# Download processed data just before Ceres/Chronos

**URL:** <https://forum.depmap.org/t/download-processed-data-just-before-ceres-chronos/976>\
**Category:** Q&A\
**Tags:** genetic-screens\
**Created:** [October 20, 2021, 4:27pm UTC](https://forum.depmap.org/t/download-processed-data-just-before-ceres-chronos/976 "2021-10-20T16:27:19Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![adriaft](https://avatars.discourse-cdn.com/v4/letter/a/3da27b/32.png) [@adriaft](https://forum.depmap.org/u/adriaft)\
**Post date:** [October 20, 2021, 4:27pm UTC](https://forum.depmap.org/t/download-processed-data-just-before-ceres-chronos/976/1 "2021-10-20T16:27:19Z")

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Hi! I’m interested in having the data just before the copy number correction is implemented (Ceres, Chronos or whatever). I saw you can download the raw logFC as well as the mappings and quality checks that were used to aggregate/discard the samples/genes…

I was wondering whether you could provide the final resulting dataset (before Chronos) or at least the code to implement the missing steps from the logFC so that I can be sure I have the same data used to feed Chronos/Ceres.

Thanks! 😌

---

<div class="post-metadata">

**Author:** ![iboyle](https://yyz1.discourse-cdn.com/flex035/user_avatar/forum.depmap.org/iboyle/32/69_2.png) [@iboyle](https://forum.depmap.org/u/iboyle)\
**Post date:** [October 26, 2021, 2:15pm UTC](https://forum.depmap.org/t/download-processed-data-just-before-ceres-chronos/976/2 "2021-10-26T14:15:23Z")

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Hello! For CERES you could use the Achilles\_gene\_effect\_unscaled file to get the gene effect scores prior to CN correction (Please note: CERES will not be run in future releases). For Chronos, you can use code below to generate the model inputs. Please let me know if there are any issues!

```auto
'''
Generate the inputs for Chronos from the downloads available on 
the DepMap Downloads page (https://depmap.org/portal/download/).  

Required files:
- Achilles_guide_map.csv
- Achilles_replicate_map.csv
- Achilles_raw_readcounts.csv
'''

import pandas as pd

## Guide Map ##

#read in guide map
full_guide_map = pd.read_csv('Achilles_guide_map.csv')
#filter out guides which map to multiple genes
guide_counts = full_guide_map.groupby('sgrna').gene.count()
guide_map = full_guide_map[full_guide_map.sgrna.isin(
    guide_counts.loc[lambda x: x == 1].index
)]
#filter out guides with multiple alignments
alignment_counts = guide_map.groupby('sgrna').genome_alignment.count()
guide_map = guide_map[guide_map.sgrna.isin(
    alignment_counts.loc[lambda x: x == 1].index
)]
#only keep genes targeted by multiple guides
gene_counts = guide_map.groupby('gene').sgrna.count()
guide_map = guide_map[guide_map.gene.isin(gene_counts.loc[lambda x: x > 1].index)]

## Readcounts ##

#read in readcounts then trim to match selected sgrnas
readcounts = pd.read_csv('Achilles_raw_readcounts.csv', index_col=0)
readcounts = readcounts.loc[readcounts.index.isin(guide_map.sgrna)]

## Sequence Map ##

#read in replicate map to make sequence map
sequence_map = pd.read_csv('Achilles_replicate_map.csv')
#rename columns
sequence_map.rename(columns={'replicate_ID': 'sequence_ID', 'DepMap_ID': 'cell_line_name'}, inplace=True)
#annotate pDNA batches
sequence_map.cell_line_name.fillna('pDNA', inplace=True)
#set number of days for screens to 21 and pDNA to 0
sequence_map.loc[sequence_map.query('cell_line_name != "pDNA"').index, 'days'] = 21
sequence_map.days.fillna(0, inplace=True)

## Remove QC Failures ##

#keep only replicates which pass QC
sequence_map = sequence_map.query('passes_QC')
#trim sequence map and readcounts to contain same replicates
readcounts = readcounts.loc[:,readcounts.columns.isin(sequence_map.sequence_ID)].T
sequence_map = sequence_map.loc[sequence_map.sequence_ID.isin(readcounts.index)]

print("Chronos Inputs Generated")
print("- guide map (table with {} sgrnas, {} genes)".format(guide_map.sgrna.nunique(), guide_map.gene.nunique()))
print("- readcounts (matrix of {} replicates x {} sgrnas)".format(readcounts.shape[0], readcounts.shape[1]))
print("- sequence map (table with {} replicates, {} cell lines)".format(sequence_map.sequence_ID.nunique(), sequence_map.cell_line_name.nunique()))

## Example Chronos Run ##
#
# import chronos
# chronos.nan_outgrowths( #note will introduce NAs into dataframe inplace
# readcounts=readcounts, 
# guide_gene_map=guide_map, 
# sequence_map=sequence_map
# )
# model = chronos.Chronos(
# sequence_map={"avana": sequence_map},
# guide_gene_map={"avana": guide_map},
# readcounts={"avana": readcounts}
# )
# model.train(nepochs=1001)
# model.save("chronos_outputs")
#
# Note: additional post-processing steps include scaling and copy number correction

```