A guide to the GEMINI R package

Mahdi Zamanighomi and Sidharth Jain

2019-10-29

Abstract

Systems for CRISPR-based combinatorial perturbation of two or more genes are emerging as powerful tools for uncovering genetic interactions. However, systematic identification of these relationships is complicated by sample, reagent, and biological variability. We develop a variational Bayes approach (GEMINI) that jointly analyzes all samples and reagents to identify genetic interactions in pairwise knockout screens. The improved accuracy and scalability of GEMINI enables the systematic analysis of combinatorial CRISPR knockout screens, regardless of design and dimension.

Introduction

GEMINI follows a basic workflow:

Using counts data derived from a combination CRISPR screen, and annotations for both samples/replicates and guide/gene IDs, GEMINI can identify genetic interactions such as synthetic lethality and recovery.

Model

GEMINI uses the following model:

\(D_{gihjl} = N(x_{gi} * y_{gl} + x_{hj}*x_{hl} + x_{gihj}*s_{ghl}, \tau_{gihjl})\)

Installation

The stable version used in the publication Zamanighomi et al., 2019 can be found on Bioconductor:

For the latest development version, you can download from GitHub using the devtools package:

Import Big Papi data

This is the data published in Najm et al., 2018 (doi: 10.1038/nbt.4048). All data was obtained from Supplementary Tables 2 and 3.

Input

GEMINI takes a counts matrix as follows:

Counts matrix
pDNA 786O.RepA 786O.RepB 786O.RepC A375.RepA
AAAGTGGAACTCAGGACATG;AAAAAAAGAGTCGAATGTTTT 1137 2438 1997 2175 1459
AAAGTGGAACTCAGGACATG;AAAGAGTCCACTCTGCACTTG 1112 2176 1385 1485 1484
AAAGTGGAACTCAGGACATG;AACAGCTCCGTGTACTGAGGC 796 2294 1579 1844 1506
AAAGTGGAACTCAGGACATG;AAGACGAAATTGAAGACGAAG 839 2169 1229 1705 1344
AAAGTGGAACTCAGGACATG;AAGCGTACTGCTCATCATCGT 689 1771 984 1526 524
AAAGTGGAACTCAGGACATG;ACATTGCATACATAGAAGATC 858 2833 1820 1866 1642

GEMINI also requires sample/replicate annotation and guide/gene annotation:

Sample/replicate annotations
colname samplename replicate
pDNA pDNA NA
786O.RepA 786O RepA
786O.RepB 786O RepB
786O.RepC 786O RepC
A375.RepA A375 RepA
A375.RepB A375 RepB
Guide/gene annotation
rowname U6.gene H1.gene
AAAGTGGAACTCAGGACATG;AAAAAAAGAGTCGAATGTTTT HPRT intron 6T
AAAGTGGAACTCAGGACATG;AAAGAGTCCACTCTGCACTTG HPRT intron UBC
AAAGTGGAACTCAGGACATG;AACAGCTCCGTGTACTGAGGC HPRT intron CD81
AAAGTGGAACTCAGGACATG;AAGACGAAATTGAAGACGAAG HPRT intron CD81
AAAGTGGAACTCAGGACATG;AAGCGTACTGCTCATCATCGT HPRT intron HSP90AA1
AAAGTGGAACTCAGGACATG;ACATTGCATACATAGAAGATC HPRT intron CHEK2

These can be used to create a gemini.input object using the gemini_create_input function.

## Merging sample annotations with colnames of counts.matrix...
## Merging guide annotations with rownames()...
## Created gemini input object.

Pre-processing

GEMINI requires log-fold changes as an input, which are calculated using the gemini_calculate_lfc function. A pseudo-count (CONSTANT) of \(32\) is used by default.

Initialization and Inference

To initialize the CAVI approach, a gemini.model object is created using the gemini_initialize function. For large libraries, more cores can be specified to speed up the initialization (see ?gemini_parallelization).

To note, it is highly recommended that at least one negative control gene should be specified here, and all other genes should be paired with at least one negative control. We use CD81 in this example.

In the absence of a negative control gene, the median LFC of all guide pairs targeting each gene is used to initialize the CAVI approach. However, this is only reasonable in all-by-all format screens.

Also to note, the pattern_split argument must describe a separator used in the rownames of the counts.matrix. For example, a semi-colon (“;”) is used in the Big Papi data and therefore specified here. On the other hand, pattern_join is specified by the user to join the gene pairs. For example, if the U6.gene is BRCA2, and the H1.gene is PARP1, the output will be “BRCA2{pattern_join}PARP1”.

## Initializing x
## Initializing y
## Initializing s
## Initializing tau
## Updating mae
## Model initialized.

Inference is performed with the gemini_inference function. For large libraries, more cores can be specified to speed up the inference (see ?gemini_parallelization).

Convergence

After running gemini_inference, the resulting convergence rate can be visualized. If the model is divergent, alternative parameters for the priors should be selected until convergence is achieved. This can be done through cross-validation. Details on this will be added soon.

Scoring and Visualization

To score genetic interactions, use the gemini_score function.

To note, at least one positive control gene (pc_gene) should be specified to remove interactions involving individually lethal genes. If no positive control is explicitly specified, lethality is estimated as described in Zamanighomi et al. In short, the most lethal 1st percentile of individual gene effects is treated as the positive control value.

Additionally, non-interacting gene pairs (nc_pairs) should be specified for the calculation of p-values and false discovery rates (FDRs). If not specified, only scores are calculated, which reflects the relative strengths of interactions in each sample.

## [1] "6T;HPRT intron"       "HPRT intron;UBC"      "HPRT intron;HSP90AA1"
## [4] "CHEK2;HPRT intron"    "AKT1;HPRT intron"

GEMINI’s scores for interactions can be seen in the Score object. Here, we show the top 10 interacting gene pairs in A549, with their scores across all cell lines.

Strong scores for top 10 interactions from A549 in all samples
786O A375 A549 HT29 Meljuso OVCAR8
PARP1;WEE1 0.0453403 1.2379052 1.457047 0.3425539 0.4460586 0.2163869
MAPK1;MAPK3 -0.0161569 0.7844598 1.382672 1.2829978 0.9680055 0.7177184
MAP2K1;PARP1 0.3458751 0.9409076 1.322750 -0.2186067 0.1133638 0.0676618
BRCA2;PARP1 0.3805435 1.0314724 1.320199 0.2962005 0.5850402 0.5321259
AKT2;WEE1 -0.1786742 0.4083603 1.272535 0.5039544 0.3303675 -0.1330083
BRCA1;BRCA2 0.0733881 0.8185404 1.250131 0.6374952 0.9092236 0.4984216
BRCA2;WEE1 -0.1443077 1.0850858 1.233050 0.6096170 0.8376799 0.0318263
BCL2L1;MCL1 -1.4310350 1.1813920 1.156947 -0.2542358 0.5450757 0.5775085
AKT2;BRCA2 -0.1435939 0.1336293 1.141525 0.1821932 0.3429816 -0.1009591
AKT2;PARP1 0.2634264 0.6327186 1.123986 0.2891668 0.2070908 0.0936534

Significant interactions can be identified through the FDR and p-value slots in the Score object. Again, we show the top 10 interacting gene pairs in A549, but now present FDRs across all cell lines.

FDRs for top 10 interactions in A549
786O A375 A549 HT29 Meljuso OVCAR8
BRCA2;PARP1 0.3318825 0.0000590 8.10e-06 0.5034639 0.0132811 0.0310663
PARP1;WEE1 0.9995686 0.0000360 8.10e-06 0.3592091 0.0719585 0.4733377
MAP2K1;PARP1 0.4056093 0.0000746 8.10e-06 0.9959789 0.5728980 0.7975755
MAPK1;MAPK3 0.9995686 0.0002654 8.10e-06 0.0000156 0.0002616 0.0051505
AKT2;WEE1 0.9995686 0.0699194 1.03e-05 0.0649226 0.2031565 0.9547407
BRCA1;BRCA2 0.9995146 0.0001786 1.07e-05 0.0115084 0.0002616 0.0482600
BRCA2;WEE1 0.9995686 0.0000587 1.08e-05 0.0165822 0.0002846 0.8451785
BCL2L1;MCL1 0.9995686 0.0000382 1.92e-05 0.9959789 0.0246745 0.0174912
AKT2;BRCA2 0.9995686 0.4545398 1.96e-05 0.8869871 0.1809226 0.9546189
AKT2;PARP1 0.6929920 0.0028113 2.07e-05 0.5223456 0.3815660 0.7572944

To visualize these interactions, we can use the gemini_boxplot function. For example, in BRCA2-PARP1:

We can see that GEMINI makes adjustments to the individual gene effects of both BRCA2 and PARP1, and adjusts the sample-independent values of each to account for variation in the screen.

This boxplot can also be re-colored by the adjustment made to each guide or guide pair through the sample-independent effects. Guides/guide pairs with the least adjustment to x (grey) are considered to have the least variation within the screen.

Summary

GEMINI can be run on any counts matrix from a pairwise screen. GEMINI computes log fold changes and infers sample-dependent and sample-independent effects using CAVI. Then, GEMINI calculates interaction strength and significance.

The manuscript and more visualization tools will be made available soon.

Session Info

## R Under development (unstable) (2019-10-24 r77329)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.3 LTS
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## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
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## [1] gemini_1.1.0
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