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Avana Historical Dataset

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dataset
modified on 14.05.2020, 21:01

***Provided only for reproducibility purposes***

***This dataset was generated as a provisional internal dataset***

***A number of lines included in this dataset were since determined to fail quality control and therefore were not made public***

***Do not use this dataset for new analyses and instead use the most recent data available on depmap.org***


This Achilles dataset contains the results of genome-scale CRISPR knockout screens for 17673 in 578 cell lines. It was processed using the following steps:


- Sum raw readcounts by replicate and guide

- Remove the list of guides with suspected off-target activity

- Remove guides with pDNA counts less than one millionth of the pDNA pool

- Remove replicates that fail fingerprinting match to parent or derivative lines

- Remove replicates with total reads less than 15 million

- Remove replicates that do not have a Pearson coefficient > .7 with at least one other replicate for the line

- Calculate log2-fold-change from pDNA counts for each replicate

- Calculate the average SSMD for each cell line using guides targeting the Hart reference essentials and non-essentials, and remove those with values more positve than -0.5. See Hart et al., Mol. Syst. Biol, 2014.

- Run CERES. See http://www.biorxiv.org/content/early/2017/07/10/160861

- Identify pan-dependent genes as those for whom 90% of cell lines rank the gene above a given dependency cutoff. The cutoff is determined from the central minimum in a histogram of gene ranks in their 90th percentile least dependent line.

- For each CERES gene score, infer the probability that the score represents a true dependency or not. This is done using an EM step until covergence indepedently in each cell line. The dependent distribution is determined empirically from the scores of the pan-dependent genes. The null distribution is determined from unexpressed gene scores in those cell lines that have expression data available, and from the Hart non-essential gene list in the remainder.


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Dataset contents:

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Post-CERES files:


gene_effect - NumericalMatrix

CERES data normalized to positive controls.

Columns: genes in the format “HUGO (Entrez)”

Rows: cell lines in the format “ID_PRIMARYSITE”


gene_dependency - NumericalMatrix

Probability that knocking out the gene has a real depletion effect.

Columns: genes in the format “HUGO (Entrez)”

Rows: cell lines in the format “ID_PRIMARYSITE”


gene_fdr - NumericalMatrix

If the given gene and all genes scoring left of it in the given cell line are treated as dependencies, the matrix entry gives the fraction of those genes are not true dependencies (the FDR for that cell line).

Columns: genes in the format “HUGO (Entrez)”

Rows: cell lines in the format “ID_PRIMARYSITE”


guide_efficacy - Table

Columns:

sgrna (nucleotides)

efficacy - CERES inferred efficacy for the guide


pan_dependent_genes - Raw

List of genes identified as dependencies in all lines, one per line. The scores of these genes are used as the dependent distribution for inferring dependency probability.


Pre-CERES files:


essential_genes - Raw

List of genes used as positive controls, currently the 217 Hart panessentials in the format “HUGO (Entrez)”. Each entry is separated by a newline.


nonessential_genes - Raw

List of genes used as negative controls (Hart nonessentials) in the format “HUGO (Entrez)”. Each entry is separated by a newline.


raw_readcounts - NumericalMatrix

Summed counts for each replicate/PDNA

Columns: replicate/pDNA IDs

Rows: Guides (nucleotides)


logfold_change - NumericalMatrix

Post-QC log2-fold change (not ZMADed)

Columns: replicate IDs

Rows: Guides (nucleotides)


guide_gene_map - Table

Columns:

sgrna (nucleotides) - appears more than once

genome_alignment

gene (“HUGO (Entrez)”)

n_alignments (integer number of perfect matches for that guide)


copy_number - Table

Segmented copy number data for included lines

Columns:

cell_line_name (“ID_PRIMARYSITE”)

Chromosome (integer, X, Y)

Start (bp)

End (bp)

Num_Probes

Segment_Mean (logfold change from average)


replicate_map - Table

Columns:

replicate_ID (str)

cell_line_name (str): CCLE_name (“ID_PRIMARYSITE”)

pDNA_batch (int): indicates which processing batch the replicate belongs to and therefore which pDNA reference it should be compared with.


dropped_guides - Raw

Guides dropped for suspected off-target activity, separated by newlines.


Annotations:


sample_info - Table

Columns:

cell_line (“ID_PRIMARYSITE”)

n_replicates (int): number of replicates surviving QC

primary_tissue (str): from masterfile

histology (str): from masterfile

histology_subtype (str): from masterfile

type (str): from masterfile

tumor_type (str): from masterfile

cas9_activity (float): percentage score from masterfile

culture_medium (str): from GPP_CRISPR tab, Q

culture_code (str): from GPP_CRISPR tab, R

culture_type (str): “adherent” or none, from masterfile

clean_cell_line_name

cell_line_SSMD

aliases