DEprot.missingness class

Source: R/classes.and.methods.R

S4 object storing the results of missingness.diagnostic: the classification of the missing values into MNAR-like (left-censored, to be replaced by randomize.missing.values) and MCAR-like (to be replaced by impute.counts), together with the corresponding summary tables and diagnostic plots.

Slots

data.used

String indicating the counts table used for the diagnostic ('raw', 'normalized', 'randomized', 'imputed'). Class: "ANY".

counts.available

String vector indicating all the counts tables available in the input object. Class: "ANY".

metadata

Data.frame corresponding to the metadata table of the samples analyzed. Class: "ANY".

group.column

String indicating the metadata column used to define the groups of replicates. Class: "ANY".

missing.matrix

Logical matrix (rows: proteins, columns: samples) indicating the missing values (TRUE). Class: "ANY".

imputation.map

Character matrix (rows: proteins, columns: samples) indicating, for each value, the strategy that the DEprot double-imputation would apply: "detected" (measured value), "MNAR" (missing in at least percentage.missing% of the replicates of a group; randomized using the bottom of the distribution), "MCAR" (sparse missing value; imputed). Class: "ANY".

protein.stats

Data.frame with one row per protein reporting the number/frequency of missing values (globally and per group), the average intensity and the assigned missing.class ('complete', 'MCAR', 'MNAR', 'all.missing'). Class: "ANY".

sample.stats

Data.frame with one row per sample reporting the number and percentage of missing values, split by class. Class: "ANY".

group.summary

Data.frame with one row per group of replicates summarizing the missing values and the number of proteins per class within the group. Class: "ANY".

pattern.summary

Data.frame with the number and percentage of proteins in each missing-value class. Class: "ANY".

global.stats

List of global metrics: total percentage of missing values, fraction of missing values that are MNAR-like, estimated LOD50 (intensity at which 50% of the values are missing), slope and p-value of the logistic dropout model, p-value of the intensity shift between complete and incomplete proteins, and the intensity threshold corresponding to the bottom tail.percentage% of the distribution. Class: "ANY".

dropout.model

Object of class glm corresponding to the logistic dropout model (cbind(n.missing, n.detected) ~ mean.intensity). Class: "ANY".

plots

List of ggplot objects: detection.density, dropout.curve, missingness.heatmap, missing.per.sample, detection.frequency, pattern.barplot, sample.similarity, upset. Class: "ANY".

jaccard.matrix

Numeric matrix of the sample-vs-sample Jaccard similarity of the detection patterns (intersection / union of the sets of detected proteins). Class: "ANY".

jaccard.cluster

hclust object generated by hclust(d = as.dist(1 - jaccard.matrix), method = cluster.method), used to order the samples and to draw the dendrogram of the sample.similarity heatmap. Class: "ANY".

contrast.stats

List (one element per contrast, NULL if no DEprot.analyses object was provided) containing the contrast id, the samples of each group, the per-protein classification restricted to the contrast (including the testable column, FALSE for proteins missing in both groups) and the corresponding plots. Class: "ANY".

parameters

List of the parameters used to run the diagnostic (counts used, group column, percentage.missing, tail.percentage and the source of these parameters: user-defined or retrieved from the randomization). Class: "ANY".