From 89e9e94bef41f643365c5c93d9c055fde63fa346 Mon Sep 17 00:00:00 2001 From: drosofff Date: Tue, 6 Feb 2024 19:57:16 +0100 Subject: [PATCH 1/2] start comparisons and fix typos --- .../28_1_manipulation_edgeR_data.md | 6 +- .../28_2_manipulation_limma_data.md | 393 ++++++++++++++++++ docs/bulk_RNAseq-IOC/29_0_comparisons.md | 20 + mkdocs.yml | 8 +- 4 files changed, 421 insertions(+), 6 deletions(-) create mode 100644 docs/bulk_RNAseq-IOC/28_2_manipulation_limma_data.md create mode 100644 docs/bulk_RNAseq-IOC/29_0_comparisons.md diff --git a/docs/bulk_RNAseq-IOC/28_1_manipulation_edgeR_data.md b/docs/bulk_RNAseq-IOC/28_1_manipulation_edgeR_data.md index 0ef2c03d7..887d2efc8 100644 --- a/docs/bulk_RNAseq-IOC/28_1_manipulation_edgeR_data.md +++ b/docs/bulk_RNAseq-IOC/28_1_manipulation_edgeR_data.md @@ -1,6 +1,6 @@ # Manipulation of edgeR data for visualisation and comparisons -Now we would like to extract the most differentially expressed genes in the various +As we did for DESeq2 we will extract the most differentially expressed genes in the various conditions, and then visualize them using an heatmap of the normalized counts for each sample. @@ -239,7 +239,7 @@ expression` :warning: Rename the collection `Select on collection...` to `edgeR top down-regulated gene lists` -:warning: keep the last three generated collections for later comparison with edgeR and +:warning: keep the last three generated collections for later comparison with DESeq2 and limma tools ## Plotting an heatmap of the most significantly de-regulated genes @@ -376,7 +376,7 @@ heatmap with high number of rows` tool !!! info "![](images/tool_small.png){width="25" align="absbottom"} `Plot heatmap with high number of rows` settings" - Input should have column headers - these will be the columns that are plotted - --> `rLog-Normalized counts of DE genes` + --> Click the collection icon and select `edgeR Log2CPM Normalized counts of DE genes` - Data transformation --> `Plot the data as it is` diff --git a/docs/bulk_RNAseq-IOC/28_2_manipulation_limma_data.md b/docs/bulk_RNAseq-IOC/28_2_manipulation_limma_data.md new file mode 100644 index 000000000..5d5db7956 --- /dev/null +++ b/docs/bulk_RNAseq-IOC/28_2_manipulation_limma_data.md @@ -0,0 +1,393 @@ +# Manipulation of limma data for visualisation and comparisons + +Now we would like to extract the most differentially expressed genes in the various +conditions, and then visualize them using an heatmap of the normalized counts for each +sample. + +## Extract the most differentially expressed genes (PRJNA630433 / limma) + +Basically, we navigate in the limma history of the PRJNA630433 use-case and we **repeat a +limma run**, asking in addition for a file containing the normalised counts, these are in +**log2 counts per million (logCPM)**. + +Note that, as edgeR, limma returns **log2 counts per million (logCPM)**. + +??? info "![](images/tool_small.png){width="25" align="absbottom"} `limma` settings" + - Differential Expression Method + + --> `limma-voom` + - Apply voom with sample quality weights? + + --> `No` + - Count Files or Matrix? + + --> Separate Count Files + - 1: Factor/Name + + --> Tissue + - 1: Factor/1: Group + + Note that there will be three Groups (ie factor levels) in this analysis: Dc, Mo and Oc. + + --> Oc + + - Counts file(s) + + --> select the data collection icon, then `15: Oc FeatureCounts counts` + - 2: Factor/2: Group + + --> Mo + + - Counts file(s) + + --> select the data collection icon, then `10: Mo FeatureCounts counts` + - 3: Factor level (you must click on :heavy_plus_sign: `Insert Group`) + + --> Dc + + - Counts file(s) + + --> select the data collection icon, then `5: Mo FeatureCounts counts` + - Use Gene Annotations? + + --> `No` + - Input Contrast information from file? + + --> `No` + - 1: Constrast + + --> `Mo-Dc` + - 2: Constrast (click :heavy_plus_sign: `Insert Contrast`) + + --> `Oc-Dc` + - 3: Constrast (click :heavy_plus_sign: `Insert Contrast`) + + --> `Oc-Mo` + - Filter Low Counts + + --> No, leave folded + - Output options + + --> Unfold and select `Output Normalised Counts Table?: Yes` + - Advanced options + + --> Put `P-Value Adjusted Threshold` to 0.1 (to be consistent with DESeq settings) + + --> Leave other advanced options unchanged + - `Run Tool` + +:warning: Note that limma is nicer than edgeR (in Galaxy) and return a single extra dataset + `limma on data ... and others: Normalised counts`. Thus, no need here to do collection + manipulations. + +Beside, as with edgeR, a `limma-voom_normcounts.tsv` also show up as a html link in the dataset `limma on +data 4, data 3, and others: Report`, that download directly to your local computer if you +click it. + +:warning: Keep the dataset `limma on ... and others: Normalised counts` for latter, we will use +it for the clustered heatmap. + +## Generate top lists of limma DE genes + +### Select genes with |log2FC > 2| and p-adj < 0.01 with ![](images/tool_small.png){width="30" align="absbottom"}`Filter data on any column using simple expressions` + +!!! info "![](images/tool_small.png){width="25" align="absbottom"} `Filter data on any column...` settings" + - Filter + + --> limma on data ... others: DE tables (:warning: this is a collection) + - With following condition + + --> `abs(c2) > 2 and c6 < 0.01` :warning: this expression is different from the one + used for DESeq2 tables because the column structure is different. Actually this is + the same expression that the one used for edgeR. + - Number of header lines to skip + + --> `1` (these tables have an added header !) + - Click the `Run Tool` button + +:warning: Rename the "filter on..." collection to `limma Top gene lists` + +### Compute a boolean value by row + +This is to determine whether genes in the lists are up or down-regulated + +!!! info "![](images/tool_small.png){width="25" align="absbottom"} `Compute on rows` settings" + - Input file + + --> `limma Top gene lists` (:warning: collection !) + - Input has a header line with column names? + + --> `Yes` + - 1: Expressions + - Add expression + + --> `c2 > 0` + tables + - Mode of the operation + + --> `Append` + - The new column name + + --> `Regulation` + - Avoid scientific notation in any newly computed columns + + --> `No` + - Click the `Run Tool` button + +:warning: Look at the effect of evaluating the expression `c2 > 0` in the new column +`expression` in the output datasets. + +### Transform `True` and `False` values to `up` and `down`, respectively + +!!! info "![](images/tool_small.png){width="25" align="absbottom"} `Column Regex Find And Replace` settings" + - Select cells from + + --> `Compute on collection 32 (or so)` + - using column + + --> `8` (for limma there are now 8 columns...) + - Check + + --> click the button :heavy_plus_sign:`Insert Check` + - Find Regex + + --> `False` + - Replacement + + --> `down` + - Check + + --> click another time the button :heavy_plus_sign:`Insert Check` + - Find Regex + + --> `True` + - Replacement + + --> `up` + - Click the `Run Tool` button + +:warning: rename the collection `Column Regex Find And Replace on collection 44` with +`limma top gene lists - oriented` + +### Split the lists in `up` and `down` regulated lists + +This will be performed through 2 successive runs of the +![](images/tool_small.png){width="25" align="absbottom"} tool `Select lines that match an +expression` + +!!! info "![](images/tool_small.png){width="25" align="absbottom"} `Select lines that match an expression` settings" + - Select lines from + + --> `limma top gene lists - oriented` + - that + + --> `matching` + - the pattern + + --> `\tup` (a tabulation immediately followed by the string *up*) + - Keep header line + + --> `Yes` + - Click the `Run Tool` button + +:warning: Immediately rename the collection `Select on collection...` to `limma top up-regulated +gene lists` + +Redo exactly the same operation with a single change in the setting of the +![](images/tool_small.png){width="25" align="absbottom"} tool `Select lines that match an +expression` + +??? info "![](images/tool_small.png){width="25" align="absbottom"} `Select lines that match an expression` settings" + - Select lines from + + --> `limma top gene lists - oriented` + - that + + --> `matching` + - the pattern + + --> `\tdown` (a tabulation immediately followed by the string *down*) + - Keep header line + + --> `Yes` + - Click the `Run Tool` button + +:warning: Rename the collection `Select on collection...` to `limma top down-regulated +gene lists` + +:warning: keep the last three generated collections for later comparison with edgeR and +DESeq2 tools + +## Plotting an heatmap of the most significantly de-regulated genes + +For this, we are going to collect and gather all significantly de-regulated genes in any of the +3 conditions, and to intersect (join operation) this list with the rLog normalized count +table precedently generated. + +### Use ![](images/tool_small.png){width="25" align="absbottom"}`Advanced cut` to select the list of deregulated genes in all three comparisons + +!!! info "![](images/tool_small.png){width="25" align="absbottom"} `advanced cut` settings" + - File to cut + + --> `limma Top gene lists` (this is a collection) + - Operation + + --> `Keep` + - Delimited by + + --> `Tab` + - Cut by + + --> `fields` + - List of Fields + + --> `Column 1` + - First line is a header line + - Click the `Run Tool` button + +:warning: Rename this collection of single column datasets `limma top genes names` + +### Next we concatenate the three datasets of the previous collection in a single dataset + +We do that using the ![](images/tool_small.png){width="25" align="absbottom"} +`Concatenate multiple datasets tail-to-head while specifying how` tool + +!!! info "![](images/tool_small.png){width="25" align="absbottom"} `Concatenate multiple datasets tail-to-head while specifying how` settings" + - What type of data do you wish to concatenate? + + --> `Single datasets` + - Concatenate Datasets + + --> :warning: Click on the collection icon and select `limma top genes names` + - Include dataset names? + + --> `No` + - Number of lines to skip at the beginning of each concatenation: + + --> `1` + - Click the `Run Tool` button + +:warning: Rename the return single dataset as `limma Pooled top genes` + +### Next we extract *Uniques* gene names from the `Pooled top genes` dataset + +You probably agree that the same gene may be deregulated in the three pair-wise comparisons +which we have performed with DESeq2. + +Thus we need to eliminate the redundancy, using the tool +![](images/tool_small.png){width="25" align="absbottom"}`Unique +occurrences of each record`. + +!!! info "![](images/tool_small.png){width="25" align="absbottom"} `Unique occurrences of each record` settings" + - File to scan for unique values + + --> `limma Pooled top genes` + - Ignore differences in case when comparing + + --> `No` + - Column only contains numeric values + + --> `No` + - Advanced Options + + --> Leave as `Hide Advanced Options` + - Click the `Run Tool` button + +### Add a header the list of unique gene names associated we significant DE in any of the comparisons + +We do this with the tools `Add Header` + +!!! info "![](images/tool_small.png){width="25" align="absbottom"} `Add Header` settings" + - List of Column headers (comma delimited, e.g. C1,C2,...) + + --> `limma_All_DE_genes` + - Data File (tab-delimted) + + --> `Unique on data 7x...` + - Click the `Run Tool` button + +:warning: Rename the generated dataset `limma_All_DE_genes` + +### Intersection (join operation) between the list of unique gene name associated with DE and the rLog-Normalized counts file. + +This is the moment when we are going to use the dataset `limma on ... and others: Normalised counts` + and intersect it (join operation) with the list of DE genes in all three condition. + +To do this, we are going to use the tool +![](images/tool_small.png){width="25" align="absbottom"}`Join two files` + +!!! info "![](images/tool_small.png){width="25" align="absbottom"} `Join two files` settings" + - 1st file + + --> select `limma on ... and others: Normalised counts` + - Column to use from 1st file + + --> `1` + - 2nd File + + --> `limma_All_DE_genes` + - Column to use from 2nd file + + --> `1` + - Output lines appearing in + + --> `Both 1st and 2nd files` + - First line is a header line + + --> `Yes` + - Ignore case + + --> `No` + - Value to put in unpaired (empty) fields + + --> `NA` + - Click the `Run Tool` button + +:warning: Rename the single-element output collection `limma Log2CPM Normalized counts of DE genes` + +### Plot a heatmap of the Log2CPM Normalized counts of limma DE genes in all three conditions + +We do this using the ![](images/tool_small.png){width="25" align="absbottom"}`Plot +heatmap with high number of rows` tool + +!!! info "![](images/tool_small.png){width="25" align="absbottom"} `Plot heatmap with high number of rows` settings" + - Input should have column headers - these will be the columns that are plotted + + --> Click the collection icon and select `limma Log2CPM Normalized counts of DE genes` + - Data transformation + + --> `Plot the data as it is` + - Enable data clustering + + --> `Yes` + + - Clustering columns and rows + + --> `Cluster rows and not columns` + - Distance method + + --> `Euclidean` + + - Clustering method + + --> `Complete` + - Labeling columns and rows + + --> `Label columns and not rows` + + - Coloring groups + + --> `Blue to white to red` + - Data scaling + + --> `Scale my data by row` + - tweak plot height + + --> `35` + - tweak row label size + + --> `1` + - tweak line height + + --> `24` + - `Run Tool` diff --git a/docs/bulk_RNAseq-IOC/29_0_comparisons.md b/docs/bulk_RNAseq-IOC/29_0_comparisons.md new file mode 100644 index 000000000..44e826536 --- /dev/null +++ b/docs/bulk_RNAseq-IOC/29_0_comparisons.md @@ -0,0 +1,20 @@ +To compare DESeq2, edgeR and limma, we are now going to collect the lists of genes +generated in the three corresponding histories in a new history which we name `DESeq2, edgeR, +limma comparisons`. + +Thus, create this history `DESeq2, edgeR, limma comparisons`, and from this history, copy +the required datasets from all three histories. + +- [x] in the history `PRJNA630433 DESeq2 analysis`, get the datasets + - top gene lists - oriented + - top up-regulated gene lists + - top down-regulated gene lists +- [x] in the history `PRJNA630433 edgeR analysis`, get the datasets + - edgeR top gene lists - oriented + - edgeR top up-regulated gene lists + - edgeR top down-regulated gene lists +- [x] in the history `PRJNA630433 limma analysis`, get the datasets + - limma top gene lists - oriented + - limma top up-regulated gene lists + - limma top down-regulated gene lists + diff --git a/mkdocs.yml b/mkdocs.yml index 51272d0c4..ef0731912 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -80,19 +80,21 @@ nav: - Week 4: - Review on week-3 work: bulk_RNAseq-IOC/24_exercices_week_03_review.md - Differential Gene Expression Analysis: bulk_RNAseq-IOC/25_DE_intro.md - - Week 3 exercices: + - Week 4 exercices: - DESeq2: bulk_RNAseq-IOC/26_deseq2.md - edgeR: bulk_RNAseq-IOC/27_edgeR.md - limma: bulk_RNAseq-IOC/27_1_limma.md - DE_manipulations: - DESeq2: bulk_RNAseq-IOC/28_manipulation_DEseq2_data.md - edgeR: bulk_RNAseq-IOC/28_1_manipulation_edgeR_data.md - - Volcano plot: bulk_RNAseq-IOC/29_volcano.md + - limma: bulk_RNAseq-IOC/28_2_manipulation_limma_data.md + - DEseq/edgeR/limma comparison: bulk_RNAseq-IOC/29_0_comparisons.md + - Volcano plots: bulk_RNAseq-IOC/29_volcano.md - Week 5: - Review on week-3 work: bulk_RNAseq-IOC/30_exercices_week_04_review.md - Gene Ontology Enrichment Analysis: bulk_RNAseq-IOC/31_GO_enrichment_intro.md - - Week 4 exercices: + - Week 5 exercices: - GOseq: bulk_RNAseq-IOC/32_GOseq.md - Week 6: From 1b030f220bae1f5b5d6da90b5940cca245cd86ca Mon Sep 17 00:00:00 2001 From: drosofff Date: Thu, 15 Feb 2024 01:24:54 +0100 Subject: [PATCH 2/2] DE caller comparison --- .../28_2_manipulation_limma_data.md | 2 + .../28_manipulation_DEseq2_data.md | 6 +- docs/bulk_RNAseq-IOC/29_0_comparisons.md | 143 +++++++++++++++--- .../images/history_comparison.png | Bin 0 -> 53846 bytes docs/bulk_RNAseq-IOC/images/jVenn_chart.png | Bin 0 -> 46189 bytes 5 files changed, 129 insertions(+), 22 deletions(-) create mode 100644 docs/bulk_RNAseq-IOC/images/history_comparison.png create mode 100644 docs/bulk_RNAseq-IOC/images/jVenn_chart.png diff --git a/docs/bulk_RNAseq-IOC/28_2_manipulation_limma_data.md b/docs/bulk_RNAseq-IOC/28_2_manipulation_limma_data.md index 5d5db7956..4bb43899d 100644 --- a/docs/bulk_RNAseq-IOC/28_2_manipulation_limma_data.md +++ b/docs/bulk_RNAseq-IOC/28_2_manipulation_limma_data.md @@ -391,3 +391,5 @@ heatmap with high number of rows` tool --> `24` - `Run Tool` + +--- \ No newline at end of file diff --git a/docs/bulk_RNAseq-IOC/28_manipulation_DEseq2_data.md b/docs/bulk_RNAseq-IOC/28_manipulation_DEseq2_data.md index 55da68f43..3939487e4 100644 --- a/docs/bulk_RNAseq-IOC/28_manipulation_DEseq2_data.md +++ b/docs/bulk_RNAseq-IOC/28_manipulation_DEseq2_data.md @@ -293,13 +293,13 @@ We do this with the tools `Add Header` !!! info "![](images/tool_small.png){width="25" align="absbottom"} `Add Header` settings" - List of Column headers (comma delimited, e.g. C1,C2,...) - --> `All_DE_genes` + --> `DESeq_All_DE_genes` - Data File (tab-delimted) --> `Unique on data 1xx...` - Click the `Run Tool` button -:warning: Rename the generated dataset `All_DE_genes` +:warning: Rename the generated dataset `DESeq_All_DE_genes` ### Intersection (join operation) between the list of unique gene name associated with DE and the rLog-Normalized counts file. @@ -318,7 +318,7 @@ To do this, we are going to use the tool --> `1` - 2nd File - --> `All_DE_genes` + --> `DESeq_All_DE_genes` - Column to use from 2nd file --> `1` diff --git a/docs/bulk_RNAseq-IOC/29_0_comparisons.md b/docs/bulk_RNAseq-IOC/29_0_comparisons.md index 44e826536..d5c860175 100644 --- a/docs/bulk_RNAseq-IOC/29_0_comparisons.md +++ b/docs/bulk_RNAseq-IOC/29_0_comparisons.md @@ -1,20 +1,125 @@ -To compare DESeq2, edgeR and limma, we are now going to collect the lists of genes -generated in the three corresponding histories in a new history which we name `DESeq2, edgeR, -limma comparisons`. - -Thus, create this history `DESeq2, edgeR, limma comparisons`, and from this history, copy -the required datasets from all three histories. - -- [x] in the history `PRJNA630433 DESeq2 analysis`, get the datasets - - top gene lists - oriented - - top up-regulated gene lists - - top down-regulated gene lists -- [x] in the history `PRJNA630433 edgeR analysis`, get the datasets - - edgeR top gene lists - oriented - - edgeR top up-regulated gene lists - - edgeR top down-regulated gene lists -- [x] in the history `PRJNA630433 limma analysis`, get the datasets - - limma top gene lists - oriented - - limma top up-regulated gene lists - - limma top down-regulated gene lists +We are now going to make a quick comparison of the three Differential Expression analysis +packages, DESeq2, edgeR, and limma, using some results generated in the histories +"PRJNA630433 DESeq2 analysis", "PRJNA630433 edgeR analysis" and "PRJNA630433 limma +analysis". +## Collect datasets + +Thus, create a new history `DESeq2, edgeR, limma comparisons`, and from this history, +import (menu `copy datasets`) the required datasets. + +- [x] in the history `PRJNA630433 DESeq2 analysis`, copy the dataset + - `DESeq_All_DE_genes` (This should be the second to last file of the history). It is a + simple list of gene names with a single header) +- [x] in the history `PRJNA630433 edgeR analysis`, get the dataset + - `edgeR_All_DE_genes` (Also probably the second to last file of the history). +- [x] in the history `PRJNA630433 limma analysis`, get the dataset + - `limma_All_DE_genes` + +:warning: You can stay in the current "PRJNA630433 edgeR analysis" and "PRJNA630433 limma +analysis" history to execute these 3 copies. Just change the `Source History` of the copy +dashboard, and be sure that, each time, the `Destination History` is `DESeq2, edgeR, limma +comparisons` + +Your starting history should look like this: + +![](images/history_comparison.png){width="300"} + +## Venn diagram + +The three datasets contain a list of significantly deregulated (up and down) genes in +either of the three comparisons (Mo vs Dc, Oc vs Dc, Oc vs Mo), as returned by DESeq2, +edgeR or Limma-voom, respectively. + +Note that, although there is no quantitative information anymore in these lists, we can +look at their overlaps using a Venn diagram approach. + +We are going to perform this analysis using the Galaxy tool `Venn diagram [JVenn]` + +!!! info "![](images/tool_small.png){width="25" align="absbottom"} `Venn diagram [JVenn]` settings" + - **1: List to compare** + - Enter your list + + --> `Input file containing your list` + - Select your file + + --> `DESeq_All_DE_genes` + + - Does file contain header? + + --> `Yes` + - Column number on which apply the comparison + + --> `c1` + + - Enter the name of this list + + --> `DESeq2` + + - **2: List to compare** + - Enter your list + + --> `Input file containing your list` + - Select your file + + --> `edgeR_All_DE_genes` + + - Does file contain header? + + --> `Yes` + - Column number on which apply the comparison + + --> `c1` + + - Enter the name of this list + + --> `edgeR` + + - **3: List to compare** (after clicking :heavy_plus_sign:**`Insert List to compare`**) + - Enter your list + + --> `Input file containing your list` + - Select your file + + --> `limma_All_DE_genes` + + - Does file contain header? + + --> `Yes` + - Column number on which apply the comparison + + --> `c1` + + - Enter the name of this list + + --> `limma-voom` + + - Run tool + +The venn diagram return by the tool should look like this: + +
![](images/jVenn_chart.png){width="500"}
+ +## Discussion + +DESeq2 appears as a less stringent "Caller" than edgeR: overall only a few genes (10 + 23) +are called by edgeR but not by DESeq2. This is expected because normalisation and statistical +tests (exact Fisher's test) are similar between DESeq2 and edgeR. However DESeq perfoms an +original step of variance shrinking and the authors of DESeq2 claim that this data transformation +allows to call confidently more DE genes without increasing the False Detection Rate (type +I error). Taking only the DE genes common to both DESeq2 and edgeR would allow to further +improve the false detection rate, at the expanse of increased False Negative rate (type II +error) + +Limma appears a bit "transversal" to DESeq2 and edgeR: beside a core of common gene (2713), +Limma calls 83 genes also called by DESeq2 and 23 genes also called by edgeR, respectively. + +Strikingly however, limma calls a substantial number of genes which are not called either +by DESeq2 or edgeR. 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