Error in "PCA" function

[SA-coder-netizen]

Hi HiTMaP team,
I was trying to run the code using my data and encountered an error not sure how to fix it. any help would be greatly appreciated.
my code
preprocess = list(force_preprocess=TRUE,
use_preprocessRDS=FALSE,
smoothSignal=list(method = c("Disable", "gaussian", "sgolay", "ma")[1]),
reduceBaseline=list(method = c("Disable", "locmin", "median")[1]),
peakPick=list(method=c("diff", "sd", "mad", "quantile", "filter", "cwt")[3]),
peakAlign=list(tolerance=5, units="ppm", level=c("local","global")[1], method=c("Enable","Disable")[1]),
normalize=list(method=c("Disable","rms","tic","reference")[1], mz=NULL)
)

imaging_identification(
#==============Choose the imzml raw data file(s) to process make sure the fasta file in the same folder
datafile = datafile,
threshold=0.005,
ppm=5,
FDR_cutoff = 0.05,
#==============specify the digestion enzyme specificity
Digestion_site="trypsin",
#==============specify the range of missed Cleavages
missedCleavages=0:1,
#==============Set the target fasta file
Fastadatabase="uniprotkb_proteome_UP000005640_AND_revi_2024_11_07.fasta",
#==============Set the possible adducts and fixed modifications
adducts=c("M+H"),
Modifications=list(fixed=NULL,fixmod_position=NULL,variable=NULL,varmod_position=NULL),
#==============The decoy mode: could be one of the "adducts", "elements" or "isotope"
Decoy_mode = "isotope",
use_previous_candidates=TRUE,
output_candidatelist=T,
#==============The pre-processing param
preprocess=preprocess,
#==============Set the parameters for image segmentation
spectra_segments_per_file=9,
Segmentation="spatialKMeans",
Smooth_range=1,
Virtual_segmentation=FALSE,
Virtual_segmentation_rankfile=NULL,
#==============Set the Score method for hi-resolution isotopic pattern matching
score_method="SQRTP",
peptide_ID_filter=2,
#==============Summarise the protein and peptide features across the project the result can be found at the summary folder
Protein_feature_summary=TRUE,
Peptide_feature_summary=TRUE,
Region_feature_summary=TRUE,
#==============The parameters for Cluster imaging. Specify the annotations of interest, the program will perform a case-insensitive search on the result file, extract the protein(s) of interest and plot them in the cluster imaging mode
plot_cluster_image_grid=TRUE,
ClusterID_colname="Protein",
componentID_colname="Peptide",
Protein_desc_of_interest= ".",
Rotate_IMG=NULL,
)
This is the output
4 Cores detected, 4 threads will be used for computing

1 files were selected and will be used for Searching

uniprotkb_proteome_UP000005640_AND_revi_2024_11_07.fasta was selected as database. Candidates will be generated through Proteomics mode

Found enzyme: trypsin

Found rule: ""

Found customized rule: ""

Candidate list has been loaded.

uniprotkb_proteome_UP000005640_AND_revi_2024_11_07.fasta was selected as database
Spectrum intensity threshold: 0.50%
mz tolerance: 5 ppm Segmentation method: spatialKMeans
Manual segmentation def file: None
Bypass spectrum generation: FALSE

Found rotation info

Loading raw image data for statistical analysis: 20241023-sharat-52059-dhb-500shot.imzML

Preparing image data for statistical analysis: 20241023-sharat-52059-dhb-500shot.imzML

|======================================================================| 100%

Warning message:
“no pending processing steps to apply”
Using image data: 20241023-sharat-52059-dhb-500shot.imzML

Segmentation in progress...

Performing forced peak alignment before segmentation...

preprocess$peakAlign$tolerance set as 5

|======================================================================| 100%

|======================================================================| 100%

Error in data.frame(Component = 1:length(PCA_imdata@model[["sdev"]]), : no slot of name "model" for this object of class "PCA2"
Traceback:

1. 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)
2. 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)
3. PCA_ncomp_selection(imdata_stats, variance_coverage = Segmentation_variance_coverage,
   . outputdir = paste0(getwd(), "/"))
4. data.frame(Component = 1:length(PCA_imdata@model[["sdev"]]),
   . Standard.deviation = PCA_imdata@model[["sdev"]])
5. .handleSimpleError(function (cnd)
   . {
   . watcher$capture_plot_and_output()
   . cnd <- sanitize_call(cnd)
   . watcher$push(cnd)
   . switch(on_error, continue = invokeRestart("eval_continue"),
   . stop = invokeRestart("eval_stop"), error = invokeRestart("eval_error",
   . cnd))
   . }, "no slot of name "model" for this object of class "PCA2"",
   . base::quote(data.frame(Component = 1:length(PCA_imdata@model[["sdev"]]),
   . Standard.deviation = PCA_imdata@model[["sdev"]])))