samuel-marsh
commented
3 months ago Hi @nhu-github,
Are you able to provide a reproducible example? I'm unable to replicate your issue with either v3/4 or V5 style objects.
Best, Sam
library(tidyverse)
library(Seurat)
#> Loading required package: SeuratObject
#> Loading required package: sp
#>
#> Attaching package: 'SeuratObject'
#> The following objects are masked from 'package:base':
#>
#> intersect, t
# Load Data
pbmc <- pbmc3k.SeuratData::pbmc3k
pbmc <- UpdateSeuratObject(pbmc)
#> Validating object structure
#> Updating object slots
#> Ensuring keys are in the proper structure
#> Warning: Assay RNA changing from Assay to Assay
#> Ensuring keys are in the proper structure
#> Ensuring feature names don't have underscores or pipes
#> Updating slots in RNA
#> Validating object structure for Assay 'RNA'
#> Object representation is consistent with the most current Seurat version
# Process until PCA
pbmc <- NormalizeData(pbmc)
pbmc <- FindVariableFeatures(pbmc)
pbmc <- ScaleData(pbmc, features = Features(pbmc))
#> Centering and scaling data matrixpbmc <- RunPCA(pbmc)
#> PC_ 1
#> Positive: MALAT1, LTB, IL32, CD2, ACAP1, STK17A, CTSW, CD247, CCL5, GIMAP5
#> AQP3, GZMA, CST7, TRAF3IP3, MAL, HOPX, ITM2A, GZMK, MYC, BEX2
#> GIMAP7, ETS1, LDLRAP1, ZAP70, LYAR, RIC3, TNFAIP8, KLRG1, SAMD3, NKG7
#> Negative: CST3, TYROBP, LST1, AIF1, FTL, FCN1, LYZ, FTH1, S100A9, FCER1G
#> TYMP, CFD, LGALS1, CTSS, S100A8, SERPINA1, LGALS2, SPI1, IFITM3, PSAP
#> CFP, SAT1, IFI30, COTL1, S100A11, NPC2, LGALS3, GSTP1, PYCARD, NCF2
#> PC_ 2
#> Positive: CD79A, MS4A1, TCL1A, HLA-DQA1, HLA-DRA, HLA-DQB1, LINC00926, CD79B, HLA-DRB1, CD74
#> HLA-DPB1, HLA-DMA, HLA-DQA2, HLA-DRB5, HLA-DPA1, HLA-DMB, FCRLA, HVCN1, LTB, BLNK
#> KIAA0125, P2RX5, IRF8, IGLL5, SWAP70, ARHGAP24, SMIM14, PPP1R14A, FCRL2, C16orf74
#> Negative: NKG7, PRF1, CST7, GZMA, GZMB, FGFBP2, CTSW, GNLY, GZMH, SPON2
#> CCL4, FCGR3A, CCL5, CD247, XCL2, CLIC3, AKR1C3, SRGN, HOPX, CTSC
#> TTC38, S100A4, ANXA1, IL32, IGFBP7, ID2, ACTB, XCL1, APOBEC3G, SAMD3
#> PC_ 3
#> Positive: HLA-DQA1, CD79A, CD79B, HLA-DQB1, HLA-DPB1, CD74, HLA-DPA1, MS4A1, HLA-DRB1, HLA-DRB5
#> HLA-DRA, HLA-DQA2, TCL1A, LINC00926, HLA-DMB, HLA-DMA, HVCN1, FCRLA, IRF8, BLNK
#> KIAA0125, SMIM14, PLD4, IGLL5, P2RX5, TMSB10, SWAP70, LAT2, MALAT1, IGJ
#> Negative: PPBP, PF4, SDPR, SPARC, GNG11, NRGN, GP9, RGS18, TUBB1, CLU
#> HIST1H2AC, AP001189.4, ITGA2B, CD9, TMEM40, CA2, PTCRA, ACRBP, MMD, TREML1
#> NGFRAP1, F13A1, RUFY1, SEPT5, MPP1, CMTM5, TSC22D1, MYL9, RP11-367G6.3, GP1BA
#> PC_ 4
#> Positive: HLA-DQA1, CD79A, CD79B, HIST1H2AC, HLA-DQB1, PF4, MS4A1, SDPR, CD74, PPBP
#> HLA-DPB1, GNG11, HLA-DQA2, SPARC, HLA-DRB1, HLA-DPA1, GP9, TCL1A, HLA-DRA, LINC00926
#> NRGN, RGS18, HLA-DRB5, PTCRA, CD9, AP001189.4, CA2, CLU, TUBB1, ITGA2B
#> Negative: VIM, S100A8, S100A6, S100A4, S100A9, TMSB10, IL32, GIMAP7, LGALS2, S100A10
#> RBP7, FCN1, MAL, LYZ, S100A12, MS4A6A, CD2, FYB, S100A11, FOLR3
#> GIMAP4, AQP3, ANXA1, AIF1, MALAT1, GIMAP5, IL8, IFI6, TRABD2A, TMSB4X
#> PC_ 5
#> Positive: GZMB, FGFBP2, NKG7, GNLY, PRF1, CCL4, CST7, SPON2, GZMA, CLIC3
#> GZMH, XCL2, CTSW, TTC38, AKR1C3, CCL5, IGFBP7, XCL1, CCL3, S100A8
#> TYROBP, HOPX, CD160, HAVCR2, S100A9, FCER1G, PTGDR, LGALS2, RBP7, S100A12
#> Negative: LTB, VIM, AQP3, PPA1, MAL, KIAA0101, CD2, CYTIP, CORO1B, FYB
#> IL32, TRADD, ANXA5, TUBA1B, HN1, TYMS, PTGES3, ITM2A, COTL1, GPR183
#> TNFAIP8, ACTG1, TRAF3IP3, ATP5C1, GIMAP4, ZWINT, PRDX1, LDLRAP1, ABRACL, NGFRAP1pbmc <- FindNeighbors(pbmc, dims = 1:10)
#> Computing nearest neighbor graph
#> Computing SNNpbmc <- FindClusters(pbmc)
#> Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
#>
#> Number of nodes: 2700
#> Number of edges: 97892
#>
#> Running Louvain algorithm...
#> Maximum modularity in 10 random starts: 0.8314
#> Number of communities: 11
#> Elapsed time: 0 secondspbmc <- RunUMAP(pbmc, dims = 1:10)
#> Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
#> To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
#> This message will be shown once per session
#> 11:09:01 UMAP embedding parameters a = 0.9922 b = 1.112
#> 11:09:01 Read 2700 rows and found 10 numeric columns
#> 11:09:01 Using Annoy for neighbor search, n_neighbors = 30
#> 11:09:01 Building Annoy index with metric = cosine, n_trees = 50
#> 0% 10 20 30 40 50 60 70 80 90 100%
#> [----|----|----|----|----|----|----|----|----|----|
#> **************************************************|
#> 11:09:01 Writing NN index file to temp file /var/folders/92/rwb8659d3jl658jf5nmmvw580000gn/T//Rtmp9pgKQE/filef575c8c1cd1
#> 11:09:01 Searching Annoy index using 1 thread, search_k = 3000
#> 11:09:02 Annoy recall = 100%
#> 11:09:02 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
#> 11:09:03 Initializing from normalized Laplacian + noise (using RSpectra)
#> 11:09:03 Commencing optimization for 500 epochs, with 107868 positive edges
#> 11:09:06 Optimization finished
DoHeatmap(object = pbmc, features = c("ACTB", "CD3E"))
# Convert to Assay5 and plot
pbmc <- scCustomize::Convert_Assay(seurat_object = pbmc, convert_to = "v5")
#> Converting assay "RNA" from Assay to Assay5.DoHeatmap(object = pbmc, features = c("ACTB", "CD3E"))
Created on 2024-05-28 with reprex v2.1.0
longmanz
For the lastest version of Seurat,
DoHeatmapdonot return plot. I also tested with example data, it works. The difference is new obj with alayer.