Error in *tmp*@metadata : no applicable method for @ applied to an object of class "NULL"

[Flu09]

I would appreciate it if you can help me. Not sure how to fix it. 

> tensor <- liana_tensor_c2c(context_df_dict = context_df_dict,
+                            sender_col = "source", # Column name of the sender cells
+                            receiver_col = "target", # Column name of the receiver cells
+                            ligand_col = "ligand.complex", # Column name of the ligands
+                            receptor_col = "receptor.complex", # Column name of the receptors
+                            score_col = 'magnitude_rank', # Column name of the communication scores to use
+                            how='outer',  # What to include across all samples
+                            lr_fill = NaN, # What to fill missing LRs with
+                            cell_fill = NaN, # What to fill missing cell types with
+                            lr_sep='^', # How to separate ligand and receptor names to name LR pair
+                            sort_elements = FALSE, # Whether sorting alphabetically element names of each tensor dim. Does not apply for context order if context_order is passed.
+                            use_available = TRUE,
+                            build_only = FALSE, # set this to FALSE to combine the downstream rank selection and decomposition steps all here
+ )
Building the tensor using magnitude_rank...
100%|██████████| 1/1 [00:03<00:00,  3.65s/it]Estimating ranks...

100%|██████████| 25/25 [09:01<00:00, 21.65s/it]Decomposing the tensor...
The rank at the elbow is: 6

Error in `*tmp*`@metadata : 
  no applicable method for `@` applied to an object of class "NULL"


> context_df_dict
$`4`
# A tibble: 18,919 × 6
   source                          target                          ligand.complex receptor.complex magnitude_rank dataset
   <chr>                           <chr>                           <chr>          <chr>                     <dbl> <chr>  
 1 Oligodendrocytes                Immature neurons                IL1RAPL1       PTPRD                  4.43e-13 4      
 2 Oligodendrocyte precursor cells GABAergic neurons               NLGN1          NRXN3                  6.92e-12 4      
 3 GABAergic neurons               Oligodendrocyte precursor cells NRXN3          NLGN1                  6.92e-12 4      
 4 Oligodendrocytes                Unknown                         IL1RAPL1       PTPRD                  2.84e-11 4      
 5 Oligodendrocytes                Mature neurons                  IL1RAPL1       PTPRD                  5.54e-11 4      
 6 Oligodendrocyte precursor cells Mature neurons                  NLGN1          NRXN3                  1.22e-10 4      
 7 Mature neurons                  Oligodendrocyte precursor cells NRXN3          NLGN1                  1.22e-10 4      
 8 Oligodendrocyte precursor cells Unknown                         NLGN1          NRXN3                  2.72e-10 4      
 9 Unknown                         Oligodendrocyte precursor cells NRXN3          NLGN1                  2.72e-10 4      
10 Oligodendrocyte precursor cells Oligodendrocyte precursor cells NRXN1          NLGN1                  5.13e-10 4      
# ℹ 18,909 more rows
# ℹ Use `print(n = ...)` to see more rows

[dbdimitrov]

Hi @Flu09,

I assume this error happens because you have set build_only to FALSE, and inplace is by default set to TRUE (yet you have no sce object). I can fix the logic in a following update, but this should work if you set build_only to TRUE and follow the rest of the tutorial:

https://ccc-protocols.readthedocs.io/en/latest/notebooks/ccc_R/QuickStart.html

[Flu09]

Thank you so much. I have a couple of questions if you have time. I am not sure why there is only one tensor dimesion, while there should be 4 dimensions. The liana_wrap should iterate over objects of different conditions or different samples or different datasets? Also, I am wondering how to proceed given than I did the following. Should I convert my seurat merged object into a sce to proceed as many plots necessitate the presence of a sce object.

context_df_dict<-list()
for (samples.name in sort(unique([email protected]$sample))){
  sdata_ = subset(x = mergedsamples, subset = sample == samples.name)
  sdata.sce <- Seurat::as.SingleCellExperiment(sdata_)
  liana_res_ <- liana_wrap(sce = sdata.sce,
                           idents_col='celltype',
                           assay="RNA", # specify the Seurat assay
                           assay.type = 'logcounts',
                           expr_prop=0.1,
                           verbose=T)#, parallelize = T, workers = 30)
  
  liana_aggregate.magnitude_ <- liana_aggregate(liana_res = liana_res_, aggregate_how='magnitude', verbose = F)
  
  # retain only the aggregate magnitude rank score
  # and format for input to liana_tensor_c2c function
  liana_aggregate.magnitude_<-liana_aggregate.magnitude_[1:5]
  colnames(liana_aggregate.magnitude_)<-c('source', 'target', 'ligand.complex', 'receptor.complex', 'magnitude_rank')
  liana_aggregate.magnitude_[['sample']] <- samples.name
  
  context_df_dict[[samples.name]] <-liana_aggregate.magnitude_
}




#we will apply some additional preprocessing (transformations/filtering) to the communication scores.
context_df_dict <- liana:::preprocess_scores(context_df_dict = context_df_dict,
                                             outer_frac = 1/3, # Fraction of samples as threshold to include cells and LR pairs.
                                             invert = TRUE, # transform the scores
                                             invert_fun = function(x) 1-x, # Transformation function
                                             non_negative = TRUE, # fills negative values
                                             non_negative_fill = 0 # set negative values to 0
)


tensor <- liana_tensor_c2c(context_df_dict = context_df_dict,
                           sender_col = "source", # Column name of the sender cells
                           receiver_col = "target", # Column name of the receiver cells
                           ligand_col = "ligand.complex", # Column name of the ligands
                           receptor_col = "receptor.complex", # Column name of the receptors
                           score_col = 'magnitude_rank', # Column name of the communication scores to use
                           how='outer',  # What to include across all samples
                           lr_fill = NaN, # What to fill missing LRs with
                           cell_fill = NaN, # What to fill missing cell types with
                           lr_sep='^', # How to separate ligand and receptor names to name LR pair
                           sort_elements = FALSE, # Whether sorting alphabetically element names of each tensor dim. Does not apply for context order if context_order is passed.
                           use_available = TRUE,
                           build_only = TRUE, # set this to FALSE to combine the downstream rank selection and decomposition steps all here
)

#Building the tensor using magnitude_rank...
#100%|██████████| 4/4 [00:10<00:00,  2.52s/it]

#This indicates the number of elements in each tensor dimension: (Contexts, LR pairs, Sender cells, Receiver cells)
tensor$shape
#[[1]]
#[1] 4

#[[2]]
#[1] 918

#[[3]]
#[1] 10

#[[4]]
#[1] 10

#This represents the fraction of values that are missing. In this case, missing values are combinations of contexts x LR pairs x Sender cells x Receiver cells that did not have a communication score or were missing in the dataframes.
tensor$missing_fraction()
#[1] 0.8852206

#This represents the fraction of values that are a real zero (excluding the missing values)
tensor$sparsity_fraction()
#[1] 0.0369744

#fraction of excluded elements
tensor$excluded_value_fraction() # Percentage of values in the tensor that are masked/missing
#[1] 0.8852206

tensor <- liana::decompose_tensor(tensor = tensor,
                                  rank = NULL, # Number of factors to perform the factorization. If NULL, it is automatically determined by an elbow analysis
                                  tf_optimization = 'robust', # To define how robust we want the analysis to be.
                                  seed = 0, # Random seed for reproducibility
                                  elbow_metric = 'error', # Metric to use in the elbow analysis
                                  smooth_elbow = FALSE, # Whether smoothing the metric of the elbow analysis
                                  upper_rank=25, # Max number of factors to try in the elbow analysis
                                  init = 'random', # Initialization method of the tensor factorization
                                  svd = 'numpy_svd', # Type of SVD to use if the initialization is 'svd'
                                  factors_only = FALSE,
)

# Estimate standard error
error_average <- tensor$elbow_metric_raw %>%
    t() %>%
    as.data.frame() %>%
    mutate(rank=row_number()) %>%
    pivot_longer(-rank, names_to = "run_no", values_to = "error") %>%
    group_by(rank) %>%
    summarize(average = mean(error),
              N = n(),
              SE.low = average - (sd(error)/sqrt(N)),
              SE.high = average + (sd(error)/sqrt(N))
           )

# plot
error_average %>%
    ggplot(aes(x=rank, y=average), group=1) +
    geom_line(col='red') +
    geom_ribbon(aes(ymin = SE.low, ymax = SE.high), alpha = 0.1) +
    geom_vline(xintercept = tensor$rank, colour='darkblue') + # rank of interest
    theme_bw() +
    labs(y="Error", x="Rank")

[dbdimitrov]

Hi @Flu09,

I am not sure why there is only one tensor dimension,

not sure if I understand how your tensor is 1 dimension, as your tensor$shape print shows a list of 4 dimensions.

Should I convert my seurat merged object into a sce to proceed as many plots necessitate the presence of a sce object.

Yes, it's best to convert it (LIANA converts it internally either way), you can use the Seurat::as.SingleCellExperiment function.

In general, I suggest sticking to the tutorials that we provide here: https://ccc-protocols.readthedocs.io/en/latest/notebooks/ccc_R/01-Preprocess-Expression.html

Note that we also show how to convert between objects, etc.

Hope this helps.