Tutorial

This tutorial uses data from the DepMap.


Open Project Achilles Gene Essentiality Scores

Download the CRISPR dataset (Achilles_gene_effect.csv). This dataset facilitates the discovery of genes that when knocked down/out effect the viability of particular cell lines.
Morpheus can also open data from Excel, text files, the clipboard, a URL, and Dropbox.

Transpose

Select Tools > Transpose to put the genes on the rows and the cell lines on the columns.

Adjust Color Scheme

Select View > Options to edit the color scale. By default, values in the heat map are mapped to colors using the minimum and maximum of each row independently. With z-scored data, a fixed scale should be used. Set the range of the fixed color scale to -2, 2.
Video

Add Cell Line Annotations

Download the cell line metadata (sample_info.csv). Select File > Open and then select "Annotate Columns" for "Open File Action". Next go to View > Options and select the Annotations tab. Select "id", "lineage", and "sample_collection_site" for column annotations.

Variance

Click Tools > Create Calculated Annotation.
Enter "variance" for the "Annotation name".
Enter "VARIANCE()" for "Formula" and click "OK".
A new row annotation named "variance" will appear to the right of the heat map.

Filter Rows

Show only the top 500 most variable genes by variance
Click Tools > Filter to open the filter dialog.
Click the "Add" button and set the field name to "variance".
Click "Switch to top N filter" and enter "500" for "N".

Hierarchical Clustering

Click Tools > Hierarchical Clustering. Change "Cluster" to "Rows and columns". Click "OK" to run the analysis.
Do haematopoietic cell lines cluster together? Try searching the columns for "haematopoietic_and_lymphoid_tissue". Right-click on the sample_collection_site header and select "Highlight matching values". Hover over the sample_collection_site values to highlight cell lines from the same site. You can dynamically cut the dendrogram by dragging the dashed line at the top of the dendrogram.
Hierarchical clustering recursively merges objects based on their pair-wise distance. Objects closest together are merged first, objects furthest apart are merged last. The result is a tree structure, referred to as a dendogram, where the leaf nodes represent the original items and internal (higher) nodes represent the merges that occurred. Click here for a more detailed description of the hierarchical clustering algorithm and here for a comparison of the Pearson and Spearman correlation methods.

Compress Heat Map

Click View > Fit To Window to compress the heat map. Select View > 100% to return to the original heat map size.

Remove Row Filter

Select Tools > Filter and delete the variance filter to show all rows.

Remove Dendrograms

Right-click on the column dendrogram and select "Delete". Do the same for the row dendrogram.

Sort Rows

Click the variance row annotation header in the heat map once to sort the heat map in ascending order by the variance. Click it again to sort the heat map in descending. The third click sorts by alternating groups of ten of most similar and dissimilar items. Note that you can shift-click to sort multiple columns simultaneously.

Search Results To Top

Enter "BRAF" in the row search box.
Click to bring the matches to the top of the heat map.

Visual Enrichment

Double-click on BRAF row to sort by dependency score.
Search columns for "skin".
Do skin cell lines seem to be more dependent on BRAF than cell lines from other lineages?
You can optionally limit your search to within a field by typing the field name followed by a colon ":" and then the term you are looking for.

Nearest Neighbors

Click Tools > Nearest Neighbors. Click "OK" to run the analysis.
A new row annotation named "Pearson correlation" will appear.
What genes correlate with BRAF?

Append Dataset

Download the PRISM drug sensitivity data (primary_replicate_collapsed_logfold_change.csv) and select File > Open. Open the dataset in a new tab. Select Tools > Transpose to put the compounds on the rows and the cell lines on the columns. Select File > Save Dataset to save the transposed matrix and enter "prism_logfold_change.gct" for the file name. Navigate back to the gene essentiality dataset tab. Select File > Open to open the transposed drug sensitivity matrix. Change "Open File Action" to "Append rows to current dataset". Match the cell line ids in the CRISPR dataset with the cell line ids in the drug sensitivity dataset.

Repeat Nearest Neighbors With drug sensitivity dataset

Create a new tab with BRAF CRISPR dependency scores and drug sensitivity scores by searching rows for "Source:prism_logfold_change BRAF". Ensure that all columns are selected. Click Tools > New Heat Map to create a new tab. Download the compound annotations (primary_replicate_collapsed_treatment_info.csv) and select File > Open. Choose "Annotate rows" for "Open File Action". In the new tab, search for BRAF and bring the matches to the top. Select the BRAF dependency scores. Click Tools > Nearest Neighbors. Click "OK" to run the analysis.
A new row annotation named "Pearson correlation" will appear.
What drugs correlate with BRAF dependency scores?

Overlay RNAi data on top of CRISPR data to create dot plots

Create a new tab with the CRISPR dependency scores only. Remove the number after the gene name by selecting Tools>Create Calculated Annotation. Enter "id_stripped" for the annotation name and FIELD('id').substring(0, FIELD('id').indexOf('(')) for the formula.
Download the RNAi dataset (D2_Achilles_gene_dep_scores.csv) and select File>Open. Repeat the same process to remove the numbers after the gene name by selecting Tools>Create Calculated Annotation. Enter "id_stripped" for the annotation name and FIELD('id').substring(0, FIELD('id').indexOf('(')) for the formula. Select File > Save Dataset to save the updated dataset and enter "D2_Achilles_gene_dep_scores.gct" for the file name. Navigate back to the CRISPR tab and select File > Open. Change "Open File Action" to "Overlay onto current dataset". Match the "CCLE Name" in the CRISPR dataset with "id" in the RNAi dataset and the "id_stripped" annotation in the CRISPR dataset with "id_stripped" in the RNAi dataset. Select View > Options to edit the color scale. Change "shape" to "circle" and "size by" to the RNAi dataset. Set the size by minimum to 2 and the maximum to -2.

Explore mutational data

Download the CCLE Mutations dataset (CCLE_mutations.csv). Rename the file to have the suffix .maf.csv (e.g. CCLE_mutations.maf.csv) so that Morpheus will recognize the file type properly.