This function generates a projection plot, which can be used to compare two groups of cells in a dimensionality reduction space.
Usage
ProjectionPlot(
srt_query,
srt_ref,
query_group = NULL,
ref_group = NULL,
query_reduction = "ref.embeddings",
ref_reduction = srt_query[[query_reduction]]@misc[["reduction.model"]] %||% NULL,
query_param = list(palette = "Set1", cells.highlight = TRUE),
ref_param = list(palette = "Paired"),
xlim = NULL,
ylim = NULL,
pt.size = 0.8,
stroke.highlight = 0.5
)Arguments
- srt_query
An object of class Seurat storing the query group cells.
- srt_ref
An object of class Seurat storing the reference group cells.
- query_group
The grouping variable for the query group cells.
- ref_group
The grouping variable for the reference group cells.
- query_reduction
The name of the reduction in the query group cells.
- ref_reduction
The name of the reduction in the reference group cells.
- query_param
A list of parameters for customizing the query group plot. Available parameters: palette (color palette for groups) and cells.highlight (whether to highlight cells).
- ref_param
A list of parameters for customizing the reference group plot. Available parameters: palette (color palette for groups) and cells.highlight (whether to highlight cells).
- xlim
The x-axis limits for the plot. If not provided, the limits will be calculated based on the data.
- ylim
The y-axis limits for the plot. If not provided, the limits will be calculated based on the data.
- pt.size
The size of the points in the plot.
- stroke.highlight
The size of the stroke highlight for cells.
Examples
data(panc8_sub)
panc8_sub <- standard_scop(panc8_sub)
#> ℹ [2025-11-13 12:01:04] Start standard scop workflow...
#> ℹ [2025-11-13 12:01:04] Checking a list of <Seurat> object...
#> ! [2025-11-13 12:01:04] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2025-11-13 12:01:04] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 1/1 of the `srt_list`...
#> ℹ [2025-11-13 12:01:07] Perform `Seurat::FindVariableFeatures()` on the data 1/1 of the `srt_list`...
#> ℹ [2025-11-13 12:01:07] Use the separate HVF from srt_list
#> ℹ [2025-11-13 12:01:07] Number of available HVF: 2000
#> ℹ [2025-11-13 12:01:08] Finished check
#> ℹ [2025-11-13 12:01:08] Perform `Seurat::ScaleData()`
#> ℹ [2025-11-13 12:01:08] Perform pca linear dimension reduction
#> StandardPC_ 1
#> Positive: CHGA, PCSK1N, G6PC2, PCSK1, IAPP, ARFGEF3, CRYBA2, PRUNE2, CDKN1C, SORL1
#> EDN3, CADM1, FXYD2, ELMO1, HADH, PAPPA2, GRIA3, RBP4, DLK1, ANXA6
#> HMGN2, GNAZ, AMPD2, IGF2, ROBO2, DNAJA4, PDK4, SEPT3, CD99L2, SYT17
#> Negative: IFITM3, ZFP36L1, SOX4, ANXA4, KRT7, TPM1, PMEPA1, SERPING1, TM4SF1, CD44
#> CDC42EP1, TMSB10, NFIB, SAT1, SDC4, SPTBN1, LCN2, KRT18, PDZK1IP1, MSN
#> SMAD3, CLDN10, CFTR, NOTCH2, KRT19, CTSH, SERPINA5, FLRT2, C3, EPS8
#> StandardPC_ 2
#> Positive: SPARC, COL4A1, COL15A1, COL1A2, COL3A1, PXDN, PDGFRB, COL5A1, BGN, COL5A2
#> COL1A1, LAMA4, TIMP3, COL6A2, IGFBP4, AEBP1, SFRP2, THBS2, FBN1, COL6A1
#> CDH11, VCAN, SERPINE1, WNT5A, FN1, TPM2, FMOD, MMP2, SNAI1, DCN
#> Negative: KRT8, SPINK1, PRSS1, ELF3, GATM, MUC1, KRT18, CPA2, CTRB1, SDC4
#> PRSS3, CLDN4, LCN2, ANPEP, CPA1, PDZK1IP1, PLA2G1B, CTRC, CPB1, PNLIP
#> KLK1, CELA2A, CELA3A, KRT7, GSTA1, CD44, PNLIPRP1, PNLIPRP2, CELA3B, GSTA2
#> StandardPC_ 3
#> Positive: FTO, SORL1, TBC1D24, CASR, PCYOX1, UTRN, ADH5, ENPP5, RNF14, PHKB
#> MAP1A, C2CD5, TTC17, RAB22A, PRR14L, AP3B1, MTR, HERC1, EXPH5, SMCHD1
#> ROBO1, ABHD10, PRUNE2, SPEN, BTBD3, IBTK, ARFGEF2, TSC1, PARP4, RMND5A
#> Negative: HSPB1, CELA3A, CELA3B, CLPS, CTRB1, SYCN, CELA2A, EIF4A1, VIM, PNLIPRP1
#> PLA2G1B, KLK1, CPA1, CTRC, DDIT4, PLTP, BGN, DYNLL2, ANGPTL4, COL6A2
#> IFITM1, IGFBP4, IGFBP2, TMSB10, PRSS1, CTRL, PDGFRB, CPA2, PRSS3, PXDN
#> StandardPC_ 4
#> Positive: CPA2, PNLIP, PRSS1, CTRC, CPA1, CPB1, PLA2G1B, PNLIPRP2, PRSS3, BCAT1
#> CEL, KLK1, CELA2A, CTRB1, PNLIPRP1, SPINK1, GSTA2, MGST1, CELA3A, LDHB
#> ALB, CTRL, CELA3B, CLPS, ALDOB, REG3G, FAM129A, GSTA1, SYCN, CBS
#> Negative: CFTR, MMP7, KRT19, SERPINA5, TINAGL1, AQP1, SPP1, SERPING1, PMEPA1, KRT23
#> ALDH1A3, TSPAN8, PROM1, IGFBP7, VCAM1, LGALS4, ONECUT2, TRPV6, CCL2, ANXA3
#> TNFAIP2, CTSH, SDC1, SLC3A1, CLDN10, ANXA9, CCND1, KRT80, VNN1, PDGFD
#> StandardPC_ 5
#> Positive: COL5A1, COL1A2, COL1A1, SFRP2, COL5A2, COL3A1, VCAN, FN1, PDGFRB, THBS2
#> FMOD, BGN, ANTXR1, MXRA8, COL6A1, AEBP1, TPM2, CDH11, DCN, ISLR
#> TGFB3, COL6A2, LTBP2, DDR2, EDNRA, ANO1, LTBP1, GFPT2, WNT5A, HEYL
#> Negative: CD93, PLVAP, PODXL, ACVRL1, ESAM, S1PR1, CXCR4, ECSCR, DYSF, CALCRL
#> ADGRF5, STC1, CD34, AFAP1L1, IFI27, SH3BP5, ACKR3, ANGPT2, DLL4, MMRN2
#> MCAM, PNP, IL3RA, SPARCL1, TCF4, FAM198B, RAPGEF5, ARHGAP31, P2RY6, F2RL3
#> ℹ [2025-11-13 12:01:10] Perform `Seurat::FindClusters()` with louvain and `cluster_resolution` = 0.6
#> ℹ [2025-11-13 12:01:10] Reorder clusters...
#> ℹ [2025-11-13 12:01:10] Perform umap nonlinear dimension reduction
#> ℹ [2025-11-13 12:01:10] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-11-13 12:01:10] UMAP will return its model
#> ℹ [2025-11-13 12:01:14] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-11-13 12:01:14] UMAP will return its model
#> ✔ [2025-11-13 12:01:19] Run scop standard workflow done
srt_ref <- panc8_sub[, panc8_sub$tech != "fluidigmc1"]
srt_query <- panc8_sub[, panc8_sub$tech == "fluidigmc1"]
srt_ref <- integration_scop(
srt_ref,
batch = "tech",
integration_method = "Uncorrected"
)
#> ◌ [2025-11-13 12:01:19] Run Uncorrected integration...
#> ℹ [2025-11-13 12:01:19] Spliting `srt_merge` into `srt_list` by column "tech"...
#> ℹ [2025-11-13 12:01:20] Checking a list of <Seurat> object...
#> ℹ [2025-11-13 12:01:20] Data 1/4 of the `srt_list` has been log-normalized
#> ℹ [2025-11-13 12:01:20] Perform `Seurat::FindVariableFeatures()` on the data 1/4 of the `srt_list`...
#> ℹ [2025-11-13 12:01:21] Data 2/4 of the `srt_list` has been log-normalized
#> ℹ [2025-11-13 12:01:21] Perform `Seurat::FindVariableFeatures()` on the data 2/4 of the `srt_list`...
#> ℹ [2025-11-13 12:01:21] Data 3/4 of the `srt_list` has been log-normalized
#> ℹ [2025-11-13 12:01:21] Perform `Seurat::FindVariableFeatures()` on the data 3/4 of the `srt_list`...
#> ℹ [2025-11-13 12:01:22] Data 4/4 of the `srt_list` has been log-normalized
#> ℹ [2025-11-13 12:01:22] Perform `Seurat::FindVariableFeatures()` on the data 4/4 of the `srt_list`...
#> ℹ [2025-11-13 12:01:22] Use the separate HVF from srt_list
#> ℹ [2025-11-13 12:01:22] Number of available HVF: 2000
#> ℹ [2025-11-13 12:01:23] Finished check
#> ℹ [2025-11-13 12:01:24] Perform Uncorrected integration
#> ℹ [2025-11-13 12:01:25] Perform `Seurat::ScaleData()`
#> ℹ [2025-11-13 12:01:25] Perform linear dimension reduction("pca")
#> ℹ [2025-11-13 12:01:27] Perform FindClusters ("louvain")
#> ℹ [2025-11-13 12:01:27] Reorder clusters...
#> ℹ [2025-11-13 12:01:27] Perform nonlinear dimension reduction ("umap")
#> ℹ [2025-11-13 12:01:27] Non-linear dimensionality reduction (umap) using (Uncorrectedpca) dims (1-10) as input
#> ℹ [2025-11-13 12:01:31] Non-linear dimensionality reduction (umap) using (Uncorrectedpca) dims (1-10) as input
#> ✔ [2025-11-13 12:01:37] Run Uncorrected integration done
CellDimPlot(
srt_ref,
group.by = c("celltype", "tech")
)
# Projection
srt_query <- RunKNNMap(
srt_query = srt_query,
srt_ref = srt_ref,
ref_umap = "UncorrectedUMAP2D"
)
#> ℹ [2025-11-13 12:01:37] Use the features to calculate distance metric
#> ℹ [2025-11-13 12:01:37] Data type is log-normalized
#> ℹ [2025-11-13 12:01:38] Data type is log-normalized
#> ℹ [2025-11-13 12:01:38] Use 2000 features to calculate distance
#> ℹ [2025-11-13 12:01:38] Use raw method to find neighbors
#> ℹ [2025-11-13 12:01:38] Running UMAP projection
ProjectionPlot(
srt_query = srt_query,
srt_ref = srt_ref,
query_group = "celltype",
ref_group = "celltype"
)
#> Scale for x is already present.
#> Adding another scale for x, which will replace the existing scale.
#> Scale for y is already present.
#> Adding another scale for y, which will replace the existing scale.
