Project: s41467-024-45240-z | Function: inconsistent_anchors (r)

system: You are an expert R programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow R best practices and maintain consistent code style.
Return **only** the complete R function code.

user: Complete this R function:

**DESCRIPTION:** Implements semi-supervised anchor filtering by removing inconsistent anchors between cells with different cell type labels. Core innovation allowing STACAS to use prior knowledge while handling incomplete annotations.

**KEY TERMS:** {
  "anchors": "anchor set object",
  "cell.labels": "metadata column with cell type annotations",
  "label.confidence": "probability of accepting inconsistent anchors",
  "Consistent": "boolean flag for anchor label consistency"
}

**CONTEXT CODE:** # Logistic transformation
logistic <- function(x,scale = NULL, center = NULL, invert = F){
  a <- ifelse(invert,1, -1)
  if(is.null(scale))  scale = sd(x)
  if(is.null(center)) center = mean(x)
  sigm <- 1/(1+ exp(a*(x-center)/scale))
  return(sigm)    
}

# Mark anchor as inconsistent based on cell-type labeling 
# Anchors will be consider as inconsistent if they are linking cells classified as different cell populations. 
inconsistent_anchors <- function(anchors, seed=123,
                                 cell.labels=NULL,
                                 label.confidence=1,
                                 quantile_ss=0){
  stop("Function implementation has been masked")
  # Check if cell.labels is provided
  if(is.null(cell.labels)) {
    message("No cell labels provided. Returning anchors as is.")
    anchors$Consistent <- TRUE
    return(anchors)
  }

  # Set seed for reproducibility
  set.seed(seed)

  # Extract anchor information
  anchor_df <- anchors@anchors

  # Check if required columns exist
  required_cols <- c("cell1", "cell2", "dataset1", "dataset2")
  if(!all(required_cols %in% colnames(anchor_df))) {
    stop("Anchor data frame must contain columns: cell1, cell2, dataset1, dataset2")
  }

  # Get unique datasets
  datasets <- unique(c(anchor_df$dataset1, anchor_df$dataset2))

  # Check if cell.labels contains all datasets
  if(!all(datasets %in% names(cell.labels))) {
    stop("cell.labels must contain all datasets present in anchors")
  }

  # Initialize Consistent column
  anchor_df$Consistent <- NA

  # For each anchor, check if cell labels match
  for(i in 1:nrow(anchor_df)) {
    cell1_label <- cell.labels[[anchor_df$dataset1[i]]][anchor_df$cell1[i]]
    cell2_label <- cell.labels[[anchor_df$dataset2[i]]][anchor_df$cell2[i]]

    # If labels are the same or either is NA, mark as consistent
    if(is.na(cell1_label) | is.na(cell2_label) | cell1_label == cell2_label) {
      anchor_df$Consistent[i] <- TRUE
    } else {
      anchor_df$Consistent[i] <- FALSE
    }
  }

  # Apply probabilistic rejection for inconsistent anchors
  if(label.confidence < 1) {
    anchor_df <- probabilistic_reject(anchor_df,
                                      accept_rate = label.confidence,
                                      q = quantile_ss,
                                      seed = seed)
  } else {
    anchor_df$Retain_ss <- anchor_df$Consistent
  }

  # Update anchors object
  anchors@anchors <- anchor_df

  return(anchors)
}

#Re-weight anchors based on combined seurat anchor score and rPCA distance
reweight_anchors <- function(ref.anchors, alpha=0.8,
                                 dist.pct = 0.5,
                                 dist.scale.factor = 2)
{
  df <- ref.anchors@anchors
  eps <- 10^(-6) #small number to avoid log(0)
  
  dist.mean.center = quantile(df$dist.mean,dist.pct)
  dist.mean.scale = sd(df$dist.mean,na.rm = T)/dist.scale.factor
  
  squash <- logistic(x = df$dist.mean, invert = T, 
                     center = dist.mean.center, 
                     scale = dist.mean.scale)
  
  #weighted geometric mean
  logsum = alpha*log(squash + eps) + (1-alpha)*log(df$knn.score + eps)
  df$score <- exp(logsum) - eps
  
  ref.anchors@anchors <- df
  return(ref.anchors)
}

# Probabilistic rejection of inconsistent anchors
# accept_rate: probability of rejecting anchors when labels are inconsistent
# q: anchor score quantile above which probabilistic rejection applies
probabilistic_reject <- function(anchors, accept_rate=0, q=0, seed=seed){
  
  set.seed(seed)
  
  e0 <- quantile(anchors$score, q)
  pos <- anchors[anchors$Consistent==FALSE, "score"] - e0
  bin <- as.numeric(pos > 0)
  
  #Randomly accept anchors with accept_rate probability above quantile=q
  accept <- bin*accept_rate > runif(length(bin), 0, 1)


Complete the function implementation replacing "stop("Not implemented")".