quantification.RdModel-based quantification for each condition or for each biological
sample per protein in a targeted Selected Reaction Monitoring (SRM),
Data-Dependent Acquisition (DDA or shotgun), and Data-Independent Acquisition
(DIA or SWATH-MS) experiment. Quantification takes the processed data set
by dataProcess as input and automatically generate the quantification
results (data.frame) in a long or matrix format.
quantification( data, type = "Sample", format = "matrix", use_log_file = TRUE, append = FALSE, verbose = TRUE, log_file_path = NULL )
| data | name of the (processed) data set. |
|---|---|
| type | choice of quantification. "Sample" or "Group" for protein sample quantification or group quantification. |
| format | choice of returned format. "long" for long format which has the columns named Protein, Condition, LogIntensities (and BioReplicate if it is subject quantification), NumFeature for number of transitions for a protein, and NumPeaks for number of observed peak intensities for a protein. "matrix" for data matrix format which has the rows for Protein and the columns, which are Groups(or Conditions) for group quantification or the combinations of BioReplicate and Condition (labeled by "BioReplicate"_"Condition") for sample quantification. Default is "matrix" |
| use_log_file | logical. If TRUE, information about data processing will be saved to a file. |
| append | logical. If TRUE, information about data processing will be added to an existing log file. |
| verbose | logical. If TRUE, information about data processing wil be printed to the console. |
| log_file_path | character. Path to a file to which information about data processing will be saved. If not provided, such a file will be created automatically. If `append = TRUE`, has to be a valid path to a file. |
data.frame as described in details.
Sample quantification : individual biological sample quantification for each protein. The label of each biological sample is a combination of the corresponding group and the sample ID. If there are no technical replicates or experimental replicates per sample, sample quantification is the same as run summarization from dataProcess. If there are technical replicates or experimental replicates, sample quantification is median among run quantification corresponding MS runs.
Group quantification : quantification for individual group or individual condition per protein. It is median among sample quantification.
The quantification for endogenous samples is based on run summarization from subplot model, with TMP robust estimation.
# Consider quantitative data (i.e. QuantData) from a yeast study with ten time points of # interests, three biological replicates, and no technical replicates which is # a time-course experiment. # Sample quantification shows model-based estimation of protein abundance in each biological # replicate within each time point. # Group quantification shows model-based estimation of protein abundance in each time point. QuantData<-dataProcess(SRMRawData, use_log_file = FALSE)#> INFO [2021-07-05 20:06:05] ** Features with one or two measurements across runs are removed. #> INFO [2021-07-05 20:06:05] ** Fractionation handled. #> INFO [2021-07-05 20:06:05] ** Updated quantification data to make balanced design. Missing values are marked by NA #> INFO [2021-07-05 20:06:05] ** Log2 intensities under cutoff = 3.776 were considered as censored missing values. #> INFO [2021-07-05 20:06:05] ** Log2 intensities = NA were considered as censored missing values. #> INFO [2021-07-05 20:06:05] ** Use all features that the dataset originally has. #> INFO [2021-07-05 20:06:05] #> # proteins: 2 #> # peptides per protein: 2-2 #> # features per peptide: 3-3 #> INFO [2021-07-05 20:06:05] #> 1 2 3 4 5 6 7 8 9 10 #> # runs 3 3 3 3 3 3 3 3 3 3 #> # bioreplicates 3 3 3 3 3 3 3 3 3 3 #> # tech. replicates 1 1 1 1 1 1 1 1 1 1 #> INFO [2021-07-05 20:06:05] == Start the summarization per subplot... #> | | | 0% | |=================================== | 50% | |======================================================================| 100% #> INFO [2021-07-05 20:06:06] == Summarization is done.#> PROTEIN PEPTIDE TRANSITION FEATURE LABEL GROUP RUN #> 1 IDHC ATDVIVPEEGELR_2 y7_NA ATDVIVPEEGELR_2_y7_NA H 0 1 #> 2 IDHC ATDVIVPEEGELR_2 y7_NA ATDVIVPEEGELR_2_y7_NA L 1 1 #> 3 IDHC ATDVIVPEEGELR_2 y7_NA ATDVIVPEEGELR_2_y7_NA H 0 2 #> 4 IDHC ATDVIVPEEGELR_2 y7_NA ATDVIVPEEGELR_2_y7_NA L 1 2 #> 5 IDHC ATDVIVPEEGELR_2 y7_NA ATDVIVPEEGELR_2_y7_NA H 0 3 #> 6 IDHC ATDVIVPEEGELR_2 y7_NA ATDVIVPEEGELR_2_y7_NA L 1 3 #> SUBJECT FRACTION originalRUN censored INTENSITY ABUNDANCE newABUNDANCE #> 1 0 1 1 FALSE 84361.0835 15.855859 15.855859 #> 2 1 1 1 FALSE 215.1353 7.240669 7.240669 #> 3 0 1 2 FALSE 62109.5876 15.801179 15.801179 #> 4 2 1 2 FALSE 1205.2252 10.113738 10.113738 #> 5 0 1 3 FALSE 65114.3646 15.755022 15.755022 #> 6 3 1 3 FALSE 1476.3046 10.292109 10.292109 #> predicted #> 1 NA #> 2 NA #> 3 NA #> 4 NA #> 5 NA #> 6 NA# Sample quantification sampleQuant<-quantification(QuantData, use_log_file = FALSE) head(sampleQuant)#> Protein 1_ReplA 1_ReplB 1_ReplC 10_ReplA 10_ReplB 10_ReplC 2_ReplA #> 1: IDHC 5.57705 6.811034 6.909093 12.72029 12.77645 12.71629 6.373069 #> 2: PMG2 10.75693 10.647513 10.530769 10.40899 10.60595 10.34241 10.057710 #> 2_ReplB 2_ReplC 3_ReplA 3_ReplB 3_ReplC 4_ReplA 4_ReplB #> 1: 6.59575 6.349843 6.140378 6.600981 7.033944 6.790116 6.174932 #> 2: 10.47072 10.613419 10.667443 10.654398 10.419662 10.773446 10.535321 #> 4_ReplC 5_ReplA 5_ReplB 5_ReplC 6_ReplA 6_ReplB 6_ReplC #> 1: 7.313115 7.260217 7.209141 6.519457 9.653219 9.542782 9.553527 #> 2: 10.696021 10.461630 10.945504 10.572543 9.943402 10.595059 10.587633 #> 7_ReplA 7_ReplB 7_ReplC 8_ReplA 8_ReplB 8_ReplC 9_ReplA 9_ReplB #> 1: 12.55638 12.65310 12.55085 12.72204 12.78281 12.80676 12.66386 12.72873 #> 2: 10.56620 10.72296 10.37506 10.22503 10.45177 10.39042 10.27202 10.57575 #> 9_ReplC #> 1: 12.66681 #> 2: 10.39904# Group quantification groupQuant<-quantification(QuantData, type="Group", use_log_file = FALSE) head(groupQuant)#> Protein 1 10 2 3 4 5 6 #> 1: IDHC 6.811034 12.72029 6.373069 6.600981 6.790116 7.209141 9.553527 #> 2: PMG2 10.647513 10.40899 10.470722 10.654398 10.696021 10.572543 10.587633 #> 7 8 9 #> 1: 12.55638 12.78281 12.66681 #> 2: 10.56620 10.39042 10.39904