatrix = FALSE,
cluster_col = "RNA_snn_res.1",
cluster_col = "RNA_snn_res.0.5",
seurat_out = TRUE
)
```
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146 changes: 72 additions & 74 deletions README.md
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Expand Up @@ -8,7 +8,7 @@
coverage](https://codecov.io/gh/rnabioco/clustifyr/branch/devel/graph/badge.svg)](https://app.codecov.io/gh/rnabioco/clustifyr?branch=devel)
[![platforms](https://bioconductor.org/shields/availability/release/clustifyr.svg)](https://bioconductor.org/packages/release/bioc/html/clustifyr.html)
[![bioc](https://bioconductor.org/shields/years-in-bioc/clustifyr.svg)](https://bioconductor.org/packages/release/bioc/html/clustifyr.html)
[![\#downloads](https://img.shields.io/badge/%23%20downloads-8045-brightgreen)](https://bioconductor.org/packages/stats/bioc/clustifyr/clustifyr_stats.tab)
[![\#downloads](https://img.shields.io/badge/%23%20downloads-11608-brightgreen)](https://bioconductor.org/packages/stats/bioc/clustifyr/clustifyr_stats.tab)
<!-- badges: end -->

clustifyr classifies cells and clusters in single-cell RNA sequencing
Expand Down Expand Up @@ -51,10 +51,10 @@ library(clustifyr)

# calculate correlation
res <- clustify(
input = pbmc_matrix_small,
metadata = pbmc_meta$classified,
ref_mat = cbmc_ref,
query_genes = pbmc_vargenes
input = pbmc_matrix_small,
metadata = pbmc_meta$classified,
ref_mat = cbmc_ref,
query_genes = pbmc_vargenes
)

# print assignments
Expand All @@ -75,9 +75,9 @@ cor_to_call(res)

# plot assignments on a projection
plot_best_call(
cor_mat = res,
metadata = pbmc_meta,
cluster_col = "classified"
cor_mat = res,
metadata = pbmc_meta,
cluster_col = "classified"
)
```

Expand All @@ -88,61 +88,50 @@ object (both v2 and v3) and assign identities.

``` r
# for SingleCellExperiment
sce_small <- sce_pbmc()
clustify(
input = sce_small, # an SCE object
ref_mat = cbmc_ref, # matrix of RNA-seq expression data for each cell type
cluster_col = "cell_type1", # name of column in meta.data containing cell clusters
obj_out = TRUE # output SCE object with cell type inserted as "type" column
)
input = sce_small, # an SCE object
ref_mat = cbmc_ref, # matrix of RNA-seq expression data for each cell type
cluster_col = "cell_type", # name of column in meta.data containing cell clusters
obj_out = TRUE # output SCE object with cell type inserted as "type" column
)
#> class: SingleCellExperiment
#> dim: 200 200
#> dim: 2000 2638
#> metadata(0):
#> assays(2): counts logcounts
#> rownames(200): SGIP1 AZIN2 ... TAF12 SNHG3
#> rowData names(10): feature_symbol is_feature_control ... total_counts
#> log10_total_counts
#> colnames(200): AZ_A1 AZ_A10 ... HP1502401_E18 HP1502401_E19
#> colData names(35): cell_quality cell_type1 ... type r
#> reducedDimNames(0):
#> rownames(2000): PPBP LYZ ... CLIC2 HEMGN
#> rowData names(0):
#> colnames(2638): AAACATACAACCAC AAACATTGAGCTAC ... TTTGCATGAGAGGC
#> TTTGCATGCCTCAC
#> colData names(8): cell_source sum ... type r
#> reducedDimNames(1): UMAP
#> mainExpName: NULL
#> altExpNames(0):

# for Seurat
library(Seurat)
# for Seurat3/4
s_small <- so_pbmc()
clustify(
input = s_small3,
cluster_col = "RNA_snn_res.1",
ref_mat = cbmc_ref,
seurat_out = TRUE
input = s_small,
cluster_col = "RNA_snn_res.0.5",
ref_mat = cbmc_ref,
seurat_out = TRUE
)
#> An object of class Seurat
#> 230 features across 80 samples within 1 assay
#> Active assay: RNA (230 features, 20 variable features)
#> 2 dimensional reductions calculated: pca, tsne
#> 2000 features across 2638 samples within 1 assay
#> Active assay: RNA (2000 features, 2000 variable features)
#> 2 layers present: counts, data
#> 1 dimensional reduction calculated: umap

# New output option, directly as a vector (in the order of the metadata), which can then be inserted into metadata dataframes and other workflows
clustify(
input = s_small3,
cluster_col = "RNA_snn_res.1",
ref_mat = cbmc_ref,
vec_out = TRUE
)
#> [1] "Mk" "Mk" "Mk" "Mk" "Mk"
#> [6] "Mk" "Mk" "Mk" "Mk" "Mk"
#> [11] "B" "B" "B" "B" "B"
#> [16] "B" "B" "B" "B" "B"
#> [21] "CD16+ Mono" "CD16+ Mono" "CD16+ Mono" "CD16+ Mono" "CD16+ Mono"
#> [26] "CD16+ Mono" "CD16+ Mono" "CD16+ Mono" "CD16+ Mono" "CD16+ Mono"
#> [31] "Mk" "B" "Mk" "Mk" "Mk"
#> [36] "Mk" "Mk" "Mk" "Mk" "Mk"
#> [41] "Mk" "B" "Mk" "Mk" "B"
#> [46] "B" "Mk" "Mk" "Mk" "Mk"
#> [51] "CD16+ Mono" "CD16+ Mono" "B" "CD16+ Mono" "CD16+ Mono"
#> [56] "CD16+ Mono" "CD16+ Mono" "CD16+ Mono" "CD16+ Mono" "Mk"
#> [61] "B" "CD16+ Mono" "B" "CD16+ Mono" "B"
#> [66] "CD16+ Mono" "CD16+ Mono" "CD16+ Mono" "CD16+ Mono" "B"
#> [71] "Mk" "Mk" "Mk" "Mk" "Mk"
#> [76] "Mk" "Mk" "Mk" "Mk" "CD16+ Mono"
input = s_small,
cluster_col = "RNA_snn_res.0.5",
ref_mat = cbmc_ref,
vec_out = TRUE
)[1:10]
#> [1] "CD4 T" "B" "CD4 T" "CD14+ Mono" "NK"
#> [6] "CD4 T" "NK" "NK" "CD4 T" "CD16+ Mono"
```

New reference matrix can be made directly from `SingleCellExperiment`
Expand All @@ -151,38 +140,46 @@ supported as well.

``` r
# make reference from SingleCellExperiment objects
sce_small <- sce_pbmc()
sce_ref <- object_ref(
input = sce_small, # SCE object
cluster_col = "cell_type1" # name of column in colData containing cell identities
input = sce_small, # SCE object
cluster_col = "cell_type" # name of column in colData containing cell identities
)
#> The following clusters have less than 10 cells for this analysis: co-expression, ductal, endothelial, epsilon, MHC class II, PSC. Classification is likely inaccurate.

# make reference from seurat objects
s_small <- so_pbmc()
s_ref <- seurat_ref(
seurat_object = s_small3,
cluster_col = "RNA_snn_res.1"
seurat_object = s_small,
cluster_col = "RNA_snn_res.0.5"
)

head(s_ref)
#> 0 1 2
#> MS4A1 0.000000 1.126047 5.151065
#> CD79B 2.469341 2.920407 5.031316
#> CD79A 0.000000 2.535151 5.375681
#> HLA-DRA 3.640368 6.008446 7.055386
#> TCL1A 0.000000 1.495867 4.963367
#> HLA-DQB1 1.603068 3.836290 5.137422
#> 0 1 2 3 4 5
#> PPBP 0.04883837 0.06494743 0.28763857 0.09375021 0.35662599 0.2442300
#> LYZ 1.40165143 1.39466552 5.21550849 1.42699419 1.35146753 3.4034309
#> S100A9 0.55679700 0.58080250 4.91453355 0.62123058 0.58823794 2.6277996
#> IGLL5 0.03116080 0.04826212 0.02434753 2.44576997 0.03284986 0.2581198
#> GNLY 0.46041901 0.41001072 0.53592906 0.37877736 2.53161887 0.2903092
#> FTL 3.35611600 3.31062958 5.86217774 3.66698837 3.37056910 5.9518479
#> 6 7 8
#> PPBP 0.00000000 0.06527347 6.0941782
#> LYZ 1.32701580 4.84714962 2.5303912
#> S100A9 0.52098541 2.53310734 1.6775692
#> IGLL5 0.05247669 0.10986617 0.2501642
#> GNLY 4.70481754 0.46959958 0.3845813
#> FTL 3.38471536 4.21848878 4.5508242
```

`clustify_lists()` handles identity assignment of matrix or
`SingleCellExperiment` and `seurat` objects based on marker gene lists.

``` r
clustify_lists(
input = pbmc_matrix_small,
metadata = pbmc_meta,
cluster_col = "classified",
marker = pbmc_markers,
marker_inmatrix = FALSE
input = pbmc_matrix_small,
metadata = pbmc_meta,
cluster_col = "classified",
marker = pbmc_markers,
marker_inmatrix = FALSE
)
#> 0 1 2 3 4 5 6
#> Naive CD4 T 1.5639055 20.19469 31.77095 8.664074 23.844992 19.06931 19.06931
Expand All @@ -206,16 +203,17 @@ clustify_lists(
#> Platelet 19.492465 59.9493793

clustify_lists(
input = s_small3,
marker = pbmc_markers,
marker_inmatrix = FALSE,
cluster_col = "RNA_snn_res.1",
seurat_out = TRUE
input = s_small,
marker = pbmc_markers,
marker_inmatrix = FALSE,
cluster_col = "RNA_snn_res.0.5",
seurat_out = TRUE
)
#> An object of class Seurat
#> 230 features across 80 samples within 1 assay
#> Active assay: RNA (230 features, 20 variable features)
#> 2 dimensional reductions calculated: pca, tsne
#> 2000 features across 2638 samples within 1 assay
#> Active assay: RNA (2000 features, 2000 variable features)
#> 2 layers present: counts, data
#> 1 dimensional reduction calculated: umap
```

## Additional resources
Expand Down
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