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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

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Individual 15

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Individual 18

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Individual 19

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Individual 21

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Individual 22

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Individual 24

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Individual 26

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Individual 28

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Individual 30

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Individual 31

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Individual 33

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Individual 34

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Individual 35

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Individual 36

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Individual 37

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Individual 38

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Individual 39

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Individual 40

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Individual 41

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Individual 43

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Individual 44

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Individual 45

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Individual 46

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Individual 47

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Individual 48

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Individual 49

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Individual 50

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Individual 51

53

Individual 53

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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{
for (package in c("ggplot2", "gridExtra")) {
  if (!requireNamespace(package, quietly = TRUE)) utils::install.packages(
    package, repos = "https://cloud.r-project.org", quiet = TRUE
  )
}

result <- fastcpd.mean(transcriptome$"10", trim = 0.005)
summary(result)
#> 
#> Call:
#> fastcpd.mean(data = transcriptome$"10", trim = 0.005)
#> 
#> Change points:
#> 177 264 394 534 579 656 788 811 869 934 960 1051 1141 1286 1319 1368 1568 1657 1724 1906 1972 1994 2041 2058 2146 2200 
#> 
#> Cost values:
#> -150.0608 -74.94586 -97.42742 -72.38886 113.286 -81.74779 -112.9846 -26.82538 -50.77711 -35.38739 2.809858 -55.11852 -59.57469 -97.52339 -35.94149 -16.56248 -190.1474 25.1781 202.9368 -159.4619 -33.35084 12.65955 10.59738 -9.050017 -76.02135 -55.56151 -6.661788 
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 = "blue") +
      ggplot2::geom_vline(
        xintercept = result_all@cp_set,
        color = "red",
        linetype = "dotted",
        linewidth = 0.5,
        alpha = 0.7
      ) +
      ggplot2::theme_void()
  }
)

gridExtra::grid.arrange(grobs = plots, ncol = 1, nrow = ncol(transcriptome))

# }