Error in eigen(Zt_wZ, symmetric = TRUE)

[Yuanca9]

Hello,

I encountered the following issue while running the run_calibration_check() function:

sceptre_object <- run_calibration_check(
  sceptre_object = sceptre_object,
  parallel = FALSE
)
Note: If you are on a Mac laptop or desktop, consider setting `parallel = TRUE` to improve speed. Otherwise, keep `parallel = FALSE`.

Constructing negative control pairs. ✓
Note: Unable to generate the number of negative control pairs (991975) requested. Generating as many negative control pairs (11761) as possible.
Generating permutation resamples. ✓
Analyzing pairs containing response ENSMUSG00000053644 (1 of 11761)
Error in eigen(Zt_wZ, symmetric = TRUE) : 'x'里有无穷值或遗漏值。

Sorry about the Chinese characters in the output. It seems that some infinite values or missing values were generated while processing my discovery pairs. However, my data has already passed the QC steps and generated valid discovery pairs. Does anyone have any ideas on how to resolve this issue?

Here's my sceptre object:

An object of class sceptre_object.

Attributes of the data:
    • 67836 cells (6242 after cellwise QC)
    • 32287 responses
    • Low multiplicity-of-infection 
    • 268 targeting gRNAs (distributed across 139 targets) 
    • 1 non-targeting gRNAs 
    • 7 covariates (cell_type, grna_n_nonzero, grna_n_umis, n_egfp, response_n_nonzero, response_n_umis, response_p_mito)

Analysis status:
    ✓ import_data()
    ✓ set_analysis_parameters()
    ✓ assign_grnas()
    ✓ run_qc()
    ✗ run_calibration_check()
    ✗ run_power_check()
    ✗ run_discovery_analysis()

Analysis parameters: 
    • Discovery pairs: data frame with 4487754 pairs (991975 after pairwise QC)
    • Positive control pairs: data frame with 139 pairs (79 after pairwise QC)
    • Sidedness of test: both
    • Control group: complement set
    • Resampling mechanism: permutations
    • gRNA integration strategy: singleton
    • Resampling approximation: skew normal
    • Multiple testing adjustment: BH at level 0.1
    • N nonzero treatment cells threshold: 7
    • N nonzero control cells threshold: 7
    • Formula object: log(response_n_nonzero) + log(response_n_umis) + response_p_mito + cell_type

gRNA-to-cell assignment information:
    • Assignment method: mixture
    • Mean N cells per gRNA: 26.83
    • Mean N gRNAs per cell (MOI): 0.11 
    • gRNA assignment formula object: log(response_n_nonzero) + log(response_n_umis) + log(n_egfp + 1) + response_p_mito + cell_type + log(grna_n_nonzero + 1) + log(grna_n_umis + 1)

[timothy-barry]

Hi,

Thanks for the report. I don't think I've seen this before. Something is going wrong in the differential expression testing step. Might it be possible for you to send me a subset of your data so that I can reproduce this issue on my machine? If so, I'll provide instructions for how to send the data.

All the best, Tim

[Yuanca9]

Hi Tim,

Thank you for your response.

Yes I can provide a subset of my data. Please let me know how to send it to you.

Best regards, Tsai

[timothy-barry]

Hi Tsai,

Please send me an R list (in .rds format) containing response_matrix, grna_matrix, grna_target_data_frame, moi, extra_covariates, and response_names. My email is tbarry@hsph.harvard.edu.

Tim