Transcription Profiling of 57 Human Bladder Carcinoma Samples

Transcriptome analysis of 57 bladder carcinomas on Affymetrix HG-U95A and HG-U95Av2 microarrays

Usage

transcriptome

Format

A data frame with 2215 rows and 43 variables:

3: Individual 3
4: Individual 4
5: Individual 5
6: Individual 6
7: Individual 7
8: Individual 8
9: Individual 9
10: Individual 10
14: Individual 14
15: Individual 15
16: Individual 16
17: Individual 17
18: Individual 18
19: Individual 19
21: Individual 21
22: Individual 22
24: Individual 24
26: Individual 26
28: Individual 28
30: Individual 30
31: Individual 31
33: Individual 33
34: Individual 34
35: Individual 35
36: Individual 36
37: Individual 37
38: Individual 38
39: Individual 39
40: Individual 40
41: Individual 41
42: Individual 42
43: Individual 43
44: Individual 44
45: Individual 45
46: Individual 46
47: Individual 47
48: Individual 48
49: Individual 49
50: Individual 50
51: Individual 51
53: Individual 53
54: Individual 54
57: Individual 57

Source

<https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-TABM-147>

<https://github.com/cran/ecp/tree/master/data>

Examples

# \donttest{
if (requireNamespace("ggplot2", quietly = TRUE)) {
  result <- fastcpd.mean(transcriptome$"10", trim = 0.005)
  summary(result)
  plot(result)

  result_all <- fastcpd.mean(
    transcriptome,
    beta = (ncol(transcriptome) + 1) * log(nrow(transcriptome)) / 2 * 5,
    trim = 0
  )

  plots <- lapply(
    seq_len(ncol(transcriptome)), function(i) {
      ggplot2::ggplot(
        data = data.frame(
          x = seq_along(transcriptome[, i]), y = transcriptome[, i]
        ),
        ggplot2::aes(x = x, y = y)
      ) +
        ggplot2::geom_line(color = "steelblue") +
        ggplot2::geom_vline(
          xintercept = result_all@cp_set,
          color = "red",
          linetype = "dotted",
          linewidth = 0.5,
          alpha = 0.7
        ) +
        ggplot2::theme_void()
    }
  )

  if (requireNamespace("gridExtra", quietly = TRUE)) {
    gridExtra::grid.arrange(grobs = plots, ncol = 1, nrow = ncol(transcriptome))
  }
}
#> 
#> Call:
#> fastcpd.mean(data = transcriptome$"10", trim = 0.005)
#> 
#> Change points:
#> 177 264 394 534 578 656 788 811 869 934 960 1051 1141 1286 1319 1367 1567 1657 1724 1906 1972 1994 2041 2058 2143 2200 
#> 
#> Cost values:
#> 80.49071 38.37606 71.90419 109.9683 171.8534 18.56209 58.95214 3.133287 24.77084 49.27842 36.67618 63.41361 57.65488 91.34648 7.042687 46.13709 69.31514 141.4311 290.2077 77.60234 52.61751 41.31567 71.81727 13.09335 36.00007 14.8323 12.87647 
#> 
#> Parameters:
#>   segment 1 segment 2 segment 3 segment 4 segment 5 segment 6 segment 7
#> 1         0         0         0         0         0         0         0
#>   segment 8 segment 9 segment 10 segment 11 segment 12 segment 13 segment 14
#> 1         0         0          0          0          0          0          0
#>   segment 15 segment 16 segment 17 segment 18 segment 19 segment 20 segment 21
#> 1          0          0          0          0          0          0          0
#>   segment 22 segment 23 segment 24 segment 25 segment 26 segment 27
#> 1          0          0          0          0          0          0


# }