Update quickOutputs.md

docs/user/QuickUsage/quickOutputs.md

@@ -1,18 +1,16 @@
-## Classic pangenome outputs
+## Usal pangenome outputs
 
-The complete workflow subcommand `all` generates automatically some files and figures. 
-Here, we are going to describe some of them that are useful and classic in pangenomic analyses, 
-as they describe the pangenome of your taxonomic group of interest in different ways.
+The complete workflow subcommand `all` automatically generates some files and figures. 
+Here, we are going to describe several of these key outputs that are commonly used in pangenomic studies as these files illustrate the pangenome from your taxonomic group of interest in different ways.
 
 ### Statistics and metrics on the pangenome
-#### Organisms statitics
+#### Organisms statistics
 
 [//]: # (TODO change this subsection after merge of split_write)
 
-PPanGGOLiN can generate a tab-separated file describing the content of each of the genome used for building the pangenome.
-It might be useful when working with fragmented data such as *MAGs* or if you suspect some of your genomes to be chimeric,
-or to not belong to your taxonomic group (as those genomes will be outliers regarding to the numbers in this file).
-The first lines starting with a '#' are indicators of parameters used when generating the numbers describing each organisms, and should not be read if loading this into a spreadsheet. They will be skipped automatically if you load this file with R.
+PPanGGOLiN can generate a tab-separated file describing the content of each genome used for building the pangenome.
+It might be useful when working with fragmented data such as *MAGs* or in cases where there is a suspicion that some genomes might be chimeric or fall outside the intended taxonomic group (as those genomes will be outliers regarding the numbers in this file).
+The first lines starting with a '#' are indicators of parameters used when generating the numbers describing each genome, and should not be included when loading the file into a spreadsheet. However, if you load this file using R, these lines will be automatically skipped
 
 This file is made of 15 columns described in the documentation here.
 
@@ -30,13 +28,13 @@ This file is basically a presence absence matrix.
 The columns are the genomes used to build the pangenome, the lines are the gene families. 
 The identifier of the gene family is the gene identifier chosen as a representative. 
 There is a 1 if the gene family is present in a genome, and 0 otherwise.
-It follows the exact same format than the 'gene_presence_absence.Rtab' file that you get from the pangenome analysis software [Roary](https://sanger-pathogens.github.io/Roary/)
+It follows the exact same format as the 'gene_presence_absence.Rtab' file that you get from the pangenome analysis software [Roary](https://sanger-pathogens.github.io/Roary/)
 
 More information about this file can be found [here](../PangenomeAnalyses/pangenomeAnalyses.md#gene-presence-absence)
 
 #### mean persistent duplication
 This file is a .tsv file, with a single parameter written as a comment at the beginning of the file, 
-which indicates the proportion of genomes in which a gene family must be present more than once to be considered 'duplicated' (and not single copy marker). 
+which indicates the proportion of genomes in which a gene family must be present more than once to be considered 'duplicated' (and not a single copy marker). 
 This file lists the gene families, their duplication ratio, their mean presence in the pangenome and whether it is considered a 'single copy marker' or not, 
 which is particularly useful when calculating the completeness recorded in the [organisms statistics file](#organisms-statitics) described previously.
 
@@ -49,12 +47,12 @@ mouseover to see numbers in more detail, and you can save what you are seeing as
 
 ![U-shaped plot _B.japonicum_](../../_static/tutorial/U-shape.gif)
 
-A dotted grey bar on the graph representing the **soft core threshold** which is the lower limit for which families are present in the majority of genomes. By default this value is 95% (so families are in more than 95 genomes).
+A dotted grey bar on the graph represents the **soft core threshold** which is the lower limit for which families are present in the majority of genomes. By default, this value is 95% (so families are in more than 95 genomes).
 Look at [here](../PangenomeAnalyses/pangenomeAnalyses.md#u-shape-plot) to change the default parameters.
 
 
 #### Tile plot: detect pangenome structure and outlier
-A tile plot is a heatmap representing the gene families (y axis) in the organisms (x axis) making up your pangenome. 
+A tile plot is a heatmap representing the gene families (y-axis) in the organisms (x-axis) making up your pangenome. 
 The tiles on the graph will be colored if the gene family is present in an organism and uncolored if absent. 
 The gene families are ordered by partition, and the genomes are ordered by a hierarchical clustering based on their shared gene families (basically two genomes that are close together in terms of gene family composition will be close together on the figure).
 
@@ -73,24 +71,24 @@ If you want the 'cloud' gene families even if a lot of data can be hard to open
 you can generate the tile plot as explain [here](../PangenomeAnalyses/pangenomeFigures.md#tile-plot)
 ```
 
-#### Rarefaction curve: indicator of the taxonomic group diversity
+#### Rarefaction curve: an indicator of the taxonomic group diversity
 
 ```{note}
-The rarefaction curve is not drawn by default in the 'workflow' subcommand as it requires a lot of computations. 
+The rarefaction curve is not drawn by default in the 'workflow' subcommand as it requires heavy computation. 
 To compute it add the option `--rarefaction` to the subcommand or look at [here](../PangenomeAnalyses/pangenomeFigures.md#rarefaction-curve) for more information. 
 ```
 The rarefaction curve represents the evolution of the number of gene families for each partition as you add more genomes to the pangenome.
 It has been used a lot in the literature as an indicator of the diversity that you are missing with your dataset on your taxonomic group.
 The idea is that if at some point when you keep adding genomes to your pangenome you do not add any more gene families,
 you might have access to your entire taxonomic group's diversity.
-On the contrary if you are still adding a lot of genes you may be still missing a lot of gene families.
+On the contrary, if you are still adding a lot of genes you may be still missing a lot of gene families.
 
-There are 8 partitions represented. For each of the partitions there are multiple representations of the observed data.
-You can find the observed: *means*, *medians*, *1st* and *3rd quartiles* of the number of gene families per number of genome used. 
+There are 8 partitions represented. For each of the partitions, there are multiple representations of the observed data.
+You can find the observed: *means*, *medians*, *1st* and *3rd quartiles* of the number of gene families per number of genomes used. 
 And you can find the *fitting* of the data by the **Heaps' law**, which is usually used to represent this evolution of the diversity in terms of gene families in each of the partitions.
 
 ### pangenomeGraph files
-The pangenome's graph can be given through multiple data formats, in order to manipulate it with other softwares.
+The pangenome's graph can be given through multiple data formats, in order to manipulate it with other software.
 All the formats provided by PPanGGOLiN are describe [here](../PangenomeAnalyses/pangenomeAnalyses.md#pangenome-graph-output)