Running ‘testthat.R’ [62s/99s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > library(Seurat)
  Loading required package: ggplot2
  Loading required package: cowplot
  
  Attaching package: 'cowplot'
  
  The following object is masked from 'package:ggplot2':
  
      ggsave
  
  Loading required package: Matrix
  > 
  > test_check("Seurat")
  Running CCA
  Merging objects
  Performing log-normalization
  0%   10   20   30   40   50   60   70   80   90   100%
  [----|----|----|----|----|----|----|----|----|----|
  **************************************************|
  
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    |======================================================================| 100%
  Rescaling group 1
  
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    |======================================================================| 100%
  Rescaling group 2
  
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    |======================================================================| 100%
  Aligning dimension 1
  Aligning dimension 2
  Aligning dimension 3
  Aligning dimension 4
  Aligning dimension 5
  Calculating for group1 
  	 Separating group1 cells
  	 Running Dimensional Reduction 
  Calculating for group2 
  	 Separating group2 cells
  	 Running Dimensional Reduction 
  [1] "Computing CC 1"
  Performing log-normalization
  0%   10   20   30   40   50   60   70   80   90   100%
  [----|----|----|----|----|----|----|----|----|----|
  **************************************************|
  
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    |======================================================================| 100%
  Computing nearest neighbor graph
  Computing SNN
  ── 1. Failure: Row merging done correctly (@test_data_manipulation.R#22)  ──────
  m3[1, 14] not equal to -0.17.
  1/1 mismatches
  [1] 0 - -0.17 == 0.17
  
  ── 2. Failure: Row merging done correctly (@test_data_manipulation.R#23)  ──────
  m3[3, 2] not equal to -1.4.
  1/1 mismatches
  [1] 0 - -1.4 == 1.4
  
  ── 3. Error: Row merging done correctly (@test_data_manipulation.R#24)  ────────
  index larger than maximal 13
  1: expect_equal(m3[14, 18], -0.43) at testthat/test_data_manipulation.R:24
  2: quasi_label(enquo(object), label)
  3: eval_bare(get_expr(quo), get_env(quo))
  4: m3[14, 18]
  5: m3[14, 18]
  6: callGeneric(x, i = i, j = j, drop = TRUE)
  7: eval(call, parent.frame())
  8: eval(call, parent.frame())
  9: x[i = i, j = j, drop = TRUE]
  10: x[i = i, j = j, drop = TRUE]
  11: subCsp_ij(x, i, j, drop = drop)
  12: intI(i, n = d[1], dn[[1]], give.dn = FALSE)
  13: stop(gettextf("index larger than maximal %d", n), domain = NA)
  
  ── 4. Failure: max.cells.per.ident downsamples properly (@test_interaction.R#70)
  x[1] not equal to "GCAGCTCTGTTTCT".
  1/1 mismatches
  x[1]: "GGCCGATGTACTCT"
  y[1]: "GCAGCTCTGTTTCT"
  
  Performing log-normalization
  0%   10   20   30   40   50   60   70   80   90   100%
  [----|----|----|----|----|----|----|----|----|----|
  **************************************************|
  
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    |======================================================================| 100%
  
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    |======================================================================| 100%
  
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    |======================================================================| 100%
  
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    |======================================================================| 100%
  Calculating gene means
  0%   10   20   30   40   50   60   70   80   90   100%
  [----|----|----|----|----|----|----|----|----|----|
  **************************************************|
  Calculating gene variance to mean ratios
  0%   10   20   30   40   50   60   70   80   90   100%
  [----|----|----|----|----|----|----|----|----|----|
  **************************************************|
  Performing log-normalization
  0%   10   20   30   40   50   60   70   80   90   100%
  [----|----|----|----|----|----|----|----|----|----|
  **************************************************|
  NormalizeData has not been run, therefore ScaleData is running on non-normalized values. Recommended workflow is to run NormalizeData first.
  ScaleData is running on non-normalized values. Recommended workflow is to run NormalizeData first.
  
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    |======================================================================| 100%
  Calculating gene means
  0%   10   20   30   40   50   60   70   80   90   100%
  [----|----|----|----|----|----|----|----|----|----|
  **************************************************|
  Calculating gene variance to mean ratios
  0%   10   20   30   40   50   60   70   80   90   100%
  [----|----|----|----|----|----|----|----|----|----|
  **************************************************|
  ── 5. Error: tSNE plots correctly (@test_seurat_object.R#114)  ─────────────────
  VECTOR_ELT() can only be applied to a 'list', not a 'double'
  1: TSNEPlot(object = nbt.test) at testthat/test_seurat_object.R:114
  2: DimPlot(object = object, reduction.use = "tsne", cells.use = cells.use, pt.size = pt.size, 
         do.label = do.label, label.size = label.size, cols.use = colors.use, ...)
  3: print(p3)
  4: print(p3)
  5: print.ggplot(p3)
  6: ggplot_gtable(data)
  7: ggplot_gtable.ggplot_built(data)
  8: build_guides(plot$scales, plot$layers, plot$mapping, position, theme, plot$guides, 
         plot$labels)
  9: guides_gengrob(gdefs, theme)
  10: lapply(gdefs, guide_gengrob, theme)
  ...
  21: pushvpgp(x)
  22: pushgrobvp(x$vp)
  23: pushgrobvp.viewport(x$vp)
  24: pushViewport(vp, recording = FALSE)
  25: lapply(vps, push.vp, recording)
  26: FUN(X[[i]], ...)
  27: push.vp.vpTree(X[[i]], ...)
  28: push.vp(vp$parent, recording)
  29: push.vp.viewport(vp$parent, recording)
  30: grid.Call.graphics(C_setviewport, vp, TRUE)
  
  ── 6. Error: FeaturePlot works as expected (@test_seurat_object.R#144)  ────────
  VECTOR_ELT() can only be applied to a 'list', not a 'double'
  1: expect_is(FeaturePlot(nbt.test, "DLX1"), "NULL") at testthat/test_seurat_object.R:144
  2: quasi_label(enquo(object), label)
  3: eval_bare(get_expr(quo), get_env(quo))
  4: FeaturePlot(nbt.test, "DLX1")
  5: print(x = cowplot::plot_grid(plotlist = pList, ncol = nCol))
  6: print(x = cowplot::plot_grid(plotlist = pList, ncol = nCol))
  7: print.ggplot(x = cowplot::plot_grid(plotlist = pList, ncol = nCol))
  8: grid.draw(gtable)
  9: grid.draw.gTree(gtable)
  10: recordGraphics(drawGTree(x), list(x = x), getNamespace("grid"))
  11: drawGTree(x)
  12: preDraw(x)
  13: preDraw.gTree(x)
  14: pushvpgp(x)
  15: pushgrobvp(x$vp)
  16: pushgrobvp.viewport(x$vp)
  17: pushViewport(vp, recording = FALSE)
  18: lapply(vps, push.vp, recording)
  19: FUN(X[[i]], ...)
  20: push.vp.viewport(X[[i]], ...)
  21: grid.Call.graphics(C_setviewport, vp, TRUE)
  
  Running CCA
  Merging objects
  Performing log-normalization
  0%   10   20   30   40   50   60   70   80   90   100%
  [----|----|----|----|----|----|----|----|----|----|
  **************************************************|
  
    |                                                                            
    |                                                                      |   0%
    |                                                                            
    |======================================================================| 100%
  Running CCA
  ── 7. Failure: CCA returns the expected cell.embeddings matrix values when run o
  abs(unname(c3@dr$cca@cell.embeddings[1, 1])) not equal to 0.3108733.
  1/1 mismatches
  [1] 0.534 - 0.311 == 0.224
  
  ── 8. Failure: CCA returns the expected cell.embeddings matrix values when run o
  abs(unname(c3@dr$cca@cell.embeddings[14, 3])) not equal to 0.6064297.
  1/1 mismatches
  [1] 0.404 - 0.606 == -0.202
  
  ── 9. Failure: CCA returns the expected cell.embeddings matrix values when run o
  abs(unname(c4@dr$cca@cell.embeddings[1, 1])) not equal to 0.3108733.
  1/1 mismatches
  [1] 0.534 - 0.311 == 0.224
  
  ── 10. Failure: CCA returns the expected cell.embeddings matrix values when run 
  abs(unname(c4@dr$cca@cell.embeddings[14, 3])) not equal to 0.6064297.
  1/1 mismatches
  [1] 0.404 - 0.606 == -0.202
  
  Computing nearest neighbor graph
  Computing SNN
  ══ testthat results  ═══════════════════════════════════════════════════════════
  OK: 341 SKIPPED: 0 FAILED: 10
  1.  Failure: Row merging done correctly (@test_data_manipulation.R#22) 
  2.  Failure: Row merging done correctly (@test_data_manipulation.R#23) 
  3.  Error: Row merging done correctly (@test_data_manipulation.R#24) 
  4.  Failure: max.cells.per.ident downsamples properly (@test_interaction.R#70) 
  5.  Error: tSNE plots correctly (@test_seurat_object.R#114) 
  6.  Error: FeaturePlot works as expected (@test_seurat_object.R#144) 
  7.  Failure: CCA returns the expected cell.embeddings matrix values when run on two objects (@test_seurat_object.R#228) 
  8.  Failure: CCA returns the expected cell.embeddings matrix values when run on two objects (@test_seurat_object.R#229) 
  9.  Failure: CCA returns the expected cell.embeddings matrix values when run on single object (@test_seurat_object.R#234) 
  10. Failure: CCA returns the expected cell.embeddings matrix values when run on single object (@test_seurat_object.R#235) 
  
  Error: testthat unit tests failed
  Execution halted