從CNCB單細(xì)胞轉(zhuǎn)錄組fastq文件并定量

安裝CellRangerhttps://support./single-cell-gene-expression/software/downloads/latest?cd

~/APP

wget

-O

cellranger-7.1.0.tar.xz

"https://cf./releases/cell-exp/cellranger-7.1.0.tar.xz?Expires=1674686023&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZi4xMHhnZW5vbWljcy5jb20vcmVsZWFzZXMvY2VsbC1leHAvY2VsbHJhbmdlci03LjEuMC50YXIueHoiLCJDb25kaXRpb24iOnsiRGF0ZUxlc3NUaGFuIjp7IkFXUzpFcG9jaFRpbWUiOjE2NzQ2ODYwMjN9fX1dfQ__&Signature=kBEWDvHNogaTb-bmHzd7kHIBIfs8LQmePXusMXNKYpuqbgNHX5Ai0mhB-wv6ig1X5QFZytrl4gYXd8DyNXVo85hRp6Iw9k1UHtuuictpnyNe-5JNocePNKou89k9AOEGoatr6xa9z1VPkOj33FTpG25OpfQowpBrwvNhFF27qoSqw7EcjSWm53zB4QsYqMR~Bi-5MXTEplAxusXnE5A1HtVOo31lsL7cavd88ez9yFcSDIf65~KJR6KqDzqYS3NAcm3MKBWWSeIVAwOWAuHaQONeAew8X4fMb3ql85CpeaCWrQdB-vlUVkQbM0gJY2S7MQ9SJ0B5qUc7qo9UWLXATw__&Key-Pair-Id=APKAI7S6A5RYOXBWRPDA"

tar

-xf

cellranger-7.1.0.tar.xz

ln

-s

~/APP/cellranger-7.1.0/bin/cellranger

~/APP/bin/cellranger

export

PATH=$HOME/APP/bin:$PATHindex10X提供人和鼠的基因組參考index，其他物種可以是用cellranger自行構(gòu)建#

Human

reference

(GRCh38)

md5sum:

dfd654de39bff23917471e7fcc7a00cd

wget

https://cf./supp/cell-exp/refdata-gex-GRCh38-2020-A.tar.gz

md5sum

refdata-gex-GRCh38-2020-A.tar.gz下載原始數(shù)據(jù)CNCB的原始數(shù)據(jù)需要申請，申請通過后可以下載metadatahttps://ngdc./gsa-human/browse/HRA000728run信息run.tab文件AccessionRuntitleExperimentaccessionRundatafiletypeFilename1MD5checksum1DownLoad1Filename2MD5checksum2

HRR208788Y35_L.R1HRX170150fastqHRR208788_f1.fastq.gzcadeb85c01d10660830651c58f320dc3HRR208788_r2.fastq.gze1bc997bf7ae94806636415c7a552fff

HRR208789Y35_L.R2HRX170150fastqHRR208789_f1.fastq.gz07ae41fb8e962dacb112d8d6d6bdb940HRR208789_r2.fastq.gz63ed718855993f248dfe37557e17586e

HRR208790Y35_L.R3HRX170150fastqHRR208790_f1.fastq.gzf787b6ee5a94fd054cf0352f00b5b7bdHRR208790_r2.fastq.gzed156f85be6247f27185db9c38170f0c

HRR208791Y35_L.R4HRX170150fastqHRR208791_f1.fastq.gz5db5a61729a1c7fb332cd002540c1569HRR208791_r2.fastq.gze21cfcfca9e1722b148f2a26c586f624

HRR208792Y35_R.R1HRX170151fastqHRR208792_f1.fastq.gz3886b0cf3a68eeff8224bee725b9b761HRR208792_r2.fastq.gz2d3047a809a2888bc5df47b2a1e57055

HRR208793Y35_R.R2HRX170151fastqHRR208793_f1.fastq.gz37c08ac3019aabc6158dbf5a39bc3fc4HRR208793_r2.fastq.gz8714f1b0a5f81f6126555e5e012273a0

HRR208794Y35_R.R3HRX170151fastqHRR208794_f1.fastq.gz4f186e72b0c95ae477f3ab17c377acacHRR208794_r2.fastq.gzefa15e66fec4b1246774823b942b2f6e

HRR208795Y35_R.R4HRX170151fastqHRR208795_f1.fastq.gz47db73fb1507e8ad034f9b5fd96b1112HRR208795_r2.fastq.gzc33d9a77063aeefa227b2f9f89b3ad05

HRR208796Y44_L.R1HRX170152fastqHRR208796_f1.fastq.gz1ea8e5f0d87c24d69045b7f61d0a36d7HRR208796_r2.fastq.gz8dbdddfd49bc99b4cf72c2ac5da1c455制作下載文件sed

'1d'

run.tab

|

cut

-f

1

>>

hrr

sed

'1d'

run.tab

|

cut

-f

1

>>

hrr

sed

'1d'

run.tab

|

cut

-f

5

>>

fq

sed

'1d'

run.tab

|

cut

-f

8

>>

fq

paste

-d

'/'

hrr

fq

>

files

rm

-f

hrr

fq下載#>>>down.sh>>>

HRA=HRA000728

cat

files

|

while

read

i

do

echo

${i};

wget

-c

--user

注冊賬戶的郵箱

--password

注冊賬戶的密碼

--mirror

/${HRA}/${i}

done

#<<<down.sh<<<nohup

bash

down.sh

&>

down.sh.log

&把fastq文件移動到同一個目錄下find

.

-type

f

-name

"*.fastq.gz

>

fqs

cat

fqs

|

while

read

i

do

mv

${i}

fastqs

done

rm

-rf

human*

rm

fqs重命名腳本10X官網(wǎng)給指出來了文件名規(guī)則：[https://support./single-cell-gene-expression/software/pipelines/latest/using/fastq-input][https://support./help/BaseSpace_OLH_009008/Content/Source/Informatics/BS/NamingConvention_FASTQ-files-swBS.htm][SampleName]_S1_L00[LaneNumber][ReadType]_001.fastq.gzWhereReadTypeisoneof:I1:Sampleindexread(optional)I2:Sampleindexread(optional)R1:Read1R2:Read2用python制作重命名腳本from

collections

import

Counter

import

numpy

as

np

import

pandas

as

pd

df=pd.read_csv('run.tab',sep='\t')

#

Sample

Name

sn=np.repeat(df.loc[:,"Run

title"],2)

#

Read

Type

r=

np.repeat([1,2],df.shape[0])

sn=[x.split('.')[0]

for

x

in

sn]

sn

=

bq.st.removes(string=sn,pattern=r"_")

#

Sample

Number;

Lane

Number

s,l

=

[],[]

for

x,y

in

enumerate(Counter(sn).values()):

l

+=

list(range(1,int(y/2)+1))

s

+=

[x+1]

*

y

l=np.repeat(l,2)

#

new

file

name

new=[f'{a}_S_L00{c}_Rfrdzdhz_001.fastq.gz'

for

a,b,c,d

in

zip(sn,s,l,r)]

#

old

file

name

old=df.loc[:,"File

name

1"].tolist()+df.loc[:,"File

name

2"].tolist()

#shell

script

df2=pd.DataFrame({1:old,2:new})

df2.insert(0,column=0,value='mv')

df2.to_csv('rename.sh',index=False,header=False,sep='

')rename.sh前幾行cd

fastqs

bash

rename.shmv

HRR208788_f1.fastq.gz

Y35L_S1_L001_R1_001.fastq.gz

mv

HRR208789_f1.fastq.gz

Y35L_S1_L001_R1_001.fastq.gz

mv

HRR208790_f1.fastq.gz

Y35L_S1_L002_R1_001.fastq.gzcellrangercount#>>>quantify.sh

fastqs_dir=~/Project/HM/fastqs

index_dir=~/DataHub/Genomics/10X/refdata-gex-GRCh38-2020-A

output_dir=~/Project/HM/quantify

cd

${output_dir}

ls

${fastqs_dir}

|

cut

-d

'_'

-f

1

|

uniq

|

while

read

i

do

cellranger

count

\

--id

$i

\

--transcriptome

${index_dir}

\

--fastqs

${fastqs_dir}

\

--sample

$i

\

--localcores

12

\

--localmem

128

done

#<<<quantify.sh

nohup

bash

quantif