cellHTS-class {cellHTS2} | R Documentation

## A class for data from cell-based high-throughput assays performed in plate format.

### Description

Container for data and experimental meta-data from cell-based high-throughput assays performed in plate format. Typical applications are RNA interference or small molecular compound screens. The class extends the `NChannelSet` class. Data are from experiments where the same set of reagents (probes) where used. The class can represent data from multi-channel assays.

The data can be thought of as being organised in a two- or three-dimensional array as follows:

- The first dimension corresponds to reagents (e.g. siRNAs, chemical compounds) that were used in the assays. For example, if the screen used 100 plates of 384 wells (24 columns, 16 rows), then the first dimension has size 38,400, and the `cellHTS` object keeps track of plate ID, row, and column associated with each element. For historic reasons, and because we are using infrastructure that was developed for microarray experiments, the following terms are used synonymously for the elements of the first dimension: reagents, features, probes, genes.

- The second dimension corresponds to assays, including replicates and different experimental conditions (cell type, treatment, genetic background). A potentially confusing terminology is that the data structure that annotates the second dimension is called `phenoData`. This is because we are using infrastructure (the `NChannelSet` class) that uses this unfortunate term for this purpose. The software provides methodology for replicate summarization and scoring, however more complicated experimental designs are not directly supported. Multi-purpose tools like `lmFit` in the `limma` package should be consulted.

- The (optional) third dimension corresponds to different channels (e.g. different luminescence reporters)

### Objects from the Class

Objects can be created by calls of the form `new("cellHTS", assayData, phenoData, ...)`. See the examples below.

### Slots

`plateList`:

  a `data.frame` containing what was read from input measurement data files plus a column `status` of type character containing the string "OK" if the data import appeared to have gone well, and the respective error or warning message otherwise.

`intensityFiles`:

  a list, where each component contains a copy of the imported input data files. Its length corresponds to the number of rows of `plateList`.

`state`:

  a logical vector of length 4 representing the processing status of the object. It must have the names "configured", "normalized", "scored" and "annotated".

`plateConf`:

  a `data.frame` containing what was read from the configuration file for the experiment (except the first two header rows). It contains at least three columns named `Plate`, `Well` and `Content`. Columns `Plate` and `Well` are allowed to contain regular expressions.

`screenLog`:

  a `data.frame` containing what was read from the screen log file for the experiment, in case it exists. Contains at least three columns, and column names `Plate`, `Well`, and `Flag`. Additional columns are `Sample` (when there are replicates or more than one sample or condition) and `Channel` (when there are multiple channels).

`screenDesc`:

  a character containing what was read from the description file of the experiment.

`rowcol.effects`:

  a 3D array of size Features (i.e. plate size x number of plates) x Samples x Channels containing estimated row and column plate spatial offsets.

`overall.effects`:

  a 3D array of size Features x Samples x Channels containing estimated plate overall offsets.

`assayData`:

  Object of class `AssayData`, usually an environment containing matrices of identical size. Each matrix represents a single channel. Columns in each matrix correspond to samples (or replicate), rows to features (probes). Once created, `cellHTS` manages coordination of samples and channels.

`plateData`:

  A list of data frames, with number of rows of each frame equal to the number of plates used in the assay and number of columns equal to the number of samples. Each data frame in the list should contain factorial annotation information relevant for the individual plates, like experimental batches or varying types of micro-titre plates. Currently, this information will be used for between-batch normalization and quality assessment.

`phenoData`:

  Object of class `AnnotatedDataFrame`. Please see the documentation of the `phenoData` slot of `NChannelSet` for more details.

  It contains information about the screens, and it must have the following columns in its `data` component: `replicate` and `assay`, where `replicate` is expected to be a vector of integers giving the replicate number, while `assay` is expected to be a vector of characters giving the name of the biological assay. Both of these vectors should have the same length as the number of Samples.

  Once created, `cellHTS` coordinates selection and subsetting of channels in `phenoData`.

`featureData`:

  Object of class `AnnotatedDataFrame`, containing information about the reagents: plate, well, column, the well annotation (sample, control, etc.), etc. For a `cellHTS` object, this slot must contain in its `data` component at least three mandatory columns named `plate`, `well` and `controlStatus`. Column `plate` is expected to be a numeric vector giving the plate number (e.g. 1, 2, ...), `well` should be a vector of characters (alphanumeric characters) giving the well ID within the plate (e.g. A01, B01, H12, etc.). Column `controlStatus` should be a factor specifying the annotation for each well with possible levels: empty, other, neg, sample, and pos. Other levels besides pos and neg may be employed for controls.

`experimentData`:

  Object of class `MIAME` containing descriptions of the experiment.

`annotation`:

  A `"character"` of length 1, which can be used to specify the name of an annotation package that goes with the reagents used for this experiment.

`processingInfo`:

  A list containing information about which normalization and summarization methods have been used.

`.__classVersion__`:

  Object of class `Versions`, containing automatically created information about the class definition, Biobase package version, and other information about the user system at the time the instance was created. See `classVersion` and `updateObject` for examples of use.

### Extends

Class `NChannelSet`, directly.

### Methods

Methods with class-specific functionality:

`name(object)`

  `signature(object="cellHTS")`. Obtains the name of the assay stored in the `object`. This corresponds to the contents of column `assay` of the `phenoData` slot of the `cellHTS` object.

`name(object) <- value`

  `signature(object = "cellHTS", value = "character")` assign the character of length one (`value`) to the elements in column `assay` of the slot `phenoData` of `object`.

`pdim(object)`

  `signature(object = "cellHTS")`. Obtain the plate dimension for the data stored in `object`.

`nbatch(object)`

  `signature(object = "cellHTS")`. Obtain the total number of batches for the data stored in `object`.

`compare2cellHTS(x, y)`

  `signature(x = "cellHTS", y = "cellHTS")`. Compares two `cellHTS` objects, `x` and `y`, returning `TRUE` if they are from the same experiment (i.e. if they derive from the same initial `cellHTS` object), or `FALSE` otherwise.

Methods with functionality derived from class `NChannelSet`: `channel`, `channelNames`, `channelNames<-`, `selectChannels`, `object[features, samples]`, `sampleNames`

Methods with functionality derived from `eSet`: `annotation`, `assayData`, `assayData<-`, `classVersion`, `classVersion<-`, `dim`, `dims`, `experimentData`, `featureData`, `phenoData`, `phenoData<-`, `pubMedIds`, `sampleNames`, `sampleNames<-`, `storageMode`, `varMetadata`, `isCurrent`, `isVersioned`.

Additional methods:

`initialize`

  used internally for creating objects

`show`

  invoked automatically when the object is displayed to the screen. It prints a summary of the object.

`state`

  Access the `state` slot of a `cellHTS` instance.

`annotate`

  Annotate the `cellHTS` object using the screen annotation file.

`configure`

  Configure the `cellHTS` object using the the screen description file, the screen configuration file and the screen log file.

`writeTab`

  Write the contents of `assayData` slot of a `cellHTS` object to a tab-delimited file.

`ROC`

  Construct an object of S4 class `ROC`, which represents a receiver-operator-characteristic curve, from the data of the annotated positive and negative controls in a scored `cellHTS` object.

`meanSdPlot(x)`

  `signature(x = "cellHTS")` plots row standard deviations across samples versus row means across samples for data stored in slot `assayData` of a `cellHTS` object. If there are multiple channels, row standard deviations and row means are calculated across samples for each channel separately. Only wells containing "sample" are considered. See `meanSdPlot` for more details about this function.

### Author(s)

Ligia P. Bras ligia@ebi.ac.uk, Wolfgang Huber huber@ebi.ac.uk

### See Also

`NChannelSet` `readPlateList` `annotate` `configure` `writeTab` `state` `Data` `normalizePlates` `ROC`

### Examples

        showClass("cellHTS")
        showMethods(class="cellHTS")

        ## An empty cellHTS
        obj <- new("cellHTS")

        data("KcViabSmall")
        KcViabSmall
        state(KcViabSmall)
        ## Replicate 1 as a cellHTS object
        y <- KcViabSmall[,1]
        compare2cellHTS(KcViabSmall, y)
        data("KcViab")
        compare2cellHTS(KcViab, KcViabSmall)

[Package cellHTS2 version 2.46.1 Index]