Trouble with mapping or reading out of mapping in AIT 11.7

[xpliu16]

I am having trouble using code adapted from code that successfully mapped to AIT 11.6 using docker://njjai/scrattch_mapping:0.52.2. Currently I'm trying to map to AIT 11.7 using docker://njjai/scrattch_mapping:0.6.6 (also tried 0.6.3 with same outcome). Currently HANN mapping is turned off because of compatibility issues with the patch-seq taxonomy.

suppressPackageStartupMessages({
  library("scrattch.mapping")
})
options(stringsAsFactors = FALSE)
options(future.globals.maxSize = 4000 * 1024^2)  # Can adjust this value if needed, depending on number of cells
options(future.rng.onMisuse="ignore")

library(scrattch.mapping)
library (dplyr)
library(stringr)
library(scrattch.patchseq)
library(reticulate)
cell_type_mapper <- import("cell_type_mapper")
#reticulate::use_python("/usr/bin/python3")

refFolder = "/allen/programs/celltypes/workgroups/rnaseqanalysis/HMBA/Aim1_Regional_Taxonomies/BasalGanglia/Macaque/" 
mappingFolder = "/home/xiaoping.liu/scrattch/mapping/NHP_BG_AIT_117"  
data_dir =  "/allen/programs/celltypes/workgroups/rnaseqanalysis/SMARTer/STAR/Macaque/patchseq/R_Object/" 
data_fn = "20241024_RSC-204-377_macaque_patchseq_star2.7" 
mode = 'patchseq'                                                                                  
h5ad_fn = "HMBA_Macaque_BG_082024_AIT.h5ad"
class_colname = 'Class_label' 
neigh_colname = 'Neighborhood_label' 
subclass_colname = 'Subclass_label'  
low_level = 'Group_label'
cluster_colname = 'Group_label'      # HACK because hierarchy is different, to match mouse whole brain
proj_strs = "qIVSCC-MET" 
roi_strs = "STR|PALGPi|PALGPe|PAL_GPe|HYSTN|OT_L"
off_target = c("Immune", "Astro-Epen", "Vascular", "OPC-Oligo")

if (is.null(h5ad_fn)) {
    AIT.anndata <- loadTaxonomy(refFolder)
} else {
    AIT.anndata <- loadTaxonomy(refFolder, h5ad_fn)
}
  
tryCatch({
    AIT.anndata = mappingMode(AIT.anndata, mode=mode)
}, error = function(err) {
    ## Add in the off.target annotation.
    AIT.anndata$obs['off_target'] = AIT.anndata$obs[class_colname]
    
    # Setup the taxonomy for patchseqQC to infer off.target contamination
    AIT.anndata = buildPatchseqTaxonomy(AIT.anndata,
                                        mode.name = mode, ## Give a name to off.target filterd taxonomy
                                        subsample = 100, ## Subsampling is only for PatchseqQC contamination calculation
                                        subclass.column = low_level, ## Used for subsampling - use lowest level above cluster
                                        class.column = class_colname, ## The column by which off-target types are determined
                                        off.target.types = off_target, ## The off-target class.column labels for patchseqQC
                                        num.markers = 50, ## Number of markers for each annotation in `class_label`
                                        taxonomyDir = refFolder)
    AIT.anndata = mappingMode(AIT.anndata, mode=mode)
}

load(paste0(data_dir, paste0(data_fn, "_cpm.Rdata")))
load(paste0(data_dir, paste0(data_fn, "_samp.dat.Rdata")))

query.metadata <- samp.dat
counts      <- cpmR   # Genes are rows, samples are columns
  
query.counts   <- counts
query.data   <- logCPM(query.counts)
  
# Put annotations and counts in the same order
query.metadata <- query.metadata[match(colnames(query.data),query.metadata$exp_component_name),] 
rownames(query.metadata) <- query.metadata$exp_component_name  
  
dend <- json_to_dend(AIT.anndata$uns$dend[[AIT.anndata$uns$mode]])
allMarkers = unique(unlist(get_dend_markers(dend)))

query.data = query.data[intersect(rownames(query.data), allMarkers),]

query.mapping_obj <- taxonomy_mapping(AIT.anndata= AIT.anndata,
                                        query.data = query.data, 
                                        corr.map   = TRUE, # Flags for which mapping algorithms to run
                                        tree.map   = TRUE, 
                                        hierarchical.map=FALSE,
                                        seurat.map = FALSE, 
                                        label.cols = c(neigh_colname, class_colname, subclass_colname, cluster_colname)
)
a <- strsplit(refFolder,'/')[[1]]
taxname <- a[length(a)]
b <- strsplit(data_fn, '_')[[1]]
dataname <- b[2]
  
save(query.mapping_obj, file=file.path(mappingFolder, paste(taxname, dataname, 'mapping.Rdata', sep='_')))
query.mapping = getMappingResults(query.mapping_obj)

At the bottom is where I get the error:

Error in (function (classes, fdef, mtable)  :
  unable to find an inherited method for function ‘getMappingResults’ for signature ‘"mappingClass", "missing"’
 
The rdata file is at: load(file = "/home/xiaoping.liu/scrattch/mapping/NHP_BG_AIT_117/Macaque_RSC-204-377_mapping.Rdata")