update impute.*(): rm reportImputing

Author: Carol-seven

R/imputations.R

@@ -18,7 +18,7 @@ dataNorm <- normalize(dataTran, normalizeType = "quant")
 ## filtering
 dataImput_pre <- filterNA(dataNorm, minProp = 0.51, by = "cond", saveRm = TRUE)
 ## imputation
-dataImput <- impute.min_local(dataImput_pre, reportImputing = FALSE)
+dataImput <- impute.min_local(dataImput_pre)
 ```
 
 The functions in the analysis module calculate the results that can be
@@ -28,7 +28,7 @@ used in subsequent visualizations.
 the reference condition, which is specified by the argument `ref`, for
 multiple comparisons. If `ref` is not provided, it will be automatically
 generated by all the combinations of two conditions, based on the level
-attributes of the condition.  
+attributes of the condition.\
 For example, suppose there are three conditions in the data: “A”, “B”,
 and “C”. If you specify `ref = "A"`, then the result includes two
 comparisons: “B-A” and “C-A”. If `ref = NULL`, there will be three
@@ -317,7 +317,7 @@ to zero mean and scale to unit variance, with default setting at `TRUE`.
 **Note:** Data scaling is done to ensure that the scale differences
 between different features do not affect the results of PCA. If not
 scaled, features with larger scales will dominate the computation of
-principal components (PCs).  
+principal components (PCs).\
 **Note:** The most common error message for the PCA is “**Cannot rescale
 a constant/zero column to unit variance**.” This clearly occurs when
 columns representing proteins contain only zeros or have constant

docs/articles/analysis.html

@@ -29,7 +29,7 @@ dataNorm <- normalize(dataTran, normalizeType = "quant")
 ## filtering
 dataImput_pre <- filterNA(dataNorm, minProp = 0.51, by = "cond", saveRm = TRUE)
 ## imputation
-dataImput <- impute.min_local(dataImput_pre, reportImputing = FALSE)
+dataImput <- impute.min_local(dataImput_pre)
 ## analysis
 anlys_modt <- analyze.mod_t(dataImput, ref = "50pmol")
 anlys_ma <- analyze.ma(dataImput, ref = "50pmol")

docs/articles/analysis.md

@@ -132,7 +132,7 @@ either an exact match identifier (the `listName =` argument), or
 text-containing identifiers (the `regexName =` argument).
 
 **Note:** If both `listName` and `regexName` are defined, the proteins
-to be selected or removed is the union of the two terms.  
+to be selected or removed is the union of the two terms.\
 **Keep in mind:** Removal of any proteins, including common
 contaminants, will affect any global calculations performed after this
 step (such as normalization). This should not be done without a clear

docs/articles/cust_vis.html

@@ -47,7 +47,7 @@ directory.
 
 ``` r
 
-dataImput <- impute.min_local(dataImput_pre, reportImputing = FALSE)
+dataImput <- impute.min_local(dataImput_pre)
 ```
 
 | R.Condition | R.Replicate | NUD4B_HUMAN (+1) | A0A7P0T808_HUMAN (+1) | A0A8I5KU53_HUMAN (+1) | ZN840_HUMAN | CC85C_HUMAN | C9JEV0_HUMAN (+1) | C9JNU9_HUMAN | ALBU_BOVIN | CYC_BOVIN | TRFE_BOVIN | F8W0H2_HUMAN | H0Y7V7_HUMAN (+1) | H0YD14_HUMAN | H3BUF6_HUMAN | H7C1W4_HUMAN (+1) | H7C3M7_HUMAN | TLR3_HUMAN | LRIG2_HUMAN | RAB3D_HUMAN | ADH1_YEAST | LYSC_CHICK | BGAL_ECOLI | CYTA_HUMAN | KPCB_HUMAN | LIPL_HUMAN | CO6_HUMAN | BGAL_HUMAN | SYTC_HUMAN | CASPE_HUMAN | DCAF6_HUMAN | DALD3_HUMAN | HGNAT_HUMAN | RFFL_HUMAN | RN185_HUMAN | ZN462_HUMAN | ALKB7_HUMAN | POLK_HUMAN | ACAD8_HUMAN |
@@ -65,11 +65,6 @@ dataImput <- impute.min_local(dataImput_pre, reportImputing = FALSE)
 | 50pmol | 3 | 11.59517 | 6.004586 | 10.729201 | 6.448756 | 8.732804 | 8.149770 | 8.386314 | 16.75777 | 13.38772 | 14.13067 | 9.658384 | 9.362666 | 10.377729 | 8.620541 | 7.026553 | 9.954832 | 7.035806 | 8.429646 | 10.531713 | 14.70670 | 13.88102 | 14.38206 | 10.036651 | 10.229206 | 9.012072 | 7.455730 | 12.49496 | 15.13045 | 7.964140 | 7.766206 | 8.520402 | 10.120238 | 7.145874 | 9.504539 | 8.267212 | 8.866513 | 7.307825 | 6.807164 |
 | 50pmol | 4 | 10.96831 | 10.008606 | 10.662903 | 8.298269 | 8.190682 | 7.806291 | 8.659793 | 16.75777 | 12.30540 | 13.84672 | 9.504539 | 9.362666 | 10.482590 | 8.555305 | 6.004586 | 9.753718 | 7.035806 | 8.429646 | 10.329078 | 14.68689 | 13.25790 | 14.10465 | 9.911682 | 10.189464 | 8.386314 | 7.026553 | 11.47571 | 15.11842 | 7.637117 | 8.017625 | 9.581714 | 10.086066 | 6.459113 | 9.099265 | 8.926299 | 7.480137 | 7.159242 | 6.822962 |
 
-If `reportImputing = TRUE`, the returned result structure will be
-altered to a list, adding a shadow data frame with imputed data labels,
-where 1 indicates the corresponding entries have been imputed, and 0
-indicates otherwise.
-
 ## Details
 
 The two primary MS/MS acquisition types implemented in large scale

docs/articles/cust_vis.md

@@ -65,13 +65,13 @@ Many normalization methods have been developed for large datasets, each
 with its own strengths and weaknesses. The following factors should be
 considered when choosing a normalization method:
 
-1.  Experiment-Specific Normalization:  
+1.  Experiment-Specific Normalization:\
     Most experiments run with [UConn PMF](https://proteomics.uconn.edu)
     are normalized by injection amount at the time of analysis to
     facilitate comparison. “Amount” is measured by UV absorbance at 280
     nm, a standard method for generic protein quantification.
 
-2.  Assumption of Non-Changing Species:  
+2.  Assumption of Non-Changing Species:\
     Most biological experiments implicitly assume that the majority of
     measured species in an experiment will not change across conditions.
     This assumption is more robust the more measurements your experiment