Error in step 15

[dipingxian431]

Hi infercnv team,

I am running infercnv with the following command:

infercnv_obj = infercnv::run(infercnv_obj,
                             cutoff=0.1, # cutoff=1 works well for Smart-seq2, and cutoff=0.1 works well for 10x Genomics
                             out_dir=tempfile(), 
                             cluster_by_groups=FALSE, 
                             denoise=TRUE,
                             HMM=TRUE,
                             analysis_mode="subclusters")

infercnv_obj = infercnv::run(infercnv_obj,
                             cutoff=0.1, # cutoff=1 works well for Smart-seq2, and cutoff=0.1 works well for 10x Genomics
                             out_dir=tempfile(), 
                             cluster_by_groups=TRUE, 
                             denoise=TRUE,
                             HMM=TRUE)

However, I am stuck at step 15 and got the following error:

STEP 15: computing tumor subclusters via leiden

INFO [2024-10-02 19:18:41] define_signif_tumor_subclusters(p_val=0.1
INFO [2024-10-02 19:18:43] define_signif_tumor_subclusters(), tumor: WTcon
INFO [2024-10-02 19:18:43] Setting auto leiden resolution for WTcon to 0.0036674
Warning: Data is of class matrix. Coercing to dgCMatrix.
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%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix
  |=========================================================================================| 100%
PC_ 1 
Positive:  ATP13A1, ZNF91, UQCRFS1, NDUFA13, DPY19L3, MAU2, PDCD5, LSM4, FKBP8, TMEM161A 
	   JUND, KXD1, ANKRD27, PIK3R2, UBA52, ARMC6, MAP1S, DDX49, NUDT19, ANO8 
	   MAST3, COPE, SUGP2, RPL18A, DDA1, MRPL34, CRTC1, BABAM1, CEBPG, MYO9B 
Negative:  TBC1D23, TFG, CMSS1, PCNP, CLDND1, ZBTB11, CRYBG3, RPL24, PROS1, CEP97 
	   C3orf38, NFKBIZ, ZNF654, ALCAM, CGGBP1, CBLB, CHMP2B, BBX, GBE1, CD47 
	   ROBO1, ATG3, PPP4R2, SLC35A5, SHQ1, NAA50, ATP6V1A, FOXP1, ZBTB20, ARL6IP5 
PC_ 2 
Positive:  NUP54, SCARB2, CCNI, SDAD1, CNOT6L, PAQR3, USO1, G3BP2, HNRNPD, EPGN 
	   ENOPH1, ANKRD17, MOB1B, SCD5, GRSF1, SEC31A, RUFY3, COPS4, YTHDC1, UBA6 
	   WDFY3, POLR2B, ARHGAP24, NOA1, MAPK10, SRP72, PTPN13, PAICS, KLHL8, PPAT 
Negative:  POGLUT1, ARHGAP31, ZBTB20, TMEM39A, TIMMDC1, ATP6V1A, GSK3B, NAA50, SLC35A5, LRRC58 
	   ATG3, RABL3, CD47, GOLGB1, BBX, IQCB1, CBLB, FAM162A, ALCAM, KPNA1 
	   DTX3L, NFKBIZ, UMPS, CEP97, SLC12A8, RPL24, ZNF148, SNX4, ZBTB11, SLC41A3 
PC_ 3 
Positive:  ADAM10, RNF111, SLTM, POLR2M, GTF2A2, TCF12, ZNF280D, BNIP2, RFX7, VPS13C 
	   CCPG1, TPM1, RSL24D1, CA12, USP3, FAM214A, HERC1, ARPP19, SNX1, SNX22 
	   MYO5A, PPIB, GNB5, OAZ2, MAPK6, SPG21, LEO1, PDCD7, DMXL2, CLPX 
Negative:  LRRC58, RABL3, IQCB1, GSK3B, GOLGB1, FAM162A, TIMMDC1, POGLUT1, KPNA1, TMEM39A 
	   ARHGAP31, DTX3L, UMPS, ZBTB20, SLC12A8, ATP6V1A, ZNF148, NAA50, SNX4, SLC35A5 
	   ATG3, SLC41A3, CD47, ZXDC, BBX, CBLB, CHCHD6, ALCAM, NFKBIZ, PLXNA1 
PC_ 4 
Positive:  POLR2M, TCF12, ADAM10, ZNF280D, RNF111, RFX7, SLTM, GTF2A2, CCPG1, BNIP2 
	   RSL24D1, VPS13C, FAM214A, TPM1, CA12, ARPP19, USP3, MYO5A, HERC1, GNB5 
	   SNX1, SNX22, MAPK6, PPIB, OAZ2, LEO1, SPG21, DMXL2, PDCD7, SPPL2A 
Negative:  JKAMP, KIAA0586, RTN1, ARID4A, DHRS7, PPM1A, PSMA3, AP5M1, MNAT1, EXOC5 
	   TRMT5, KTN1, HIF1A, FBXO34, PPP2R5E, SYNE2, MAPK1IP1L, MTHFD1, SOCS4, ZBTB1 
	   PLEKHG3, GMFB, CHURC1, DDHD1, MAX, FUT8, FERMT2, GPHN, GNPNAT1, MPP5 
PC_ 5 
Positive:  GFM2, HEXB, UTP15, NSA2, BTF3, HMGCR, FCHO2, POLK, TNPO1, F2R 
	   MRPS27, MAP1B, AGGF1, MCCC2, WDR41, BDP1, TBCA, TAF9, AP3B1, CDK7 
	   SCAMP1, SLC30A5, LHFPL2, PIK3R1, SREK1, ARSB, NLN, JMY, CWC27, IPO11 
Negative:  DDX49, COPE, CRTC1, SUGP2, UBA52, ARMC6, KXD1, TMEM161A, FKBP8, MAU2 
	   LSM4, NDUFA13, JUND, ATP13A1, PIK3R2, ZNF91, MAST3, UQCRFS1, RPL18A, DPY19L3 
	   MAP1S, PDCD5, ANO8, ANKRD27, DDA1, NUDT19, MRPL34, CEBPG, BABAM1, LSM14A 
Computing nearest neighbor graph
Computing SNN
INFO [2024-10-02 19:19:16] -mirroring for hspike
INFO [2024-10-02 19:19:16] define_signif_tumor_subclusters(p_val=0.1
INFO [2024-10-02 19:19:16] define_signif_tumor_subclusters(), tumor: spike_tumor_cell_WTcon
INFO [2024-10-02 19:19:16] cut tree into: 1 groups
INFO [2024-10-02 19:19:16] -processing spike_tumor_cell_WTcon,spike_tumor_cell_WTcon_s1
INFO [2024-10-02 19:19:16] define_signif_tumor_subclusters(), tumor: simnorm_cell_WTcon
INFO [2024-10-02 19:19:16] cut tree into: 1 groups
INFO [2024-10-02 19:19:16] -processing simnorm_cell_WTcon,simnorm_cell_WTcon_s1
INFO [2024-10-02 19:19:38] ::plot_cnv:Start
INFO [2024-10-02 19:19:38] ::plot_cnv:Current data dimensions (r,c)=4971,8241 Total=41018945.4841801 Min=0.805509530861034 Max=1.96281771526343.
INFO [2024-10-02 19:19:38] ::plot_cnv:Depending on the size of the matrix this may take a moment.
INFO [2024-10-02 19:19:39] plot_cnv(): auto thresholding at: (0.858213 , 1.144371)
INFO [2024-10-02 19:20:13] plot_cnv_observation:Start
INFO [2024-10-02 19:20:14] Observation data size: Cells= 0 Genes= 4971
INFO [2024-10-02 19:20:14] clustering observations via method: ward.D
Error in seq_len(max(obs_annotations_groups)) : 
  argument must be coercible to non-negative integer
In addition: Warning messages:
1: In max(nchar(obs_annotations_names)) :
  no non-missing arguments to max; returning -Inf
2: In max(obs_annotations_groups) :
  no non-missing arguments to max; returning -Inf

my infercnv version is 1.21.0.
I am very puzzled about the error message. Is there something wrong with my command?
Thanks,
Mingchao

[xiaoli-gg]

Hi infercnv team,
I am running infercnv with the following command:

if (!requireNamespace("BiocManager", quietly = TRUE))
  install.packages("BiocManager")
BiocManager::install("infercnv")
library(infercnv)

infercnv_obj = CreateInfercnvObject(raw_counts_matrix=system.file("extdata", "oligodendroglioma_expression_downsampled.counts.matrix.gz", package = "infercnv"),
                                    annotations_file=system.file("extdata", "oligodendroglioma_annotations_downsampled.txt", package = "infercnv"),
                                    delim="\t",
                                    gene_order_file=system.file("extdata", "gencode_downsampled.EXAMPLE_ONLY_DONT_REUSE.txt", package = "infercnv"),
                                    ref_group_names=c("Microglia/Macrophage","Oligodendrocytes (non-malignant)")) 

infercnv_obj = infercnv::run(infercnv_obj,
                             cutoff=1, # cutoff=1 works well for Smart-seq2, and cutoff=0.1 works well for 10x Genomics
                             out_dir=tempfile(), 
                             cluster_by_groups=TRUE, 
                             denoise=F,
                             HMM=TRUE)

I am also stuck at step 15 and got the following error:

STEP 15: computing tumor subclusters via leiden###

INFO [2024-10-04 07:11:19] define_signif_tumor_subclusters(p_val=0.1
INFO [2024-10-04 07:11:19] define_signif_tumor_subclusters(), tumor: malignant_93
Warning: Data is of class matrix. Coercing to dgCMatrix.
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%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Warning: No layers found matching search pattern provided
Error in `ScaleData()`:
! No layer matching pattern 'data' found. Please run NormalizeData and retry
Run `rlang::last_trace()` to see where the error occurred.

My infercnv version is 1.20.0
Am I having problems with which configurations?
Thanks
Mengpan Li