Unable to load any objects on SPATA2

[ssrinivasan2]

Hi,

I am new to this package and am trying to better visualize already processed Seurat Objects.

Similar to the other issues, I am unable to load any objects into SPATA2. I have tried uninstalling and re-installing SPATA2 and Seurat as well, but this did not change the issues. Could you please let me know if there is something I am doing wrong?

I am using R/4.3.2 and SPATA2 v2.0.4 and Seurat v5.0.1.

I have tried several commands from the GitHub tutorial:

1. Loading Seurat objects

seurat_object <- SeuratData::LoadData("stxBrain", type = "anterior1") seurat_object <- Seurat::SCTransform(seurat_object, assay = "Spatial", verbose = FALSE)

spata2_object <- SPATA2::asSPATA2( object = seurat_object, sample_name = "mouse_brain", image_name = "anterior1", spatial_method = "Visium" )

2. Loading 10x data directly

object_t275 <- initiateSpataObject_10X( directory_10X = "path/to/a/10Xvisium-folder", # the directory from which to load the data sample_name = "gbm-275" )

3. Loading SPATA objects from SPATAdata

object_t269 <- downloadSpataObject(sample_name = "269_T")

In all three cases, I get this error:

Error in give_feedback(msg = msg, verbose = verbose, fdb.fn = fdb.fn, :

[kueckelj]

Hello, thanks for reaching out to us. Can you install confuns again and see if the error persists?

[ssrinivasan2]

Hi Jan, thank you for your response! I reinstalled confuns, but the same error persists.

[kueckelj]

I am looking into it. I will come back to you in no time.

[kueckelj]

Can you install confuns again and try again?

[healthgreat]

I reinstalled Confuns, but if I load it from scratch using initiate SpataObject=10X, it still prompts Error in getActiveMatrixName (object, verbose=FALSE): Please set an active matrix with setActivenMatrix() I suspect that this setActivenMatrix is spelled incorrectly. Shouldn't it be setActiveMatrix?

devtools::install_github(repo = "kueckelj/confuns",force = TRUE) Downloading GitHub repo kueckelj/confuns@HEAD ── R CMD build ─────────────────────────────────────────────────────────────────────────────────────────── ✔ checking for file 'C:\Users\Administrator\AppData\Local\Temp\RtmpCmCvtN\remotes1a1c77f7701e\kueckelj-confuns-3f03060/DESCRIPTION' ... ─ preparing 'confuns': (497ms) ✔ checking DESCRIPTION meta-information ... ─ checking for LF line-endings in source and make files and shell scripts (398ms) ─ checking for empty or unneeded directories ─ building 'confuns_1.0.3.tar.gz'

• installing source package 'confuns' ... using staged installation R data * moving datasets to lazyload DB byte-compile and prepare package for lazy loading Note: possible error in 'validColorSpectra(flatten = TRUE)': unused argument (flatten = TRUE) Note: possible error in 'validColorPalettes(flatten = TRUE)': unused argument (flatten = TRUE) * help installing help indices building package indices testing if installed package can be loaded from temporary location testing if installed package can be loaded from final location testing if installed package keeps a record of temporary installation path
• DONE (confuns)

> object_t275 <-initiateSpataObject_10X(

• directory_10X = "./rawdata/CR_22_17626_FP_R_SSV_1/", # the directory from which to load the data
• sample_name = "CR_22_17626_FP_R_SSV_1"
• ) 11:01:57 Starting initiation. 11:01:57 No gene-set data.frame path specified. 11:01:57 Reading in .h5 file. 11:01:57 Loading from directory: './rawdata/CR_22_17626_FP_R_SSV1/' 11:01:59 Performing Seurat-analysis steps. 11:01:59 Calculating percentage of ribosomal and mitochondrial genes. 11:01:59 Removing stress genes and mitochondrial genes. 11:01:60 Skip running 'SCTransform()' as it's argument input is neither TRUE nor a list. 11:01:60 Running 'Seurat::NormalizeData()' with specified parameters. Normalizing layer: counts Performing log-normalization 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| 11:02:01 Running 'Seurat::FindVariableFeatures()' with specified parameters. Finding variable features for layer counts Calculating gene variances 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Calculating feature variances of standardized and clipped values 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| 11:02:03 Running 'Seurat::ScaleData()' with default parameters. Centering and scaling data matrix |================================================================================================| 100% 11:02:05 Running 'Seurat::RunPCA()' with specified parameters. PC 1 Positive: APOE, CD74, LAPTM5, C11orf96, TYROBP, C3, HLA-DRA, CTSD, C1QB, C1QC FABP4, IFI30, SRGN, HLA-DPB1, PECAM1, SLC11A1, OLR1, GPX3, CCN1, G0S2 THBS4, APOD, COL6A3, TYMP, PLIN2, CD68, C1QA, ZNF331, ANGPTL4, ADAMTS1 Negative: SPINT2, TFF1, C19orf33, LGALS4, ATP1B1, FXYD3, ELF3, S100A6, MUC1, CTSE TFF2, CBLC, TMC5, GPRC5A, CYP3A5, GOLM1, RHPN2, KRT8, ID1, SPINK1 S100P, TNS4, VASP, INAVA, KRT18, LSR, ACTN4, ABCC3, MUC5AC, TMPRSS4 PC 2 Positive: CTSB, MMP14, FBP1, CTSD, IL32, TIMP3, TGFBI, TREM1, SLC11A1, KRT7 CAPG, SPP1, LAMB3, COL6A3, DDIT4, IFI27, UBD, KRT8, IFI6, MARCO CLTB, LAPTM5, TAGLN2, OLR1, MMP11, S100A4, CXCL5, S100A10, MMP12, CD68 Negative: IGFBP3, CLU, CXCL14, IGFBP5, PRELP, APOD, C7, GCG, PTGDS, DMBT1 SOD3, DPEP1, IGHA1, HEYL, MSMB, OLFM4, MGP, SYNPO2, HSPB6, COL14A1 SLC40A1, JCHAIN, PPP1R1B, ADIRF, IGHM, TFF2, BPIFB1, ELN, EPHA3, IGHG4 PC 3 Positive: APOE, UBD, FABP4, IFI6, TYROBP, LAPTM5, IFI30, HMGA1, LYZ, C1QC C1QB, CTSD, APOC1, TYMP, CCL18, IFI27, CD74, CD36, SLC11A1, ST14 OLR1, AQP9, AQP1, FBP1, PSMD8, SPINK1, GPX3, JPT1, VIL1, C1QA Negative: COL10A1, TAGLN, COL5A1, THBS2, CALD1, STMN2, INHBA, COL5A2, ANTXR1, COL12A1 COL11A1, IGFBP3, IGFBP5, LOXL2, TIMP3, NPR3, HOPX, SLC44A1, PALLD, DPYSL3 CXCL14, HTRA1, POSTN, MMP2, ITGA11, SULF1, MIF, LTBP1, CST1, NR4A1 PC 4 Positive: KRT7, CLTB, EPHB6, MALL, UBD, DHRS9, TM4SF1, COL17A1, PMEPA1, SOD3 KRT23, TACSTD2, DES, CDH1, TNFRSF21, MELTF, KLK6, ITGB6, SERPINB2, ITGA3 CDA, VWF, IGLC1, C7, ITGB4, KRT16, ADIRF, ITGA2, LAMB3, THBS4 Negative: LYZ, DMBT1, BPIFB1, SPP1, MSMB, CTSB, CTSD, SLC11A1, MUC5B, CD68 HIST1H1B, FBP1, FCER1G, OLR1, LSP1, LAPTM5, OLFM4, APOC1, MARCO, FCGR3A INHBA, COL11A1, AQP5, PPP1R1B, IFI30, SLC17A9, TRNP1, ANPEP, S100A9, ITGAX PC 5 Positive: FOSB, NR4A1, MIDN, MUC5AC, IER3, CCN1, DES, GREM1, LOXL2, ID1 IER2, PSCA, AQP5, LFNG, REG4, MMP2, TRNP1, ACTG2, MARCKSL1, ACTN4 C11orf96, MCAM, SULT1C2, EGR3, PXDN, BCL3, COL12A1, COL11A1, GPX3, COL6A3 Negative: GCG, IGKC, IGHA1, CXCL14, CST1, IGFBP3, JCHAIN, STRA6, IGFBP5, DPEP1 IGHM, IGHG4, SERPINA1, IGHJ6, IGHG1, EPHA3, SST, IGKV4-1, TTR, PRELP MMP7, INS, FABP1, COLEC11, MOGAT1, CHGB, FBP1, ADAMTSL2, CHGA, APOD 11:02:14 Running 'Seurat::FindNeighbors()' with specified parameters. Computing nearest neighbor graph Computing SNN 11:02:15 Running 'Seurat::FindClusters()' with specified parameters. Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 3947 Number of edges: 152994

Running Louvain algorithm... 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Maximum modularity in 10 random starts: 0.7921 Number of communities: 13 Elapsed time: 0 seconds 11:02:15 Running 'Seurat::RunTSNE()' with default parameters. 11:02:22 Running 'Seurat::RunUMAP()' with specified parameters. 11:02:22 UMAP embedding parameters a = 0.9922 b = 1.112 Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics' Also defined by ‘spam’ 11:02:22 Read 3947 rows and found 30 numeric columns 11:02:22 Using Annoy for neighbor search, n_neighbors = 30 Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics' Also defined by ‘spam’ 11:02:22 Building Annoy index with metric = cosine, n_trees = 50 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| 11:02:22 Writing NN index file to temp file C:\Users\ADMINI~1\AppData\Local\Temp\RtmpCmCvtN\file1a1c4d336de 11:02:22 Searching Annoy index using 1 thread, search_k = 3000 11:02:22 Annoy recall = 100% 11:02:23 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30 11:02:24 Initializing from normalized Laplacian + noise (using RSpectra) 11:02:24 Commencing optimization for 500 epochs, with 168256 positive edges Using method 'umap' 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| 11:02:33 Optimization finished 11:02:33 Initiating spata2 object. 11:02:33 Setting up new spata2 object. 11:02:33 Transferring data. 11:02:35 Done. 11:02:35 Saving. 11:02:35 No directory specified. Skip saving Seurat object. 11:02:35 Computing pixel scale factor. 11:02:35 Using center to center distance to compute pixel scale factor. 11:02:35 Computing barcode spot distances. 11:02:38 Done. Error in getActiveMatrixName(object, verbose = FALSE) : Please set an active matrix with setActivenMatrix() In addition: Warning message: Not validating Seurat objects

[YingXu-FDU]

Hi there, i met with the same problem. Error in getActiveMatrixName(object, verbose = FALSE) : Please set an active matrix with setActivenMatrix()

have you fixed it?

[simonmfr]

Thank you for raising this issue. Please update SPATA2 and confuns, and try again. It should work now.