06-tables.Rmd

# MTXQCvX2 - Dictionary output tables{#output}

This chapter covers the description of parameters and values of all output files generated by all `.Rmd` files of the MTXQCvX2. Each parameter is accompanied with a short description. The value shown is only a exemplary value of a MTXQCvX2 project and does not represent a full collection. The input parameters of each `.Rmd` file are introduced in the chapter [MTXQCvX2 universe](#universe) for each document individually.

## Project-files{#o_project}
### `MTXQC_params.csv`

The file `MTXQC_params.csv` is created with each run of the `MTXQCvX2_ExperimentalSetup.Rmd`. It summarises all defines parameters of your project and builds the basis how MTXQCvX2 is going to process your input files. 

The content of the file can be modified by running the `.Rmd` file multiple times changing the parameters. This provides a convient flexibilty, e.g., to include additional quantification standards at a later time point or to exclude them for another round of MTXQCvX2 processing.

```{r mtxqcparams, echo=FALSE}

t = read.csv("tables/mtxqc_params.csv", TRUE)
knitr::kable(t, booktabs = TRUE)

```

### `Maui_params.csv`

This file is created as soon as you define the input format as Maui-derived content while running `MTXQCvX2_ExperimentalSetup.Rmd`. It contains the definition of file names and their related content. File names are pre-defined for those ones that are exported by Maui-Exports with a fixed file name.

```{r mauiparams, echo=FALSE}

t = read.csv("tables/maui_params.csv", TRUE)
knitr::kable(t, booktabs = TRUE)

```

### `Metmax_params.csv`

This table is create while running `MTXQCvX2_part4.Rmd` and summarises the input parameter you defined for knitting the document. It states the defined input files and the related processes that should be performed by this module. 

```{r metmaxparams, echo=FALSE}

t = read.csv("tables/metmax_par.csv", TRUE)
knitr::kable(t, booktabs = TRUE)

```

## `output/gc/...`{#o_gc}
### `HM_GC_values.csv` \& `qcmetric_xy.csv`

MTXQC exports a file summarising quality factors for each of the four parameter evaluating the GC performance. A summary representing the values illustrated in the heatmap are shown in table [`HM_GC_values.csv`](\@ref(tab:o_hm_gc)), individual exports for each metric in table [`qcmetric_xy.csv`](\@ref(tab:o_gc_metric)).

```{r o_hm_gc, echo=FALSE}

t = read.csv("tables/output_HM_gc.csv", TRUE)
knitr::kable(t, booktabs = TRUE)
```

```{r o_gc_metric, echo=FALSE}

t = read.csv("tables/output_HM_gc.csv", TRUE)
knitr::kable(t, booktabs = TRUE)
```


### `IntStandard_normfactors.csv` \& `IntStandard_stats.csv`

```{r o_is_normfactors, echo=FALSE}

t = read.csv("tables/output_isnormfactors.csv", TRUE)
knitr::kable(t, booktabs = TRUE)
```

```{r o_is_normstats, echo=FALSE}

t = read.csv("tables/output_is_stats.csv", TRUE)
knitr::kable(t, booktabs = TRUE)
```


### `Min_Annotation.csv` \& `SumArea_stats.csv`

```{r o_soa, echo=FALSE}

t = read.csv("tables/output_gc_sumarea.csv", TRUE)
knitr::kable(t, booktabs = TRUE)
```

```{r o_minann, echo=FALSE}

t = read.csv("tables/output_minann.csv", TRUE)
knitr::kable(t, booktabs = TRUE)
```

### `mz73_data.csv`

```{r o_mzz, echo=FALSE}

t = read.csv("tables/output_mz72.csv", TRUE)
knitr::kable(t, booktabs = TRUE)
```

## `output/quant/...`{#o_quant}

### `calcheck_linearity.csv`

```{r o_calcheck, echo=FALSE}

t = read.csv("tables/quant_calcheck.csv", TRUE)
knitr::kable(t, booktabs = TRUE)
```

### `CalculationFileData.csv`{#CalcFileData}

This is porbably the most important file that is generated by running `MTXQCvX2_part1.Rmd`. It summarises all quality factors, experimental data and determined quantities of your experiment. This file provides the input for `MTXQCvX2_part2-PostProcessing.Rmd`.

```{r o_calcfiledata, echo=FALSE}

t = read.csv("tables/output_quant_calculationfiledata.csv", TRUE)
knitr::kable(t, longtable = TRUE, booktabs = TRUE)
```

### `HeatMap_Quant_pTop5.csv`

```{r o_hm_quant, echo=FALSE}

t = read.csv("tables/output_hm_quant.csv", TRUE)
knitr::kable(t, booktabs = TRUE)
```

### `pTop5_Calibration_Samples_lincheck.csv`

```{r o_quant_lincheck, echo=FALSE}

t = read.csv("tables/output_quant_lincheck.csv", TRUE)
knitr::kable(t, booktabs = TRUE)
```

### `top5_CalibrationInfo_unique.csv`

```{r o_quant_info, echo=FALSE}
t = read.csv("tables/output_quant_calibrationInfo.csv", TRUE)
knitr::kable(t, booktabs = TRUE)
```

### `top5_QMQcurveInfo.csv`

```{r o_quant_qmq, echo=FALSE}
t = read.csv("tables/output_quant_qmq.csv", TRUE)
knitr::kable(t, booktabs = TRUE)
```

## `output/inc/...`{#o_inc}

### `HeatMap_Incorporation.csv`

```{r o_inc_hm, echo=FALSE}
t = read.csv("tables/output_inc_hm.csv", TRUE)
knitr::kable(t, booktabs = TRUE)
```

### `SE_calculation_NAscore.csv`

```{r o_inc_calc, echo=FALSE}
t = read.csv("tables/output_inc_SEcalc.csv", TRUE)
knitr::kable(t, booktabs = TRUE)
```

### `SE_classification.csv`

```{r o_inc_class, echo=FALSE}
t = read.csv("tables/output_inc_SEclass.csv", TRUE)
knitr::kable(t, booktabs = TRUE)
```

### `SE_validation.csv`

```{r o_inc_eval, echo=FALSE}
t = read.csv("tables/output_inc_SEval.csv", TRUE)
knitr::kable(t, booktabs = TRUE)
```