Description Usage Arguments Details Value References Examples

Calculates the PECA ordinary or modified t-statistic to determine differential expression between two groups of samples in Affymetrix gene expression studies or peptide-based proteomic studies.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
## Read AffyBatch object
PECA_AffyBatch(affy=NULL, normalize=FALSE, test="t", type="median", paired=FALSE, progress=FALSE)
## Read CEL-files
PECA_CEL(samplenames1=NULL, samplenames2=NULL, normalize=FALSE, test="t",
type="median", paired=FALSE, progress=FALSE)
## Read tab separated text file
PECA_tsv(file=NULL, samplenames1=NULL, samplenames2=NULL, normalize=FALSE,
test="t", type="median", paired=FALSE, progress=FALSE)
## Read dataframe
PECA_df(df=NULL, id=NULL, samplenames1=NULL, samplenames2=NULL, normalize=FALSE,
test="t", type="median", paired=FALSE, progress=FALSE)
``` |

`affy` |
AffyBatch object. |

`normalize` |
A character string indicating if (" |

`test` |
A character string indicating whether the ordinary t-test (" |

`type` |
A character string indicating whether (" |

`paired` |
A logical indicating whether a paired test is performed. |

`file` |
Filename of tab separated data. |

`samplenames1` |
A character vector containing the names of the .CEL-files/columns in the first group. |

`samplenames2` |
A character vector containing the names of the .CEL-files/columns in the second group. |

`df` |
Dataframe to be used as an input. |

`id` |
Column name of dataframe used for aggregating results. |

`progress` |
A logical indicating whether a progress bar is shown. |

`PECA`

determines differential gene expression using directly the probe-level measurements from Affymetrix gene expression microarrays or proteomic datasets. An expression change between two groups of samples is first calculated for each probe/peptide on the array. The gene/protein-level expression changes are then defined as medians over the probe-level changes. For more details about the probe-level expression change averaging (PECA) procedure, see Elo et al. (2005), Laajala et al. (2009) and Suomi et al.

`PECA`

calculates the probe-level expression changes using the ordinary or modified t-statistic. The ordinary t-statistic is calculated using the function `rowttests`

in the Bioconductor `genefilter`

package. The modified t-statistic is calculated using the linear modeling approach in the Bioconductor `limma`

package. Both paired and unpaired tests are supported.

The significance of an expression change is determined based on the analytical p-value of the gene-level test statistic. Unadjusted p-values are reported along with the corresponding p-values looked up from beta ditribution. The quality control and filtering of the data (e.g. based on low intensity or probe specificity) is left to the user.

`PECADE`

returns a matrix which rows correspond to the genes under analysis and columns indicate the corresponding signal log-ratio (slr), t-statistic, p-value and FDR adjusted p-value.

T. Suomi, G.L. Corthals, O. Nevalainen and L.L. Elo: Using peptide-level proteomics data for detecting differentially expressed proteins. 2015

L.L. Elo, L. Lahti, H. Skottman, M. Kylaniemi, R. Lahesmaa and T. Aittokallio: Integrating probe-level expression changes across generations of Affymetrix arrays. Nucleic Acids Research 33(22), e193, 2005.

E. Laajala, T. Aittokallio, R. Lahesmaa and L.L. Elo: Probe-level estimation improves the detection of differential splicing in Affymetrix exon array studies. Genome Biology 10(7), R77, 2009.

H. Bengtsson, K. Simpson, J. Bullard and K. Hansen: aroma.affymetrix: A generic framework in R for analyzing small to very large Affymetrix data sets in bounded memory. Tech Report \#745, Department of Statistics, University of California, Berkeley, 2008.

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
## Generate example data frame
df <- data.frame(id=c(rep("a",10),rep("b",10),rep("c",10)))
df$A1 <- rnorm(30, mean=50, sd=5)
df$A2 <- rnorm(30, mean=48, sd=5)
df$A3 <- rnorm(30, mean=50, sd=5)
df$B1 <- rnorm(30, mean=52, sd=5)
df$B2 <- rnorm(30, mean=53, sd=5)
df$B3 <- rnorm(30, mean=51, sd=5)
## Run the test
group1 <- c("A1","A2","A3")
group2 <- c("B1","B2","B3")
results <- PECA_df(df, group1, group2, id=id)
``` |

```
Performing log-transformation
Calculating low-level statistics
Aggregating statistics
Done
```

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