Change object class testing to use inherits or is.* instead of class

In R 4.X, matrices are now arrays
This means that class(matrix()) will be a two-length vector instead of one-length
if doesn't like two-length vectors, so using class becomes dangerous
Instead, we're using inherits or is.* functions to test object class

R/differential_expression.R

@@ -136,7 +136,7 @@ FindAllMarkers <- function(
         return(cond$message)
       }
     )
-    if (class(x = genes.de[[i]]) == "character") {
+    if (is.character(x = genes.de[[i]])) {
       messages[[i]] <- genes.de[[i]]
       genes.de[[i]] <- NULL
     }
@@ -233,7 +233,7 @@ FindConservedMarkers <- function(
   verbose = TRUE,
   ...
 ) {
-  if (class(x = meta.method) != "function") {
+  if (!is.function(x = meta.method)) {
     stop("meta.method should be a function from the metap package. Please see https://cran.r-project.org/web/packages/metap/metap.pdf for a detailed description of the available functions.")
   }
   object.var <- FetchData(object = object, vars = grouping.var)
@@ -1412,7 +1412,7 @@ NBModelComparison <- function(y, theta, latent.data, com.fac, grp.fac) {
     ),
     silent = TRUE
   )
-  if (class(x = fit2)[1] == 'numeric' | class(x = fit4)[1] == 'numeric') {
+  if (is.numeric(x = fit2) || is.numeric(x = fit4)) {
     message('One of the glm.nb calls failed')
     return(c(rep(x = NA, 5), freqs))
   }

R/objects.R

@@ -722,7 +722,7 @@ CreateSeuratObject <- function(
       warning("Some cells in meta.data not present in provided counts matrix.")
       meta.data <- meta.data[intersect(x = rownames(x = meta.data), y = colnames(x = counts)), ]
     }
-    if (class(x = meta.data) == "data.frame") {
+    if (is.data.frame(x = meta.data)) {
       new.meta.data <- data.frame(row.names = colnames(x = counts))
       for (ii in 1:ncol(x = meta.data)) {
         new.meta.data[rownames(x = meta.data), colnames(x = meta.data)[ii]] <- meta.data[, ii, drop = FALSE]
@@ -6224,7 +6224,7 @@ setMethod( # because R doesn't allow S3-style [[<- for S4 classes
         )
       }
       # Check keyed objects
-      if (class(x = value) %in% c('Assay', 'DimReduc')) {
+      if (inherits(x = value, what = c('Assay', 'DimReduc'))) {
         if (length(x = Key(object = value)) == 0) {
           Key(object = value) <- paste0(tolower(x = i), '_')
         } else if (!grepl(pattern = '^[[:alnum:]]+_$', x = Key(object = value))) {
@@ -6281,9 +6281,9 @@ setMethod( # because R doesn't allow S3-style [[<- for S4 classes
       }
       # For Assays, run CalcN
       if (inherits(x = value, what = 'Assay')) {
-        if ((!i %in% Assays(object = x)) | 
+        if ((!i %in% Assays(object = x)) |
             (i %in% Assays(object = x) && ! identical(
-              x = GetAssayData(object = x, assay = i, slot = "counts"), 
+              x = GetAssayData(object = x, assay = i, slot = "counts"),
               y = GetAssayData(object = value, slot = "counts"))
             )) {
           n.calc <- CalcN(object = value)

R/preprocessing.R

@@ -109,9 +109,9 @@ CalculateBarcodeInflections <- function(
     }
   )$x
   ## workaround for aggregate behavior noted above
-  if (class(x = whichmin_list) == 'list') { # uneven lengths
+  if (is.list(x = whichmin_list)) { # uneven lengths
     is_inflection <- unlist(x = whichmin_list)
-  } else if (class(x = whichmin_list) == 'matrix') { # even lengths
+  } else if (is.matrix(x = whichmin_list)) { # even lengths
     is_inflection <- as.vector(x = t(x = whichmin_list))
   }
   tmp <- cbind(barcode_dist_sub, is_inflection)
@@ -191,15 +191,15 @@ CreateGeneActivityMatrix <- function(
   peak.df <- as.data.frame(x = peak.df)
   colnames(x = peak.df) <- c("chromosome", 'start', 'end')
   peaks.gr <- GenomicRanges::makeGRangesFromDataFrame(df = peak.df)
-  
+
   # if any peaks start at 0, change to 1
-  # otherwise GenomicRanges::distanceToNearest will not work 
+  # otherwise GenomicRanges::distanceToNearest will not work
   BiocGenerics::start(peaks.gr[BiocGenerics::start(peaks.gr) == 0, ]) <- 1
 
   # get annotation file, select genes
   gtf <- rtracklayer::import(con = annotation.file)
   gtf <- GenomeInfoDb::keepSeqlevels(x = gtf, value = seq.levels, pruning.mode = 'coarse')
-  
+
   # change seqlevelsStyle if not the same
   if (!any(GenomeInfoDb::seqlevelsStyle(x = gtf) == GenomeInfoDb::seqlevelsStyle(x = peaks.gr))) {
     GenomeInfoDb::seqlevelsStyle(gtf) <- GenomeInfoDb::seqlevelsStyle(peaks.gr)
@@ -216,11 +216,11 @@ CreateGeneActivityMatrix <- function(
   keep.overlaps <- gene.distances[rtracklayer::mcols(x = gene.distances)$distance == 0]
   peak.ids <- peaks.gr[S4Vectors::queryHits(x = keep.overlaps)]
   gene.ids <- gtf.genes[S4Vectors::subjectHits(x = keep.overlaps)]
-  
+
   # Some GTF rows will not have gene_name attribute
   # Replace it by gene_id attribute
   gene.ids$gene_name[is.na(gene.ids$gene_name)] <- gene.ids$gene_id[is.na(gene.ids$gene_name)]
-  
+
   peak.ids$gene.name <- gene.ids$gene_name
   peak.ids <- as.data.frame(x = peak.ids)
   peak.ids$peak <- rownames(peak.matrix)[S4Vectors::queryHits(x = keep.overlaps)]
@@ -582,10 +582,10 @@ GetResidual <- function(
 #' mat_norm
 #'
 LogNormalize <- function(data, scale.factor = 1e4, verbose = TRUE) {
-  if (class(x = data) == "data.frame") {
+  if (is.data.frame(x = data)) {
     data <- as.matrix(x = data)
   }
-  if (class(x = data) != "dgCMatrix") {
+  if (!inherits(x = data, what = 'dgCMatrix')) {
     data <- as(object = data, Class = "dgCMatrix")
   }
   # call Rcpp function to normalize
@@ -1066,10 +1066,10 @@ Read10X_h5 <- function(filename, use.names = TRUE, unique.features = TRUE) {
 #' mat_norm
 #'
 RelativeCounts <- function(data, scale.factor = 1, verbose = TRUE) {
-  if (class(x = data) == "data.frame") {
+  if (is.data.frame(x = data)) {
     data <- as.matrix(x = data)
   }
-  if (class(x = data) != "dgCMatrix") {
+  if (!inherits(x = data, what = 'dgCMatrix')) {
     data <- as(object = data, Class = "dgCMatrix")
   }
   if (verbose) {
@@ -1419,10 +1419,10 @@ SubsetByBarcodeInflections <- function(object) {
 #' mat_norm <- TF.IDF(data = mat)
 #'
 TF.IDF <- function(data, verbose = TRUE) {
-  if (class(x = data) == "data.frame") {
+  if (is.data.frame(x = data)) {
     data <- as.matrix(x = data)
   }
-  if (class(x = data) != "dgCMatrix") {
+  if (!inherits(x = data, what = 'dgCMatrix')) {
     data <- as(object = data, Class = "dgCMatrix")
   }
   if (verbose) {
@@ -2502,7 +2502,6 @@ ClassifyCells <- function(data, q) {
         message("No threshold found for ", colnames(x = data)[i], "...")
       }
     )
-    # if (class(x = model) == "character") {
     if (is.character(x = model)) {
       next
     }
@@ -2569,10 +2568,10 @@ ClassifyCells <- function(data, q) {
 # @import Matrix
 #
 CustomNormalize <- function(data, custom_function, margin, verbose = TRUE) {
-  if (class(x = data) == "data.frame") {
+  if (is.data.frame(x = data)) {
     data <- as.matrix(x = data)
   }
-  if (class(x = data) != "dgCMatrix") {
+  if (!inherits(x = data, what = 'dgCMatrix')) {
     data <- as(object = data, Class = "dgCMatrix")
   }
   myapply <- ifelse(test = verbose, yes = pbapply, no = apply)
@@ -2782,7 +2781,7 @@ NBResiduals <- function(fmla, regression.mat, gene, return.mode = FALSE) {
       data = regression.mat
     ),
     silent = TRUE)
-  if (class(fit)[1] == 'numeric') {
+  if (is.numeric(x = fit)) {
     message(sprintf('glm.nb failed for gene %s; falling back to scale(log(y+1))', gene))
     resid <- scale(x = log(x = regression.mat[, 'GENE'] + 1))[, 1]
     mode <- 'scale'