hsjeon-k
commented
1 year ago Hello, thank you for letting us know!
From the runtime logged on your error message, it seems like you may be running CytoSPACE with a large dataset. In this case, the process might be getting terminated/killed due to memory restrictions. I would suggest trying the subsampling option to reduce the memory requirement, where your ST dataset will be partitioned into smaller chunks for assigning single cells. Please see the "Advanced options - Spot subsampling for parallelization" section of the documentation for more information.
The required flags will be: cytospace [...] -sss -nosss [number_of_ST_cells_in_each_partition] -nop [number_of_processors] (please note that -nosss is different from -noss, which is not relevant in this case).
I find that for the example breast cancer dataset, -sss -nosss 3000 -nop 2 works well on my local environment with ~8GB RAM. But the sufficient -nosss and -nop combination would really depend on your dataset size and environment setup, so I would suggest that you experiment with different values (using the highest possible -nosss that can run without going out of memory would be the most effective).
When using the -nop flag, you might get a different error message for going out of memory -- in my case, I usually see concurrent.futures.process.BrokenProcessPool: A process in the process pool was terminated abruptly while the future was running or pending. which I resolve by choosing a lower -nosss.
Hello,
cytospace is a wonderful tool！
I ran into an issue with explore my scRNA and ST data.
My command : cytospace -sp scRNA_08_counts.txt -ctp scRNA_08_celllabel.txt -stp stRNA08_counts.txt -cp stRNA08_coordinates.txt -o cytospace_results_08 -sm lap_CSPR
Error: (cytospace) shpc_100668@shpc52:~/liverzonation/cytospace/data/liver$ cytospace -sp scRNA_08_counts.txt -ctp scRNA_08_celllabel.txt -stp stRNA08_counts.txt -cp stRNA08_coordinates.txt -o cytospace_results_08 -sm lapCSPR Read and validate data ... 100% |██████████████████████████████████████████████████| Reading data [done] Estimating cell type fractions 2023-03-11 14:53:27 Load ST data PC 1 Positive: IGHG4, IGKC, IGHG3, TAGLN, MYL9, CRIP1, FLNA, C7, VIM, S100A6 IGHGP, MYH11, ACTA2, AEBP1, JCHAIN, ADIRF, IGLC2, MGP, IGHG1, CD74 TPM2, IGHA1, TMSB10, TMSB4X, IGFBP7, SPARCL1, FBLN1, GSN, HLA-DRB1, IGFBP5 Negative: APOA1, APOC3, HP, APOC1, APOA2, ORM1, FGB, CYP3A4, CYP2E1, VTN ITIH4, SERPINF2, ADH1A, AL645922.1, SCD, MT2A, AGXT, RIDA, CYP2C8, FTCD MT1G, ASS1, AGT, APOC4, FABP1, F2, HAMP, DHCR24, IGFBP4, CYP1A2 PC 2 Positive: IGHG4, IGHG3, IGKC, IGHGP, IGHG1, IGLC2, JCHAIN, IGHA1, IGLL5, APOA1 IGHM, IGHG2, MZB1, IGLC3, APOC3, APOC1, APOA2, C7, HP, FGB CYP2E1, ORM1, INMT, F2, FABP1, IGHA2, VTN, SERPINF2, AGXT, ITIH4 Negative: TFF2, MUC5B, SCGB3A1, TFF3, TFF1, MUC1, CEACAM6, FXYD2, LGALS2, SPINK1 LYZ, CD24, PIGR, C19orf33, ANXA4, SLC44A4, DUOX2, S100A6, TCN1, AGR2 AKR1B10, NQO1, CRISP3, GABRP, GPX2, KRT19, FXYD3, APCDD1, SPP1, SMIM24 PC 3 Positive: IGHG4, IGHG3, IGKC, IGHGP, TFF2, MUC5B, IGHG1, IGLC2, SCGB3A1, TFF3 JCHAIN, FXYD2, TFF1, MUC1, CEACAM6, IGHA1, SPINK1, LGALS2, LYZ, CD24 PIGR, ANXA4, IGHM, SLC44A4, C19orf33, IGLL5, DUOX2, TCN1, IGHG2, AGR2 Negative: TAGLN, MYL9, FLNA, CRIP1, MYH11, ACTA2, TPM2, ADIRF, DES, SPARCL1 AC006254.1, VIM, AEBP1, CSRP1, IGFBP5, CAVIN1, ACTG2, MFGE8, MYL6, CNN1 MGP, LMOD1, DSTN, TNS1, CKB, MCAM, CRIP2, MUSTN1, CCDC3, CAV1 PC 4 Positive: IGHG4, MYL9, IGHG3, TAGLN, DES, MYH11, IGHGP, FLNA, TFF2, MUC5B TFF1, TPM2, ACTA2, SCGB3A1, MUC1, ACTG2, CSRP1, CEACAM6, SPINK1, CNN1 LMOD1, TFF3, PIGR, CKB, DUOX2, AC006254.1, AKR1B10, CRISP3, C19orf33, DSTN Negative: CD74, CCL19, TMSB4X, TMSB10, HLA-DRB1, HLA-DRA, C7, RPS27, HLA-DPB1, HLA-DQB1 PTGDS, IGHM, IGHA1, HLA-DPA1, CCL21, RPLP2, TIMP1, RPS19, CD52, MGP RPS18, VIM, RPL13, LAPTM5, RPS12, RPL27A, IGFBP7, FBLN1, RPS15, RPL13A PC 5 Positive: IGKC, IGHA1, JCHAIN, IGHG1, IGHM, IGLC2, MYL9, IGLC3, IGHA2, TAGLN MZB1, IGLL5, DES, MYH11, MUC5B, ACTG2, CYP3A4, LMOD1, TFF2, TFF1 MUC1, CSRP1, CNN1, TPM2, CYP2E1, FLNA, DUOX2, IGFBP5, CKB, HSPB6 Negative: IGHG4, IGHG3, IGHGP, CD74, CCL19, TMSB4X, TMSB10, HLA-DRB1, RPS27, HLA-DPB1 TIMP1, CCL21, HLA-DRA, PTGDS, RPS18, S100A6, VIM, HLA-DQB1, CD52, CXCR4 LAPTM5, RPLP2, HLA-DPA1, RPS19, ACTB, RPL13, TRBC1, RPLP1, MGP, CCR7 2023-03-11 15:09:37 Load scRNA data Warning: Invalid name supplied, making object name syntactically valid. New object name is Cell.IDsCellType; see ?make.names for more details on syntax validity PC 1 Positive: PTPRB, LDB2, DNASE1L3, ST6GAL1, RELN, STAB2, LIFR, NPL, NTN4, MEIS2 LINC02388, NRG3, CD36, HECW2, GPM6A, ANKS1A, CRHBP, ST6GALNAC3, AC098650.1, MRC1 FBXL7, FCHSD2, PLCB1, AKAP12, PLXDC2, MS4A6A, ITGA9, BMPER, NRP1, PPFIBP1 Negative: CYP3A5, ACSM2A, CYP2C8, AFM, ACSM2B, C3, TAT, CYP2B6, CYP2C9, GHR FTCD, TF, MAT1A, ECHDC2, PLG, AL583836.1, FMO5, SCP2, CPS1, CUX2 APOB, ZC3H13, CP, HSP90AA1, ALB, AC011604.2, ACSL1, DPYS, PCSK6, BHMT PC 2 Positive: ADAMTSL1, CCBE1, AC098650.1, COL25A1, PRKG1, PTH1R, HHIP, DACH1, ADAMTS13, ANK3 SYT1, LAMA2, ZFPM2, RIPOR3, AF165147.1, NR1H4, FGFR2, GRK5, LINC02398, HMCN1 ADAMTS2, ANKS1A, SGCZ, CNTN4, PDE1A, PLA2G5, ARHGAP15, CALD1, PCDH9, LAMB1 Negative: PTPRB, LDB2, DNASE1L3, ST6GAL1, STAB2, NPL, NTN4, CD36, NRG3, MRC1 CRHBP, HECW2, ST6GALNAC3, AKAP12, MS4A6A, FCHSD2, PPFIBP1, MEIS2, LINC02388, STAB1 RIPOR2, SLC26A5, PTPRT, LGMN, KALRN, ACSM3, SEMA6A, XAF1, F8, ADGRF5 PC 3 Positive: ANXA4, PKHD1, BICC1, RALYL, CTNND2, DCDC2, KCNJ15, CFTR, WNK2, PDGFD FGFR2, MAML2, CASR, CALN1, DSCAML1, LINC01320, SLC5A1, AC008554.1, SLC4A4, TM4SF4 FRAS1, PLD1, CASC15, SLC12A2, SLC17A4, TMTC2, ROCR, GLIS3, KRT7, CHST4 Negative: CYP3A5, ACSM2A, CYP2C8, AFM, ACSM2B, C3, TAT, CYP2B6, CYP2C9, GHR NEAT1, TF, PLG, RELN, MAT1A, CPS1, PCSK6, TMEM56, CP, SCP2 CUX2, APOB, FTCD, BHMT, ACSL1, C1R, CCBE1, PDE3B, ECHDC2, NSUN6 PC 4 Positive: DNASE1L3, NPL, STAB2, RELN, NTN4, MS4A6A, ANXA4, MRC1, CRHBP, AKAP12 STAB1, PKHD1, LGMN, ST6GAL1, NRP1, MYO10, HECW2, BICC1, PPFIBP1, ANKS1A SLC26A5, ADGRL2, PTPRT, HS3ST3A1, OIT3, FGFR2, CLEC4M, CPM, ANKRD44, CTTNBP2 Negative: MECOM, ST8SIA6, ANO2, TMTC1, VWF, SPARCL1, PECAM1, SULF1, ELN, LDB2 PTPRB, PLCXD3, FAM155A, ARL15, SLCO2A1, PLCB4, COL8A1, FLRT2, ENTPD1, MMP28 DOCK9, RYR3, NAV3, HEG1, ST6GALNAC3, OPCML, PTGIS, TANC2, CDH11, RUNX1T1 PC_ 5 Positive: RBFOX1, CTNNA2, PCDH15, CNTNAP2, EYS, ASIC2, GALNTL6, CCDC26, AGBL4, GRID2 NRXN1, NRXN3, LRRC4C, AC007402.1, DPP6, LINC00276, NPL, CDH12, CSMD1, CACNA1A CDH18, PTPRT, CFAP299, AL589740.1, DGKB, ERC2, NELL1, CNTNAP5, DOCK3, AC011287.1 Negative: MECOM, PTPRB, LDB2, ST8SIA6, FLRT2, ANXA4, ARL15, SPARCL1, CYP3A5, SULF1 ANO2, PLCXD3, MEIS2, PECAM1, PKHD1, TMTC1, BICC1, PLPP1, SLCO2A1, VWF ST6GALNAC3, HSP90AA1, FGFR2, ELN, NR2F2-AS1, PTPRG, RUNX1T1, PTPN14, MMP28, PTGIS 2023-03-11 15:13:36 Integration Performing PCA on the provided reference using 2875 features as input. Projecting PCA Finding neighborhoods Finding anchors Found 6322 anchors Filtering anchors Retained 1454 anchors Finding integration vectors Finding integration vector weights 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Predicting cell labels 100% |██████████████████████████████████████████████████| Reading data [done] Time to read and validate data: 2877.4 seconds Estimating number of cells in each spot ... Time to estimate number of cells per spot: 416.89 seconds Down/up sample of scRNA-seq data according to estimated cell type fractions Time to down/up sample scRNA-seq data: 17.6 seconds Building cost matrix ... Time to build cost matrix: 3448.55 seconds Solving linear assignment problem ... Terminated
I'm really at a loss as to how to proceed, and any guidance would be much appreciated! Thank you for your kind help!