Error in "rowscale_int" function #12
Description
Hi HiTMaP team,
Currently I have been running through your mouse brain example data. However, I encountered an error:
Codes I used:
fastafile <- 'uniprot_mouse_20210107.fasta'
datafile <- "Mouse_brain.imzML"
preprocess = list(force_preprocess=TRUE,
use_preprocessRDS=FALSE,
smoothSignal=list(method = "Disable"),
reduceBaseline=list(method = "locmin"),
peakPick=list(method= "mad"),
peakAlign=list(tolerance=5, units="ppm", level="local", method="Enable"))
imaging_identification(datafile=datafile,
Digestion_site="trypsin",
Fastadatabase="uniprot_mouse_20210107.fasta",
output_candidatelist=T,
preprocess=preprocess,
spectra_segments_per_file=9,
use_previous_candidates=F,
ppm=10,
FDR_cutoff = 0.05,
IMS_analysis=T,
mzrange = c(500,4000),
plot_cluster_image_grid=F)
Results:
16 Cores detected, 4 threads will be used for computing
1 files were selected and will be used for Searching
uniprot_mouse_20210107.fasta was selected as database. Candidates will be generated through Proteomics mode
Found enzyme: trypsin
Found rule: ""
Found customized rule: ""
Testing fasta sequances for degestion site: (KR)|((?<=W)K(?=P))|((?<=M)R(?=P))
Generated 17080 Proteins in total. Computing exact masses...
Generating peptide formula...
Generating peptide formula with adducts: M+H
Calculating peptide mz with adducts: M+H
Candidate list has been exported.
uniprot_mouse_20210107.fasta was selected as database
Spectrum intensity threshold: 0.10%
mz tolerance: 10 ppm Segmentation method: spatialKMeans
Manual segmentation def file: None
Bypass spectrum generation: FALSE
Found rotation info
Loading raw image data for statistical analysis: Mouse_brain.imzML
parsing imzML file: ‘Z:\Data\peptide_HCCA\HiTMaP_analysis\Data_tar\MouseBrain_Trypsin_FT\Mouse_brain.imzML’
detected 'processed' imzML
creating MSImagingExperiment
applying profile m/z-values to all spectra
using mass.range 500 to 4000
using resolution 5 ppm
binning intensity from mz 500 to 3999.9938 with relative resolution 5e-06
returning MSImagingExperiment
done.
Preparing image data for statistical analysis: Mouse_brain.imzML
queued: baseline reduction
queued: baseline reduction, height peak picking
processing: baseline reduction, height peak picking
|==============================================================================================================================================================| 100%
output spectra: intensity
Using image data: Mouse_brain.imzML
parsing imzML file: ‘Z:\Data\peptide_HCCA\HiTMaP_analysis\Data_tar\MouseBrain_Trypsin_FT\Mouse_brain.imzML’
detected 'processed' imzML
creating MSImagingExperiment
applying profile m/z-values to all spectra
using mass.range 500 to 4000
using resolution 5 ppm
binning intensity from mz 500 to 3999.9938 with relative resolution 5e-06
returning MSImagingExperiment
done.
Segmentation in progress...
Performing forced peak alignment before segmentation...
preprocess$peakAlign$tolerance set as 5
detected ~0 peaks per spectrum
binning peaks to create shared reference
|==============================================================================================================================================================| 100%
aligned to 8 reference peaks with relative tolerance 5e-06 (5 ppm)
centering data matrix
|==============================================================================================================================================================| 100%
Error in rowscale_int(x, center = center, scale = scale, group = group, :
length of 'center' must be equal to nrow of x
In addition: Warning message:
In .local(object, ...) : '.local' is deprecated.
Use 'subsetFeatures' instead.
See help("Deprecated")
traceback()
16: stop("length of 'center' must be equal to nrow of x")
15: rowscale_int(x, center = center, scale = scale, group = group,
..., BPPARAM = BPPARAM)
14: .local(x, center, scale, ...)
13: rowscale(x, center = center, scale = scale., verbose = verbose,
nchunks = nchunks, BPPARAM = BPPARAM)
12: rowscale(x, center = center, scale = scale., verbose = verbose,
nchunks = nchunks, BPPARAM = BPPARAM)
11: prcomp_lanczos(x, k = max(ncomp), center = center, scale. = scale,
nchunks = nchunks, verbose = verbose, BPPARAM = BPPARAM,
...)
10: .local(x, ...)
9: PCA(spectra(x), ncomp = ncomp, transpose = TRUE, center = center,
scale = scale, ...)
8: PCA(spectra(x), ncomp = ncomp, transpose = TRUE, center = center,
scale = scale, ...)
7: .local(x, ...)
6: Cardinal::PCA(imdata, ncomp = 12)
5: Cardinal::PCA(imdata, ncomp = 12)
4: PCA_ncomp_selection(imdata_stats, variance_coverage = Segmentation_variance_coverage,
outputdir = paste0(getwd(), "/"))
3: Preprocessing_segmentation(datafile = datafile[z], workdir = workdir[z],
segmentation_num = segmentation_num, ppm = ppm, import_ppm = import_ppm,
Bypass_Segmentation = Bypass_Segmentation, mzrange = mzrange,
Segmentation = Segmentation, Segmentation_def = Segmentation_def,
Segmentation_ncomp = Segmentation_ncomp, Segmentation_variance_coverage = Segmentation_variance_coverage,
Smooth_range = Smooth_range, colorstyle = colorstyle, Virtual_segmentation_rankfile = Virtual_segmentation_rankfile,
rotate = rotate, BPPARAM = BPPARAM, preprocess = preprocess)
2: IMS_data_process(datafile = datafile, workdir = workdir, Peptide_Summary_searchlist = Peptide_Summary_searchlist,
segmentation_num = spectra_segments_per_file, threshold = threshold,
rotate = Rotate_IMG, ppm = ppm, mzrange = mzrange, Segmentation = Segmentation,
Segmentation_ncomp = Segmentation_ncomp, PMFsearch = PMFsearch,
Virtual_segmentation_rankfile = Virtual_segmentation_rankfile,
BPPARAM = BPPARAM, Bypass_generate_spectrum = Bypass_generate_spectrum,
score_method = score_method, Decoy_mode = Decoy_mode, Decoy_search = Decoy_search,
adjust_score = adjust_score, peptide_ID_filter = peptide_ID_filter,
Protein_desc_of_interest = Protein_desc_of_interest, plot_matching_score_t = plot_matching_score,
FDR_cutoff = FDR_cutoff, Segmentation_def = Segmentation_def,
Segmentation_variance_coverage = Segmentation_variance_coverage,
preprocess = preprocess)
1: imaging_identification(datafile = datafile, Digestion_site = "trypsin",
Fastadatabase = "uniprot_mouse_20210107.fasta", output_candidatelist = T,
preprocess = preprocess, spectra_segments_per_file = 9, use_previous_candidates = F,
ppm = 10, FDR_cutoff = 0.05, IMS_analysis = T, mzrange = c(500,
4000), plot_cluster_image_grid = F)
Could you please provide some clues about how to solve it? Thanks.
Best,
Shuo