diff --git a/docs/IonTorrent/Exercises_day2.html b/docs/IonTorrent/Exercises_day2.html
index d251de5..883b26c 100644
--- a/docs/IonTorrent/Exercises_day2.html
+++ b/docs/IonTorrent/Exercises_day2.html
@@ -867,8 +867,13 @@ Getting the data into phyloseq
Select the file file.opti_mcc.shared and in the
Selected files choose the tab Phenodata.
In the column description, write the sample names as
+you want them to appear in result plots. For example, HPc1,
+HPc2 etc.
Create new columns called site and
-bagging by clicking + Add column.
bagging by clicking + Add column. You delete
+the column chiptype or adjust the width of the columns to
+make space if necessary.
Check how the sample names in the column sample
start, and based on this enter the site codes HP and
KEK in the column site. Next, check the
@@ -878,9 +883,9 @@
Getting the data into phyloseq
ps, fill in bagged in the column
bagging.
-Check that your filled in phenodata table matches the example
-below.
-
+Check that your filled in phenodata table matches the example below
+(especially the last two columns).
+
Step 18. Producing a phyloseq
object
Finally! 😄 We are done with pre-processing and ready to convert our
@@ -983,8 +988,8 @@
Taking a closer look at patterns
relative abundance data to produce bar plots.
This will produce a file called ps_relabund.Rda. Select
it and run the OTU relative abundance bar plots tool,
-making sure that you have the Sample column selected in
-Phenodata variable with sequencing sample IDs. There are
+making sure that you have the description column selected
+in Phenodata variable with sequencing sample IDs. There are
quite a few other parameters here that you can also modify. Feel free to
play around with the options but start with these:
@@ -1008,10 +1013,10 @@ Taking a closer look at patterns
Selecting the resulting file ps_clr.Rda, run the
Distance matrices and ordinations tool. Once again, one
must specify a column in the phenodata with unique IDs for each sample
-(i.e. the Sample column). There are options for Euclidean
-and Bray-Curtis distances - choose Euclidean and for the ordination
-type, select nMDS. Also colour the ordination points by choosing
-site in
+(i.e. the description column). There are options for
+Euclidean and Bray-Curtis distances - choose Euclidean and for the
+ordination type, select nMDS. Also colour the ordination points by
+choosing site in
Phenodata variable for grouping ordination points by colour
and define the shape by choosing bagging in
Phenodata variable for grouping ordination points by shape.
diff --git a/eLena_md/IonTorrent/.DS_Store b/eLena_md/IonTorrent/.DS_Store
deleted file mode 100644
index bf3fc3f..0000000
Binary files a/eLena_md/IonTorrent/.DS_Store and /dev/null differ
diff --git a/eLena_md/IonTorrent/Exercises_IonTorrent_day2.Rmd b/eLena_md/IonTorrent/Exercises_IonTorrent_day2.Rmd
index df17579..484b984 100644
--- a/eLena_md/IonTorrent/Exercises_IonTorrent_day2.Rmd
+++ b/eLena_md/IonTorrent/Exercises_IonTorrent_day2.Rmd
@@ -40,11 +40,13 @@ The `Generate input files for phyloseq` tool produces a phenodata file (a file t
i) Select the file `file.opti_mcc.shared` and in the *Selected files* choose the tab *Phenodata*.
-ii) Create new columns called `site` and `bagging` by clicking `+ Add column`.
+ii) In the column `description`, write the sample names as you want them to appear in result plots. For example, *HPc1*, *HPc2* etc. The names must be unique for each sample.
-iii) Check how the sample names in the column `sample` start, and based on this enter the site codes `HP` and `KEK` in the column `site`. Next, check the characters after `HP`and `KEK` in the sample names. When the next character is c, fill in `control` in the column `bagging`. When the next characters are `ps`, fill in `bagged` in the column `bagging`.
+iii) Create new columns called `site` and `bagging` by clicking `+ Add column`. You delete the column `chiptype` or adjust the width of the columns to make space if necessary.
-Check that your filled in phenodata table matches the example below.
+iv) Check how the sample names in the column `sample` start, and based on this enter the site codes `HP` and `KEK` in the column `site`. Next, check the characters after `HP`and `KEK` in the sample names. When the next character is c, fill in `control` in the column `bagging`. When the next characters are `ps`, fill in `bagged` in the column `bagging`.
+
+Check that your filled in phenodata table matches the example below (especially the last two columns).
@@ -53,7 +55,7 @@ Check that your filled in phenodata table matches the example below.
Finally! `r emo::ji("smile")` We are done with pre-processing and ready to convert our files into a `phyloseq` object that will be used in the community analysis steps.
-Select the Mothur shared file `file.opti_mcc.shared` and constaxonomy file `file.opti_mcc.0.03.cons.taxonomy`. Select the tool `Convert Mothur files into phyloseq object`. In *Parameters*, specify the phenodata column including unique IDs for each community profile (the column `sample`). Run the tool.
+Select the Mothur shared file `file.opti_mcc.shared` and constaxonomy file `file.opti_mcc.0.03.cons.taxonomy`. Select the tool `Convert Mothur files into phyloseq object`. In *Parameters*, specify the phenodata column including unique IDs for each community profile (the column `description`). Run the tool.
This tool produces two files:
@@ -129,7 +131,7 @@ After all, we don't want to blindly rely on statistical test output!
Select `ps_pruned.Rda` and run the `Transform OTU counts` tool, selecting `Relative abundances (%)` as the data treatment. We can use relative abundance data to produce bar plots.
-This will produce a file called `ps_relabund.Rda`. Select it and run the `OTU relative abundance bar plots` tool, making sure that you have the `Sample` column selected in `Phenodata variable with sequencing sample IDs`. There are quite a few other parameters here that you can also modify. Feel free to play around with the options but start with these:
+This will produce a file called `ps_relabund.Rda`. Select it and run the `OTU relative abundance bar plots` tool, making sure that you have the `description` column selected in `Phenodata variable with sequencing sample IDs`. There are quite a few other parameters here that you can also modify. Feel free to play around with the options but start with these:
- 1 in Relative abundance cut-off threshold (%) for excluding OTUs
- Class as the level of biological organisation
@@ -152,7 +154,7 @@ Going back to `ps_pruned.Rda`, let's also visualise the data using a multivariat
```
Why might we want to use CLR transformation here, instead of % relative abundances?
```
-Selecting the resulting file `ps_clr.Rda`, run the `Distance matrices and ordinations` tool. Once again, one must specify a column in the phenodata with unique IDs for each sample (i.e. the `Sample` column). There are options for Euclidean and Bray-Curtis distances - choose Euclidean and for the ordination type, select nMDS. Also colour the ordination points by choosing `site` in `Phenodata variable for grouping ordination points by colour` and define the shape by choosing `bagging` in `Phenodata variable for grouping ordination points by shape`.
+Selecting the resulting file `ps_clr.Rda`, run the `Distance matrices and ordinations` tool. Once again, one must specify a column in the phenodata with unique IDs for each sample (i.e. the `description` column). There are options for Euclidean and Bray-Curtis distances - choose Euclidean and for the ordination type, select nMDS. Also colour the ordination points by choosing `site` in `Phenodata variable for grouping ordination points by colour` and define the shape by choosing `bagging` in `Phenodata variable for grouping ordination points by shape`.
Here, we are using Aitchinson distances because we carried out a CLR transformation and chose Euclidean distances. An alternative approach would be to continue from the rarefied dataset (`ps_rarefied.Rda`) and use Bray-Curtis dissimilarities to produce the nMDS. In addition to nMDS, there is another ordination type (db-RDA), but let's focus on the nMDS for now.
diff --git a/eLena_md/IonTorrent/Images/phenodata_IonTorrent.png b/eLena_md/IonTorrent/Images/phenodata_IonTorrent.png
index ebcd5d9..755946a 100644
Binary files a/eLena_md/IonTorrent/Images/phenodata_IonTorrent.png and b/eLena_md/IonTorrent/Images/phenodata_IonTorrent.png differ