hcc1954: hcc1954

In ddepn: Dynamic Deterministic Effects Propagation Networks: Infer signalling networks for timecourse RPPA data.

Description

Example phosphoproteomic data set. Phosphorylation of 16 proteins from the ERBB signalling network were measured in 10 time points (between 0 and 60 minutes) in HCC1954 cell line. Measurements were generated via Reverse Phase Protein Array technique.

Format

Matrix 16x540. Rows hold proteins, Columns hold the Experiments and time points.
Column naming: STIMULUS_biolreplicate_time
Rows naming: Protein_Phosphorylation site.

Details

Use data(hcc1954) to obtain the FCF normalised and replicate centered data matrix or data(hcc1954raw) to obtain the raw data matrices. The latter comprise the raw data hcc1954raw, the FCF values hcc1954fcf and the medians of the experiment FCF values hcc1954fcfmedian. See examples for how to transform the raw to the normalised data.

The following experiments were performed: stimulation with ...
...EGF
...HRG
...EGF&HRG

The following proteins were measured:

protein_phosphorylationsite	entrez id
pEGFR_Y1068	1956
pERBB2_Y1112	2064
pERK12_T202Y204	5595 5594
pAKT_S473	207 208
pPDK1_S241	5170
pMEK_S217S221	5604 5605
pPLCgamma_S1248	5335 5336
pPKCalpha_S657Y658	5578
pp38_T180Y182	1432
pSRC_Y416	6714
pmTOR_S2448	2475
pp70S6K_T389	6198
pGSK3_Y279Y216	2931 2932
pPRAS_T246	100136753
pERBB3_Y1289	2065
pERBB4_Y1162	2066

Note

To use these data matrices as inputs for ddepn, the information on the biological replicates in the column names have to be removed using the function format_ddepn, since all replicates are treated equally during inference. The information on the biological replicates was left in the data matrix, however, since it might be useful for different approaches, too.

References

Bender et. al. 2010: Dynamic deterministic effects propagation networks: learning signalling pathways from longitudinal protein array data; Bioinformatics, Vol. 26(18), pp. i596-i602

See Also

format_ddepn center_ddepn

Examples

## Not run: 

library(ddepn)

## get the normalised data matrix
data(hcc1954)
colnames(hcc1954)
rownames(hcc1954)

## get the raw matrices
data(hcc1954raw)

## perform FCF normalisation
datfcf <- hcc1954raw / hcc1954fcf * hcc1954fcfmedian

## center the data, datc is then equal to the hcc1954 matrix
datc <- center_ddepn(datfcf)

## End(Not run)

ddepn documentation built on May 2, 2019, 4:42 p.m.