Extraction of Differentially Expressed Genes Under Starvation
Download Data¶
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import requests
from tqdm import tnrange, tqdm_notebook
def download_file(doi,ext):
url = 'https://api.datacite.org/dois/'+doi+'/media'
r = requests.get(url).json()
netcdf_url = r['data'][0]['attributes']['url']
r = requests.get(netcdf_url,stream=True)
#Set file name
fname = doi.split('/')[-1]+ext
#Download file with progress bar
if r.status_code == 403:
print("File Unavailable")
if 'content-length' not in r.headers:
print("Did not get file")
else:
with open(fname, 'wb') as f:
total_length = int(r.headers.get('content-length'))
pbar = tnrange(int(total_length/1024), unit="B")
for chunk in r.iter_content(chunk_size=1024):
if chunk:
pbar.update()
f.write(chunk)
return fname
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#Starvation h5ad data, all nonzero genes included, filtered for 'real cells' from de-multiplexing
download_file('10.22002/D1.1797','.gz')
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:18: TqdmDeprecationWarning: Please use `tqdm.notebook.trange` instead of `tqdm.tnrange`
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'D1.1797.gz'
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#CellRanger Starvation h5ad data
download_file('10.22002/D1.1798','.gz')
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:18: TqdmDeprecationWarning: Please use `tqdm.notebook.trange` instead of `tqdm.tnrange`
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'D1.1798.gz'
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#Kallisto bus clustered starvation data, h5ad
download_file('10.22002/D1.1796','.gz')
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:18: TqdmDeprecationWarning: Please use `tqdm.notebook.trange` instead of `tqdm.tnrange`
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'D1.1796.gz'
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#Saved DeSeq2 Results for Fed/Starved (Differentially expressed under starvation --> perturbed genes)
download_file('10.22002/D1.1810','.gz')
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:18: TqdmDeprecationWarning: Please use `tqdm.notebook.trange` instead of `tqdm.tnrange`
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'D1.1810.gz'
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#Human ortholog annotations
download_file('10.22002/D1.1819','.gz')
#Panther annotations
download_file('10.22002/D1.1820','.gz')
#GO Terms
download_file('10.22002/D1.1822','.gz')
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:18: TqdmDeprecationWarning: Please use `tqdm.notebook.trange` instead of `tqdm.tnrange`
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'D1.1822.gz'
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!gunzip *.gz
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!pip install --quiet anndata
!pip install --quiet scanpy
!pip3 install --quiet leidenalg
!pip install --quiet louvain
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|████████████████████████████████| 51kB 7.2MB/s
Building wheel for sinfo (setup.py) ... done
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|████████████████████████████████| 2.2MB 5.4MB/s
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!pip3 install --quiet rpy2
Import Packages¶
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import pandas as pd
import anndata
import scanpy as sc
import numpy as np
import scipy.sparse
import warnings
warnings.filterwarnings('ignore')
from sklearn.neighbors import (KNeighborsClassifier,NeighborhoodComponentsAnalysis)
from sklearn.pipeline import Pipeline
from sklearn.manifold import TSNE
from sklearn.decomposition import PCA
from sklearn.preprocessing import scale
import random
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
%matplotlib inline
sc.set_figure_params(dpi=125)
import seaborn as sns
sns.set(style="whitegrid")
%load_ext rpy2.ipython
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# See version of all installed packages, last done 11/27/2020
# !pip list -v > pkg_vers_20201127.txt
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#Read in annotations
from io import StringIO
hg_ortho_df = pd.read_csv(StringIO(''.join(l.replace('|', '\t') for l in open('D1.1819'))),
sep="\t",header=None,skiprows=[0,1,2,3])
hg_ortho_df[['XLOC','TCONS']] = hg_ortho_df[13].str.split(expand=True)
hg_ortho_df[['Gene','gi']] = hg_ortho_df[3].str.split(expand=True)
hg_ortho_df['Description']= hg_ortho_df[11]
panther_df = pd.read_csv('D1.1820',
sep="\t",header=None) #skiprows=[0,1,2,3]
goTerm_df = pd.read_csv('D1.1822',
sep=" ",header=None) #skiprows=[0,1,2,3]
Run DeSeq2 Analysis for Starvation Data¶
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#Remove clusters with < 10 cells per condition
#Read in previously saved data
bus_fs_clus = anndata.read("D1.1796")
print(bus_fs_clus )
bus_fs_raw = anndata.read("D1.1797")
bus_fs_raw = bus_fs_raw[bus_fs_clus.obs_names,]
#bus_fs_raw.obs['orgID'] = bus_fs_clus.obs['orgID']
bus_fs_raw.obs['fed'] = bus_fs_clus.obs['fed']
bus_fs_raw.obs['cellRanger_louvain'] = bus_fs_clus.obs['cellRanger_louvain']
bus_fs_raw
#clusSize
Trying to set attribute `.obs` of view, copying.
AnnData object with n_obs × n_vars = 13673 × 8696
obs: 'batch', 'n_counts', 'n_countslog', 'louvain', 'leiden', 'orgID', 'fed', 'starved', 'fed_neighbor_score', 'cellRanger_louvain', 'annos', 'new_cellRanger_louvain', 'annosSub'
var: 'n_counts', 'mean', 'std'
uns: 'annosSub_colors', 'annos_colors', 'cellRanger_louvain_colors', 'cellRanger_louvain_sizes', "dendrogram_['new_cellRanger_louvain']", 'dendrogram_new_cellRanger_louvain', 'fed_colors', 'fed_neighbor_score_colors', 'leiden', 'leiden_colors', 'louvain', 'louvain_colors', 'neighbors', 'new_cellRanger_louvain_colors', 'orgID_colors', 'paga', 'pca', 'rank_genes_groups', 'umap'
obsm: 'X_nca', 'X_pca', 'X_tsne', 'X_umap'
varm: 'PCs'
obsp: 'connectivities', 'distances'
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AnnData object with n_obs × n_vars = 13673 × 46716
obs: 'batch', 'fed', 'cellRanger_louvain'
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#Determine which clusters large enough to do DE with
def clusToKeep(bus_fs_clus):
keep = []
clusSize = {}
for i in np.unique(bus_fs_clus.obs['cellRanger_louvain']):
cells = bus_fs_clus[bus_fs_clus.obs['cellRanger_louvain'].isin([i])]
fed_cells = len(cells[cells.obs['fed']=='True'].obs_names)
starv_cells = len(cells[cells.obs['fed']=='False'].obs_names)
min_cells = np.min([fed_cells,starv_cells])
if min_cells > 10:
keep += [i]
#clusSize[i] = min_cells
return keep
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#Subsample from full dataset, across each cluster
def getSampled_Cluster(bus_fs_clus,bus_fs_raw,keep):
subSample = 100
cellNames = np.array(bus_fs_clus.obs_names)
fed = np.array(list(bus_fs_clus.obs['fed'] == 'True'))
starv = np.array(list(bus_fs_clus.obs['fed'] == 'False'))
#di = np.array(list(jelly4Raw.obs['condition'] == 'DI'))
allCells = []
for i in keep:
#subSample = clusSize[i] # REMOVE IF NOT LOOKING AT SIMILARLY SIZED CLUSTERS
cells = np.array(list(bus_fs_clus.obs['cellRanger_louvain'].isin([i])))
fed_cells = list(np.where(fed & cells)[0])
starv_cells = list(np.where(starv & cells)[0])
#Take all cells if < subSample
if len(fed_cells) >= subSample:
fed_choice = random.sample(fed_cells,subSample)
else:
fed_choice = fed_cells
if len(starv_cells) >= subSample:
starv_choice = random.sample(starv_cells,subSample)
else:
starv_choice = starv_cells
pos = list(fed_choice)+list(starv_choice)
#print(len(pos))
allCells += list(cellNames[pos])
sub_raw = bus_fs_raw[allCells,:]
return sub_raw
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#For full dataset don't filter by highly variable
keep = clusToKeep(bus_fs_clus)
sub_raw = getSampled_Cluster(bus_fs_clus,bus_fs_raw,keep)
print(sub_raw)
sub_raw_copy = sub_raw.copy()
sc.pp.filter_cells(sub_raw, min_counts=0)
sc.pp.filter_genes(sub_raw, min_counts=0)
sc.pp.normalize_per_cell(sub_raw_copy, counts_per_cell_after=1e4)
sub_raw_copy.raw = sc.pp.log1p(sub_raw_copy, copy=True)
#sc.pp.highly_variable_genes(sub_raw_copy,n_top_genes=5000) #This is just a small example, for full data used all nonzero genes
#sub_raw = sub_raw[:,sub_raw_copy.var['highly_variable']]
Trying to set attribute `.obs` of view, copying.
View of AnnData object with n_obs × n_vars = 6026 × 46716
obs: 'batch', 'fed', 'cellRanger_louvain'
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#Instantiate dataframe with gene names
def makeDF_forR(sub_raw):
fullDF = pd.DataFrame(scipy.sparse.csr_matrix.toarray(sub_raw.X).T, index = sub_raw.var_names.tolist(), columns= sub_raw.obs_names.tolist())
conds = sub_raw.obs['fed'].tolist()
#ids = sub_jelly4Raw.obs['orgID'].tolist()
clus = sub_raw.obs['cellRanger_louvain'].tolist()
reps = np.repeat(0,len(sub_raw.obs_names))
length = len(sub_raw[sub_raw.obs['fed'] == 'True'].obs_names)
reps[sub_raw.obs['fed'] == 'True'] = range(1,length+1)
length = len(sub_raw[sub_raw.obs['fed'] == 'False'].obs_names)
reps[sub_raw.obs['fed'] == 'False'] = range(1,length+1)
sampleDF = pd.DataFrame({'cell_ID': fullDF.columns}) \
.assign(condition = conds) \
.assign(replicate = reps) \
.assign(cluster = clus)
sampleDF.index = sampleDF.cell_ID
sampleDF.head()
fullDF.to_csv('fullDF.csv')
sampleDF.to_csv('sampleDF.csv')
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makeDF_forR(sub_raw)
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%%R
fullDF <- read.csv(file = 'fullDF.csv')
sampleDF <- read.csv(file = 'sampleDF.csv')
head(sampleDF)
cell_ID cell_ID.1 condition replicate cluster 1 AGCAGCCTCTGGTATG-1 AGCAGCCTCTGGTATG-1 True 1 0 2 CGTGAGCGTATATCCG-2 CGTGAGCGTATATCCG-2 True 2 0 3 CATGGCGTCAGTTGAC-1 CATGGCGTCAGTTGAC-1 True 3 0 4 CCCAATCGTTGTTTGG-1 CCCAATCGTTGTTTGG-1 True 4 0 5 ACGAGCCCACATCTTT-2 ACGAGCCCACATCTTT-2 True 5 0 6 GTCACAAGTCTAGGTT-2 GTCACAAGTCTAGGTT-2 True 6 0
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%%R
rownames(sampleDF) <- sampleDF$cell_ID
#Replace '.' in cell barcodes with '-'
rownames(fullDF) <- fullDF$X
colnames(fullDF) <- gsub("\\.", "-", colnames(fullDF))
fullDF <- subset(fullDF, select = -c(X) )
head(fullDF)
sampleDF <- subset(sampleDF, select = -c(cell_ID.1) )
# head(sampleDF)
sampleDF$condition <- factor(sampleDF$condition, labels = c("starved", "fed"))
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%%R
#Set up R environment
install.packages("BiocManager")
BiocManager::install(version = "3.10")
R[write to console]: Installing package into ‘/usr/local/lib/R/site-library’ (as ‘lib’ is unspecified) R[write to console]: trying URL 'https://cran.rstudio.com/src/contrib/BiocManager_1.30.10.tar.gz' R[write to console]: Content type 'application/x-gzip' R[write to console]: length 40205 bytes (39 KB) R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: R[write to console]: downloaded 39 KB R[write to console]: R[write to console]: R[write to console]: The downloaded source packages are in ‘/tmp/RtmpBlm3aK/downloaded_packages’ R[write to console]: R[write to console]: R[write to console]: Error: Bioconductor version '3.10' requires R version '3.6'; see https://bioconductor.org/install
Error: Bioconductor version '3.10' requires R version '3.6'; see https://bioconductor.org/install
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!sudo apt-get update
!sudo apt-get install libxml2-dev
!sudo apt-get install r-cran-xml
!sudo apt-get install libcurl4-openssl-dev
Hit:1 http://archive.ubuntu.com/ubuntu bionic InRelease Get:2 http://archive.ubuntu.com/ubuntu bionic-updates InRelease [88.7 kB] Get:3 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/ InRelease [3,626 B] Get:4 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic InRelease [15.9 kB] Hit:5 http://ppa.launchpad.net/cran/libgit2/ubuntu bionic InRelease Get:6 http://archive.ubuntu.com/ubuntu bionic-backports InRelease [74.6 kB] Hit:7 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic InRelease Get:8 http://security.ubuntu.com/ubuntu bionic-security InRelease [88.7 kB] Ign:9 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 InRelease Ign:10 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64 InRelease Hit:11 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 Release Hit:12 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64 Release Get:13 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/ Packages [41.5 kB] Get:14 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main Sources [1,704 kB] Get:15 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 Packages [2,140 kB] Get:16 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main amd64 Packages [873 kB] Get:17 http://archive.ubuntu.com/ubuntu bionic-updates/restricted amd64 Packages [277 kB] Get:18 http://archive.ubuntu.com/ubuntu bionic-updates/multiverse amd64 Packages [53.4 kB] Get:19 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 Packages [2,273 kB] Get:21 http://security.ubuntu.com/ubuntu bionic-security/multiverse amd64 Packages [14.9 kB] Get:23 http://security.ubuntu.com/ubuntu bionic-security/restricted amd64 Packages [247 kB] Get:24 http://security.ubuntu.com/ubuntu bionic-security/main amd64 Packages [1,846 kB] Get:25 http://security.ubuntu.com/ubuntu bionic-security/universe amd64 Packages [1,376 kB] Fetched 11.1 MB in 2s (5,148 kB/s) Reading package lists... Done Reading package lists... Done Building dependency tree Reading state information... Done libxml2-dev is already the newest version (2.9.4+dfsg1-6.1ubuntu1.3). 0 upgraded, 0 newly installed, 0 to remove and 26 not upgraded. Reading package lists... Done Building dependency tree Reading state information... Done The following NEW packages will be installed: r-cran-xml 0 upgraded, 1 newly installed, 0 to remove and 26 not upgraded. Need to get 1,711 kB of archives. After this operation, 3,072 kB of additional disk space will be used. Get:1 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main amd64 r-cran-xml amd64 3.99-0.5-1cran1.1804.0 [1,711 kB] Fetched 1,711 kB in 0s (15.8 MB/s) debconf: unable to initialize frontend: Dialog debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 1.) debconf: falling back to frontend: Readline debconf: unable to initialize frontend: Readline debconf: (This frontend requires a controlling tty.) debconf: falling back to frontend: Teletype dpkg-preconfigure: unable to re-open stdin: Selecting previously unselected package r-cran-xml. (Reading database ... 145480 files and directories currently installed.) Preparing to unpack .../r-cran-xml_3.99-0.5-1cran1.1804.0_amd64.deb ... Unpacking r-cran-xml (3.99-0.5-1cran1.1804.0) ... Setting up r-cran-xml (3.99-0.5-1cran1.1804.0) ... Reading package lists... Done Building dependency tree Reading state information... Done libcurl4-openssl-dev is already the newest version (7.58.0-2ubuntu3.12). 0 upgraded, 0 newly installed, 0 to remove and 26 not upgraded.
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%%R
#install.packages("DESeq2",repos = "http://cran.us.r-project.org")
BiocManager::install("DESeq2")
R[write to console]: Bioconductor version 3.12 (BiocManager 1.30.10), R 4.0.3 (2020-10-10) R[write to console]: Installing package(s) 'BiocVersion', 'DESeq2' R[write to console]: also installing the dependencies ‘bit’, ‘bitops’, ‘formatR’, ‘bit64’, ‘plogr’, ‘RCurl’, ‘GenomeInfoDbData’, ‘zlibbioc’, ‘matrixStats’, ‘lambda.r’, ‘futile.options’, ‘RSQLite’, ‘xtable’, ‘GenomeInfoDb’, ‘XVector’, ‘MatrixGenerics’, ‘DelayedArray’, ‘futile.logger’, ‘snow’, ‘AnnotationDbi’, ‘annotate’, ‘S4Vectors’, ‘IRanges’, ‘GenomicRanges’, ‘SummarizedExperiment’, ‘BiocGenerics’, ‘Biobase’, ‘BiocParallel’, ‘genefilter’, ‘locfit’, ‘geneplotter’, ‘RcppArmadillo’ R[write to console]: trying URL 'https://cran.rstudio.com/src/contrib/bit_4.0.4.tar.gz' R[write to console]: Content type 'application/x-gzip' R[write to console]: length 279723 bytes (273 KB) R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = 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#Make output directory
!mkdir kallistoDEAnalysis_Starv
In [ ]:
%%R
clusters <- unique(sampleDF$cluster)
clusters
[1] 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 [26] 25 26 27 28 29 30 31 32 33 34 35
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%%R
#Run DeSeq2 for each of the cell types (between control and starved cells)
install.packages("DESeq2",repos = "http://cran.us.r-project.org")
library("DESeq2")
clusters <- unique(sampleDF$cluster)
Genes <- c()
Cluster <- c()
Condition <- c()
padj <- c()
log2FC <- c()
for (i in clusters){
indices = which(sampleDF$cluster == i)
subset = fullDF[,indices]
subset_meta = subset(sampleDF,cluster == i)
dds <- DESeqDataSetFromMatrix(countData = subset, colData = subset_meta, design= ~replicate + condition)
#Set control condition
dds$condition <- relevel(dds$condition, ref = 'fed')
dds <- DESeq(dds,test="LRT", reduced=~replicate, sfType="poscounts", useT=TRUE, minmu=1e-6,
minReplicatesForReplace=Inf,betaPrior = FALSE)#parallel = TRUE
#Starv v Fed results
res <- results(dds,alpha=0.05,name="condition_starved_vs_fed")
resLFC <- res
resLFC <- na.omit(resLFC)
resOrdered <- resLFC[resLFC$padj < .05,]
#Keep log2 fold changes < -1 or > 1
resOrdered <- resOrdered[abs(resOrdered$log2FoldChange) > 1,]
outcomes <- resOrdered[order(resOrdered$padj),]
Genes <- c(Genes,row.names(outcomes))
Cluster <- c(Cluster,rep(i,length(row.names(outcomes))))
Condition <- c(Condition,rep('Starved',length(row.names(outcomes))))
padj <- c(padj,outcomes$padj)
log2FC <- c(log2FC,outcomes$log2FoldChange)
}
deGenesDF <- data.frame(matrix(ncol = 6, nrow = length(Genes)))
names(deGenesDF) <- c("Genes", "Cluster", "Condition","padj","padjClus","log2FC")
deGenesDF$Genes <- Genes
deGenesDF$Cluster <- Cluster
deGenesDF$Condition <- Condition
deGenesDF$padj <- padj
deGenesDF$padjClus <- padj*length(unique(Cluster))
deGenesDF$log2FC <- log2FC
write.csv(deGenesDF,'./kallistoDEAnalysis_Starv/deSeq2_deGenesDF_log2FCof1_singleCellReplicates_noShrinkage_subSample.csv')
head(deGenesDF)
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R[write to console]: The following object is masked from ‘package:MatrixGenerics’:
rowMedians
R[write to console]: The following objects are masked from ‘package:matrixStats’:
anyMissing, rowMedians
R[write to console]: converting counts to integer mode
R[write to console]: the design formula contains one or more numeric variables with integer values,
specifying a model with increasing fold change for higher values.
did you mean for this to be a factor? if so, first convert
this variable to a factor using the factor() function
R[write to console]: the design formula contains one or more numeric variables that have mean or
standard deviation larger than 5 (an arbitrary threshold to trigger this message).
it is generally a good idea to center and scale numeric variables in the design
to improve GLM convergence.
R[write to console]: estimating size factors
R[write to console]: estimating dispersions
R[write to console]: gene-wise dispersion estimates
R[write to console]: mean-dispersion relationship
R[write to console]: -- note: fitType='parametric', but the dispersion trend was not well captured by the
function: y = a/x + b, and a local regression fit was automatically substituted.
specify fitType='local' or 'mean' to avoid this message next time.
R[write to console]: final dispersion estimates
R[write to console]: fitting model and testing
R[write to console]: converting counts to integer mode
R[write to console]: the design formula contains one or more numeric variables with integer values,
specifying a model with increasing fold change for higher values.
did you mean for this to be a factor? if so, first convert
this variable to a factor using the factor() function
R[write to console]: the design formula contains one or more numeric variables that have mean or
standard deviation larger than 5 (an arbitrary threshold to trigger this message).
it is generally a good idea to center and scale numeric variables in the design
to improve GLM convergence.
R[write to console]: estimating size factors
R[write to console]: estimating dispersions
R[write to console]: gene-wise dispersion estimates
R[write to console]: mean-dispersion relationship
R[write to console]: -- note: fitType='parametric', but the dispersion trend was not well captured by the
function: y = a/x + b, and a local regression fit was automatically substituted.
specify fitType='local' or 'mean' to avoid this message next time.
R[write to console]: final dispersion estimates
R[write to console]: fitting model and testing
R[write to console]: 2 rows did not converge in beta, labelled in mcols(object)$fullBetaConv. Use larger maxit argument with nbinomLRT
R[write to console]: converting counts to integer mode
R[write to console]: the design formula contains one or more numeric variables with integer values,
specifying a model with increasing fold change for higher values.
did you mean for this to be a factor? if so, first convert
this variable to a factor using the factor() function
R[write to console]: the design formula contains one or more numeric variables that have mean or
standard deviation larger than 5 (an arbitrary threshold to trigger this message).
it is generally a good idea to center and scale numeric variables in the design
to improve GLM convergence.
R[write to console]: estimating size factors
R[write to console]: estimating dispersions
R[write to console]: gene-wise dispersion estimates
R[write to console]: mean-dispersion relationship
R[write to console]: final dispersion estimates
R[write to console]: fitting model and testing
R[write to console]: 1 rows did not converge in beta, labelled in mcols(object)$fullBetaConv. Use larger maxit argument with nbinomLRT
R[write to console]: converting counts to integer mode
R[write to console]: the design formula contains one or more numeric variables with integer values,
specifying a model with increasing fold change for higher values.
did you mean for this to be a factor? if so, first convert
this variable to a factor using the factor() function
R[write to console]: the design formula contains one or more numeric variables that have mean or
standard deviation larger than 5 (an arbitrary threshold to trigger this message).
it is generally a good idea to center and scale numeric variables in the design
to improve GLM convergence.
R[write to console]: estimating size factors
R[write to console]: estimating dispersions
R[write to console]: gene-wise dispersion estimates
R[write to console]: mean-dispersion relationship
R[write to console]: final dispersion estimates
R[write to console]: fitting model and testing
R[write to console]: 12 rows did not converge in beta, labelled in mcols(object)$fullBetaConv. Use larger maxit argument with nbinomLRT
R[write to console]: converting counts to integer mode
R[write to console]: the design formula contains one or more numeric variables with integer values,
specifying a model with increasing fold change for higher values.
did you mean for this to be a factor? if so, first convert
this variable to a factor using the factor() function
R[write to console]: the design formula contains one or more numeric variables that have mean or
standard deviation larger than 5 (an arbitrary threshold to trigger this message).
it is generally a good idea to center and scale numeric variables in the design
to improve GLM convergence.
R[write to console]: estimating size factors
R[write to console]: estimating dispersions
R[write to console]: gene-wise dispersion estimates
R[write to console]: mean-dispersion relationship
R[write to console]: final dispersion estimates
R[write to console]: fitting model and testing
R[write to console]: 10 rows did not converge in beta, labelled in mcols(object)$fullBetaConv. Use larger maxit argument with nbinomLRT
R[write to console]: converting counts to integer mode
R[write to console]: the design formula contains one or more numeric variables with integer values,
specifying a model with increasing fold change for higher values.
did you mean for this to be a factor? if so, first convert
this variable to a factor using the factor() function
R[write to console]: the design formula contains one or more numeric variables that have mean or
standard deviation larger than 5 (an arbitrary threshold to trigger this message).
it is generally a good idea to center and scale numeric variables in the design
to improve GLM convergence.
R[write to console]: estimating size factors
R[write to console]: estimating dispersions
R[write to console]: gene-wise dispersion estimates
R[write to console]: mean-dispersion relationship
R[write to console]: final dispersion estimates
R[write to console]: fitting model and testing
R[write to console]: 8 rows did not converge in beta, labelled in mcols(object)$fullBetaConv. Use larger maxit argument with nbinomLRT
R[write to console]: converting counts to integer mode
R[write to console]: the design formula contains one or more numeric variables with integer values,
specifying a model with increasing fold change for higher values.
did you mean for this to be a factor? if so, first convert
this variable to a factor using the factor() function
R[write to console]: the design formula contains one or more numeric variables that have mean or
standard deviation larger than 5 (an arbitrary threshold to trigger this message).
it is generally a good idea to center and scale numeric variables in the design
to improve GLM convergence.
R[write to console]: estimating size factors
R[write to console]: estimating dispersions
R[write to console]: gene-wise dispersion estimates
R[write to console]: mean-dispersion relationship
R[write to console]: final dispersion estimates
R[write to console]: fitting model and testing
R[write to console]: 8 rows did not converge in beta, labelled in mcols(object)$fullBetaConv. Use larger maxit argument with nbinomLRT
R[write to console]: converting counts to integer mode
R[write to console]: the design formula contains one or more numeric variables with integer values,
specifying a model with increasing fold change for higher values.
did you mean for this to be a factor? if so, first convert
this variable to a factor using the factor() function
R[write to console]: the design formula contains one or more numeric variables that have mean or
standard deviation larger than 5 (an arbitrary threshold to trigger this message).
it is generally a good idea to center and scale numeric variables in the design
to improve GLM convergence.
R[write to console]: estimating size factors
R[write to console]: estimating dispersions
R[write to console]: gene-wise dispersion estimates
R[write to console]: mean-dispersion relationship
R[write to console]: final dispersion estimates
R[write to console]: fitting model and testing
R[write to console]: 4 rows did not converge in beta, labelled in mcols(object)$fullBetaConv. Use larger maxit argument with nbinomLRT
R[write to console]: converting counts to integer mode
R[write to console]: the design formula contains one or more numeric variables with integer values,
specifying a model with increasing fold change for higher values.
did you mean for this to be a factor? if so, first convert
this variable to a factor using the factor() function
R[write to console]: the design formula contains one or more numeric variables that have mean or
standard deviation larger than 5 (an arbitrary threshold to trigger this message).
it is generally a good idea to center and scale numeric variables in the design
to improve GLM convergence.
R[write to console]: estimating size factors
R[write to console]: estimating dispersions
R[write to console]: gene-wise dispersion estimates
R[write to console]: mean-dispersion relationship
R[write to console]: final dispersion estimates
R[write to console]: fitting model and testing
R[write to console]: 1 rows did not converge in beta, labelled in mcols(object)$fullBetaConv. Use larger maxit argument with nbinomLRT
R[write to console]: converting counts to integer mode
R[write to console]: the design formula contains one or more numeric variables with integer values,
specifying a model with increasing fold change for higher values.
did you mean for this to be a factor? if so, first convert
this variable to a factor using the factor() function
R[write to console]: the design formula contains one or more numeric variables that have mean or
standard deviation larger than 5 (an arbitrary threshold to trigger this message).
it is generally a good idea to center and scale numeric variables in the design
to improve GLM convergence.
R[write to console]: estimating size factors
R[write to console]: estimating dispersions
R[write to console]: gene-wise dispersion estimates
R[write to console]: mean-dispersion relationship
R[write to console]: final dispersion estimates
R[write to console]: fitting model and testing
R[write to console]: 1 rows did not converge in beta, labelled in mcols(object)$fullBetaConv. Use larger maxit argument with nbinomLRT
R[write to console]: converting counts to integer mode
R[write to console]: the design formula contains one or more numeric variables with integer values,
specifying a model with increasing fold change for higher values.
did you mean for this to be a factor? if so, first convert
this variable to a factor using the factor() function
R[write to console]: the design formula contains one or more numeric variables that have mean or
standard deviation larger than 5 (an arbitrary threshold to trigger this message).
it is generally a good idea to center and scale numeric variables in the design
to improve GLM convergence.
R[write to console]: estimating size factors
R[write to console]: estimating dispersions
R[write to console]: gene-wise dispersion estimates
R[write to console]: mean-dispersion relationship
R[write to console]: -- note: fitType='parametric', but the dispersion trend was not well captured by the
function: y = a/x + b, and a local regression fit was automatically substituted.
specify fitType='local' or 'mean' to avoid this message next time.
R[write to console]: final dispersion estimates
R[write to console]: fitting model and testing
R[write to console]: converting counts to integer mode
R[write to console]: the design formula contains one or more numeric variables with integer values,
specifying a model with increasing fold change for higher values.
did you mean for this to be a factor? if so, first convert
this variable to a factor using the factor() function
R[write to console]: the design formula contains one or more numeric variables that have mean or
standard deviation larger than 5 (an arbitrary threshold to trigger this message).
it is generally a good idea to center and scale numeric variables in the design
to improve GLM convergence.
R[write to console]: estimating size factors
R[write to console]: estimating dispersions
R[write to console]: gene-wise dispersion estimates
R[write to console]: mean-dispersion relationship
R[write to console]: -- note: fitType='parametric', but the dispersion trend was not well captured by the
function: y = a/x + b, and a local regression fit was automatically substituted.
specify fitType='local' or 'mean' to avoid this message next time.
R[write to console]: final dispersion estimates
R[write to console]: fitting model and testing
R[write to console]: 1 rows did not converge in beta, labelled in mcols(object)$fullBetaConv. Use larger maxit argument with nbinomLRT
R[write to console]: converting counts to integer mode
R[write to console]: the design formula contains one or more numeric variables with integer values,
specifying a model with increasing fold change for higher values.
did you mean for this to be a factor? if so, first convert
this variable to a factor using the factor() function
R[write to console]: the design formula contains one or more numeric variables that have mean or
standard deviation larger than 5 (an arbitrary threshold to trigger this message).
it is generally a good idea to center and scale numeric variables in the design
to improve GLM convergence.
R[write to console]: estimating size factors
R[write to console]: estimating dispersions
R[write to console]: gene-wise dispersion estimates
R[write to console]: mean-dispersion relationship
R[write to console]: final dispersion estimates
R[write to console]: fitting model and testing
R[write to console]: 1 rows did not converge in beta, labelled in mcols(object)$fullBetaConv. Use larger maxit argument with nbinomLRT
R[write to console]: converting counts to integer mode
R[write to console]: the design formula contains one or more numeric variables with integer values,
specifying a model with increasing fold change for higher values.
did you mean for this to be a factor? if so, first convert
this variable to a factor using the factor() function
R[write to console]: the design formula contains one or more numeric variables that have mean or
standard deviation larger than 5 (an arbitrary threshold to trigger this message).
it is generally a good idea to center and scale numeric variables in the design
to improve GLM convergence.
R[write to console]: estimating size factors
R[write to console]: estimating dispersions
R[write to console]: gene-wise dispersion estimates
R[write to console]: mean-dispersion relationship
R[write to console]: -- note: fitType='parametric', but the dispersion trend was not well captured by the
function: y = a/x + b, and a local regression fit was automatically substituted.
specify fitType='local' or 'mean' to avoid this message next time.
R[write to console]: final dispersion estimates
R[write to console]: fitting model and testing
R[write to console]: converting counts to integer mode
R[write to console]: the design formula contains one or more numeric variables with integer values,
specifying a model with increasing fold change for higher values.
did you mean for this to be a factor? if so, first convert
this variable to a factor using the factor() function
R[write to console]: the design formula contains one or more numeric variables that have mean or
standard deviation larger than 5 (an arbitrary threshold to trigger this message).
it is generally a good idea to center and scale numeric variables in the design
to improve GLM convergence.
R[write to console]: estimating size factors
R[write to console]: estimating dispersions
R[write to console]: gene-wise dispersion estimates
R[write to console]: mean-dispersion relationship
R[write to console]: final dispersion estimates
R[write to console]: fitting model and testing
R[write to console]: converting counts to integer mode
R[write to console]: the design formula contains one or more numeric variables with integer values,
specifying a model with increasing fold change for higher values.
did you mean for this to be a factor? if so, first convert
this variable to a factor using the factor() function
R[write to console]: the design formula contains one or more numeric variables that have mean or
standard deviation larger than 5 (an arbitrary threshold to trigger this message).
it is generally a good idea to center and scale numeric variables in the design
to improve GLM convergence.
R[write to console]: estimating size factors
R[write to console]: estimating dispersions
R[write to console]: gene-wise dispersion estimates
R[write to console]: mean-dispersion relationship
R[write to console]: final dispersion estimates
R[write to console]: fitting model and testing
R[write to console]: converting counts to integer mode
R[write to console]: the design formula contains one or more numeric variables with integer values,
specifying a model with increasing fold change for higher values.
did you mean for this to be a factor? if so, first convert
this variable to a factor using the factor() function
R[write to console]: the design formula contains one or more numeric variables that have mean or
standard deviation larger than 5 (an arbitrary threshold to trigger this message).
it is generally a good idea to center and scale numeric variables in the design
to improve GLM convergence.
R[write to console]: estimating size factors
R[write to console]: estimating dispersions
R[write to console]: gene-wise dispersion estimates
R[write to console]: mean-dispersion relationship
R[write to console]: final dispersion estimates
R[write to console]: fitting model and testing
R[write to console]: converting counts to integer mode
R[write to console]: the design formula contains one or more numeric variables with integer values,
specifying a model with increasing fold change for higher values.
did you mean for this to be a factor? if so, first convert
this variable to a factor using the factor() function
R[write to console]: the design formula contains one or more numeric variables that have mean or
standard deviation larger than 5 (an arbitrary threshold to trigger this message).
it is generally a good idea to center and scale numeric variables in the design
to improve GLM convergence.
R[write to console]: estimating size factors
R[write to console]: estimating dispersions
R[write to console]: gene-wise dispersion estimates
R[write to console]: mean-dispersion relationship
R[write to console]: -- note: fitType='parametric', but the dispersion trend was not well captured by the
function: y = a/x + b, and a local regression fit was automatically substituted.
specify fitType='local' or 'mean' to avoid this message next time.
R[write to console]: final dispersion estimates
R[write to console]: fitting model and testing
R[write to console]: converting counts to integer mode
R[write to console]: the design formula contains one or more numeric variables with integer values,
specifying a model with increasing fold change for higher values.
did you mean for this to be a factor? if so, first convert
this variable to a factor using the factor() function
R[write to console]: the design formula contains one or more numeric variables that have mean or
standard deviation larger than 5 (an arbitrary threshold to trigger this message).
it is generally a good idea to center and scale numeric variables in the design
to improve GLM convergence.
R[write to console]: estimating size factors
R[write to console]: estimating dispersions
R[write to console]: gene-wise dispersion estimates
R[write to console]: mean-dispersion relationship
R[write to console]: final dispersion estimates
R[write to console]: fitting model and testing
R[write to console]: converting counts to integer mode
R[write to console]: the design formula contains one or more numeric variables with integer values,
specifying a model with increasing fold change for higher values.
did you mean for this to be a factor? if so, first convert
this variable to a factor using the factor() function
R[write to console]: the design formula contains one or more numeric variables that have mean or
standard deviation larger than 5 (an arbitrary threshold to trigger this message).
it is generally a good idea to center and scale numeric variables in the design
to improve GLM convergence.
R[write to console]: estimating size factors
R[write to console]: estimating dispersions
R[write to console]: gene-wise dispersion estimates
R[write to console]: mean-dispersion relationship
R[write to console]: final dispersion estimates
R[write to console]: fitting model and testing
R[write to console]: converting counts to integer mode
R[write to console]: the design formula contains one or more numeric variables with integer values,
specifying a model with increasing fold change for higher values.
did you mean for this to be a factor? if so, first convert
this variable to a factor using the factor() function
R[write to console]: the design formula contains one or more numeric variables that have mean or
standard deviation larger than 5 (an arbitrary threshold to trigger this message).
it is generally a good idea to center and scale numeric variables in the design
to improve GLM convergence.
R[write to console]: estimating size factors
R[write to console]: estimating dispersions
R[write to console]: gene-wise dispersion estimates
R[write to console]: mean-dispersion relationship
R[write to console]: -- note: fitType='parametric', but the dispersion trend was not well captured by the
function: y = a/x + b, and a local regression fit was automatically substituted.
specify fitType='local' or 'mean' to avoid this message next time.
R[write to console]: final dispersion estimates
R[write to console]: fitting model and testing
R[write to console]: converting counts to integer mode
R[write to console]: the design formula contains one or more numeric variables with integer values,
specifying a model with increasing fold change for higher values.
did you mean for this to be a factor? if so, first convert
this variable to a factor using the factor() function
R[write to console]: the design formula contains one or more numeric variables that have mean or
standard deviation larger than 5 (an arbitrary threshold to trigger this message).
it is generally a good idea to center and scale numeric variables in the design
to improve GLM convergence.
R[write to console]: estimating size factors
R[write to console]: estimating dispersions
R[write to console]: gene-wise dispersion estimates
R[write to console]: mean-dispersion relationship
R[write to console]: -- note: fitType='parametric', but the dispersion trend was not well captured by the
function: y = a/x + b, and a local regression fit was automatically substituted.
specify fitType='local' or 'mean' to avoid this message next time.
R[write to console]: final dispersion estimates
R[write to console]: fitting model and testing
R[write to console]: converting counts to integer mode
R[write to console]: the design formula contains one or more numeric variables with integer values,
specifying a model with increasing fold change for higher values.
did you mean for this to be a factor? if so, first convert
this variable to a factor using the factor() function
R[write to console]: the design formula contains one or more numeric variables that have mean or
standard deviation larger than 5 (an arbitrary threshold to trigger this message).
it is generally a good idea to center and scale numeric variables in the design
to improve GLM convergence.
R[write to console]: estimating size factors
R[write to console]: estimating dispersions
R[write to console]: gene-wise dispersion estimates
R[write to console]: mean-dispersion relationship
R[write to console]: final dispersion estimates
R[write to console]: fitting model and testing
R[write to console]: converting counts to integer mode
R[write to console]: the design formula contains one or more numeric variables with integer values,
specifying a model with increasing fold change for higher values.
did you mean for this to be a factor? if so, first convert
this variable to a factor using the factor() function
R[write to console]: the design formula contains one or more numeric variables that have mean or
standard deviation larger than 5 (an arbitrary threshold to trigger this message).
it is generally a good idea to center and scale numeric variables in the design
to improve GLM convergence.
R[write to console]: estimating size factors
R[write to console]: estimating dispersions
R[write to console]: gene-wise dispersion estimates
R[write to console]: mean-dispersion relationship
R[write to console]: -- note: fitType='parametric', but the dispersion trend was not well captured by the
function: y = a/x + b, and a local regression fit was automatically substituted.
specify fitType='local' or 'mean' to avoid this message next time.
R[write to console]: final dispersion estimates
R[write to console]: fitting model and testing
R[write to console]: converting counts to integer mode
R[write to console]: the design formula contains one or more numeric variables with integer values,
specifying a model with increasing fold change for higher values.
did you mean for this to be a factor? if so, first convert
this variable to a factor using the factor() function
R[write to console]: the design formula contains one or more numeric variables that have mean or
standard deviation larger than 5 (an arbitrary threshold to trigger this message).
it is generally a good idea to center and scale numeric variables in the design
to improve GLM convergence.
R[write to console]: estimating size factors
R[write to console]: estimating dispersions
R[write to console]: gene-wise dispersion estimates
R[write to console]: mean-dispersion relationship
R[write to console]: final dispersion estimates
R[write to console]: fitting model and testing
R[write to console]: converting counts to integer mode
R[write to console]: the design formula contains one or more numeric variables with integer values,
specifying a model with increasing fold change for higher values.
did you mean for this to be a factor? if so, first convert
this variable to a factor using the factor() function
R[write to console]: the design formula contains one or more numeric variables that have mean or
standard deviation larger than 5 (an arbitrary threshold to trigger this message).
it is generally a good idea to center and scale numeric variables in the design
to improve GLM convergence.
R[write to console]: estimating size factors
R[write to console]: estimating dispersions
R[write to console]: gene-wise dispersion estimates
R[write to console]: mean-dispersion relationship
R[write to console]: -- note: fitType='parametric', but the dispersion trend was not well captured by the
function: y = a/x + b, and a local regression fit was automatically substituted.
specify fitType='local' or 'mean' to avoid this message next time.
R[write to console]: final dispersion estimates
R[write to console]: fitting model and testing
R[write to console]: converting counts to integer mode
R[write to console]: the design formula contains one or more numeric variables with integer values,
specifying a model with increasing fold change for higher values.
did you mean for this to be a factor? if so, first convert
this variable to a factor using the factor() function
R[write to console]: the design formula contains one or more numeric variables that have mean or
standard deviation larger than 5 (an arbitrary threshold to trigger this message).
it is generally a good idea to center and scale numeric variables in the design
to improve GLM convergence.
R[write to console]: estimating size factors
R[write to console]: estimating dispersions
R[write to console]: gene-wise dispersion estimates
R[write to console]: mean-dispersion relationship
R[write to console]: -- note: fitType='parametric', but the dispersion trend was not well captured by the
function: y = a/x + b, and a local regression fit was automatically substituted.
specify fitType='local' or 'mean' to avoid this message next time.
R[write to console]: final dispersion estimates
R[write to console]: fitting model and testing
R[write to console]: converting counts to integer mode
R[write to console]: the design formula contains one or more numeric variables with integer values,
specifying a model with increasing fold change for higher values.
did you mean for this to be a factor? if so, first convert
this variable to a factor using the factor() function
R[write to console]: the design formula contains one or more numeric variables that have mean or
standard deviation larger than 5 (an arbitrary threshold to trigger this message).
it is generally a good idea to center and scale numeric variables in the design
to improve GLM convergence.
R[write to console]: estimating size factors
R[write to console]: estimating dispersions
R[write to console]: gene-wise dispersion estimates
R[write to console]: mean-dispersion relationship
R[write to console]: final dispersion estimates
R[write to console]: fitting model and testing
R[write to console]: converting counts to integer mode
R[write to console]: the design formula contains one or more numeric variables with integer values,
specifying a model with increasing fold change for higher values.
did you mean for this to be a factor? if so, first convert
this variable to a factor using the factor() function
R[write to console]: the design formula contains one or more numeric variables that have mean or
standard deviation larger than 5 (an arbitrary threshold to trigger this message).
it is generally a good idea to center and scale numeric variables in the design
to improve GLM convergence.
R[write to console]: estimating size factors
R[write to console]: estimating dispersions
R[write to console]: gene-wise dispersion estimates
R[write to console]: mean-dispersion relationship
R[write to console]: final dispersion estimates
R[write to console]: fitting model and testing
R[write to console]: converting counts to integer mode
R[write to console]: the design formula contains one or more numeric variables with integer values,
specifying a model with increasing fold change for higher values.
did you mean for this to be a factor? if so, first convert
this variable to a factor using the factor() function
R[write to console]: the design formula contains one or more numeric variables that have mean or
standard deviation larger than 5 (an arbitrary threshold to trigger this message).
it is generally a good idea to center and scale numeric variables in the design
to improve GLM convergence.
R[write to console]: estimating size factors
R[write to console]: estimating dispersions
R[write to console]: gene-wise dispersion estimates
R[write to console]: mean-dispersion relationship
R[write to console]: final dispersion estimates
R[write to console]: fitting model and testing
R[write to console]: converting counts to integer mode
R[write to console]: the design formula contains one or more numeric variables with integer values,
specifying a model with increasing fold change for higher values.
did you mean for this to be a factor? if so, first convert
this variable to a factor using the factor() function
R[write to console]: the design formula contains one or more numeric variables that have mean or
standard deviation larger than 5 (an arbitrary threshold to trigger this message).
it is generally a good idea to center and scale numeric variables in the design
to improve GLM convergence.
R[write to console]: estimating size factors
R[write to console]: estimating dispersions
R[write to console]: gene-wise dispersion estimates
R[write to console]: mean-dispersion relationship
R[write to console]: final dispersion estimates
R[write to console]: fitting model and testing
R[write to console]: converting counts to integer mode
R[write to console]: the design formula contains one or more numeric variables with integer values,
specifying a model with increasing fold change for higher values.
did you mean for this to be a factor? if so, first convert
this variable to a factor using the factor() function
R[write to console]: the design formula contains one or more numeric variables that have mean or
standard deviation larger than 5 (an arbitrary threshold to trigger this message).
it is generally a good idea to center and scale numeric variables in the design
to improve GLM convergence.
R[write to console]: estimating size factors
R[write to console]: estimating dispersions
R[write to console]: gene-wise dispersion estimates
R[write to console]: mean-dispersion relationship
R[write to console]: -- note: fitType='parametric', but the dispersion trend was not well captured by the
function: y = a/x + b, and a local regression fit was automatically substituted.
specify fitType='local' or 'mean' to avoid this message next time.
R[write to console]: final dispersion estimates
R[write to console]: fitting model and testing
R[write to console]: converting counts to integer mode
R[write to console]: the design formula contains one or more numeric variables with integer values,
specifying a model with increasing fold change for higher values.
did you mean for this to be a factor? if so, first convert
this variable to a factor using the factor() function
R[write to console]: the design formula contains one or more numeric variables that have mean or
standard deviation larger than 5 (an arbitrary threshold to trigger this message).
it is generally a good idea to center and scale numeric variables in the design
to improve GLM convergence.
R[write to console]: estimating size factors
R[write to console]: estimating dispersions
R[write to console]: gene-wise dispersion estimates
R[write to console]: mean-dispersion relationship
R[write to console]: final dispersion estimates
R[write to console]: fitting model and testing
R[write to console]: converting counts to integer mode
R[write to console]: the design formula contains one or more numeric variables with integer values,
specifying a model with increasing fold change for higher values.
did you mean for this to be a factor? if so, first convert
this variable to a factor using the factor() function
R[write to console]: the design formula contains one or more numeric variables that have mean or
standard deviation larger than 5 (an arbitrary threshold to trigger this message).
it is generally a good idea to center and scale numeric variables in the design
to improve GLM convergence.
R[write to console]: estimating size factors
R[write to console]: estimating dispersions
R[write to console]: gene-wise dispersion estimates
R[write to console]: mean-dispersion relationship
R[write to console]: final dispersion estimates
R[write to console]: fitting model and testing
R[write to console]: converting counts to integer mode
R[write to console]: the design formula contains one or more numeric variables with integer values,
specifying a model with increasing fold change for higher values.
did you mean for this to be a factor? if so, first convert
this variable to a factor using the factor() function
R[write to console]: the design formula contains one or more numeric variables that have mean or
standard deviation larger than 5 (an arbitrary threshold to trigger this message).
it is generally a good idea to center and scale numeric variables in the design
to improve GLM convergence.
R[write to console]: estimating size factors
R[write to console]: estimating dispersions
R[write to console]: gene-wise dispersion estimates
R[write to console]: mean-dispersion relationship
R[write to console]: final dispersion estimates
R[write to console]: fitting model and testing
R[write to console]: converting counts to integer mode
R[write to console]: the design formula contains one or more numeric variables with integer values,
specifying a model with increasing fold change for higher values.
did you mean for this to be a factor? if so, first convert
this variable to a factor using the factor() function
R[write to console]: the design formula contains one or more numeric variables that have mean or
standard deviation larger than 5 (an arbitrary threshold to trigger this message).
it is generally a good idea to center and scale numeric variables in the design
to improve GLM convergence.
R[write to console]: estimating size factors
R[write to console]: estimating dispersions
R[write to console]: gene-wise dispersion estimates
R[write to console]: mean-dispersion relationship
R[write to console]: final dispersion estimates
R[write to console]: fitting model and testing
Genes Cluster Condition padj padjClus log2FC 1 XLOC_030861 0 Starved 9.125564e-14 3.102692e-12 -1.641450 2 XLOC_010635 0 Starved 5.121409e-13 1.741279e-11 -1.382850 3 XLOC_040775 0 Starved 1.881249e-11 6.396248e-10 1.093848 4 XLOC_012879 0 Starved 9.571692e-11 3.254375e-09 -1.921527 5 XLOC_028699 0 Starved 1.657337e-10 5.634945e-09 -1.099936 6 XLOC_011294 0 Starved 1.278944e-09 4.348410e-08 -1.137523
In [ ]:
%%R
install.packages("UpSetR",repos = "http://cran.us.r-project.org")
R[write to console]: Installing package into ‘/usr/local/lib/R/site-library’ (as ‘lib’ is unspecified) R[write to console]: also installing the dependencies ‘gridExtra’, ‘plyr’ R[write to console]: trying URL 'http://cran.us.r-project.org/src/contrib/gridExtra_2.3.tar.gz' R[write to console]: Content type 'application/x-gzip' R[write to console]: length 1062844 bytes (1.0 MB) R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: R[write to console]: downloaded 1.0 MB R[write to console]: trying URL 'http://cran.us.r-project.org/src/contrib/plyr_1.8.6.tar.gz' R[write to console]: Content type 'application/x-gzip' R[write to console]: length 401191 bytes (391 KB) R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: R[write to console]: downloaded 391 KB R[write to console]: trying URL 'http://cran.us.r-project.org/src/contrib/UpSetR_1.4.0.tar.gz' R[write to console]: Content type 'application/x-gzip' R[write to console]: length 4194664 bytes (4.0 MB) R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: R[write to console]: downloaded 4.0 MB R[write to console]: R[write to console]: R[write to console]: The downloaded source packages are in ‘/tmp/Rtmpl94P6z/downloaded_packages’ R[write to console]: R[write to console]:
In [ ]:
%%R -w 20 -h 20 --units in -r 500
#Cite http://people.seas.harvard.edu/~alex/papers/2014_infovis_upset.pdf
library("UpSetR")
deGenesDF <- read.csv(file = './kallistoDEAnalysis_Starv/deSeq2_deGenesDF_log2FCof1_singleCellReplicates_noShrinkage_subSample.csv') #./kallistoDEAnalysis_Starv/deSeq2_deGenesDF_log2FCof1_singleCellReplicates_noShrinkage_subSample.csv
#Bonferronni correction across clusters
deGenesDF_toPlot = subset(deGenesDF,padjClus < .05)
# Create empty list to store vectors
vecsToPlot <- list()
clusters = unique(deGenesDF_toPlot$Cluster)
for (i in 1:length(clusters)){
subset = subset(deGenesDF_toPlot,Cluster == clusters[i])
vecsToPlot[[i]] <- unique(subset$Genes)
}
names(vecsToPlot) <- clusters
upset(fromList(vecsToPlot), sets = as.character(clusters),nintersects= NA,order.by = "freq",
mainbar.y.label='',
sets.x.label = '',
text.scale = c(1.5, 2, 1.5, 2, 1.3, 1.3),
show.numbers = "no",
point.size = 2.8,
mb.ratio= c(0.5, 0.5),
queries = list(list(query = intersects, params = list("9"), color = "firebrick",active = T),
list(query = intersects, params = list("28"), color = "firebrick",active = T),
list(query = intersects, params = list("21"), color = "firebrick",active = T),
list(query = intersects, params = list("17"), color = "firebrick",active = T),
list(query = intersects, params = list("6"), color = "firebrick",active = T),
list(query = intersects, params = list("31"), color = "firebrick",active = T),
list(query = intersects, params = list("24"), color = "firebrick",active = T),
list(query = intersects, params = list("8"), color = "firebrick",active = T),
list(query = intersects, params = list("15"), color = "firebrick",active = T),
list(query = intersects, params = list("10"), color = "firebrick",active = T),
list(query = intersects, params = list("23"), color = "firebrick",active = T),
list(query = intersects, params = list("26"), color = "firebrick",active = T),
list(query = intersects, params = list("11"), color = "firebrick",active = T),
list(query = intersects, params = list("33"), color = "firebrick",active = T),
list(query = intersects, params = list("12"), color = "firebrick",active = T),
list(query = intersects, params = list("19"), color = "firebrick",active = T),
list(query = intersects, params = list("29"), color = "firebrick",active = T),
list(query = intersects, params = list("14"), color = "firebrick",active = T),
list(query = intersects, params = list("30"), color = "firebrick",active = T),
list(query = intersects, params = list("18"), color = "firebrick",active = T),
list(query = intersects, params = list("3"), color = "firebrick",active = T),
list(query = intersects, params = list("22"), color = "firebrick",active = T),
list(query = intersects, params = list("27"), color = "firebrick",active = T),
list(query = intersects, params = list("0"), color = "firebrick",active = T),
list(query = intersects, params = list("34"), color = "firebrick",active = T),
list(query = intersects, params = list("32"), color = "firebrick",active = T),
list(query = intersects, params = list("16"), color = "firebrick",active = T),
list(query = intersects, params = list("35"), color = "firebrick",active = T)))
In [ ]:
deseq_df = pd.read_csv('./kallistoDEAnalysis_Starv/deSeq2_deGenesDF_log2FCof1_singleCellReplicates_noShrinkage_subSample.csv',
sep=",")
deseq_df.head()
In [ ]:
orthoGene = []
orthoDescr = []
pantherNum = []
pantherDescr = []
goTerms = []
for g in deseq_df.Genes:
sub_df = hg_ortho_df[hg_ortho_df.XLOC.isin([g])]
panth_df = panther_df[panther_df[0].isin([g])]
go_df = goTerm_df[goTerm_df[0].isin([g])]
if len(sub_df) > 0:
#Save first result for gene/description
orthoGene += [list(sub_df.Gene)[0]]
orthoDescr += [list(sub_df.Description)[0]]
else:
orthoGene += ['NA']
orthoDescr += ['NA']
if len(panth_df) > 0:
#Save first result for gene/description
pantherNum += [list(panth_df[1])]
pantherDescr += [list(panth_df[2])]
else:
pantherNum += ['NA']
pantherDescr += ['NA']
if len(go_df) > 0:
#Save first result for gene/description
goTerms += [list(go_df[1])]
else:
goTerms += ['NA']
deseq_df['orthoGene'] = orthoGene
deseq_df['orthoDescr'] = orthoDescr
deseq_df['pantherID'] = pantherNum
deseq_df['pantherDescr'] = pantherDescr
deseq_df['goTerms'] = goTerms
deseq_df.head()
Out[ ]:
| Unnamed: 0 | Genes | Cluster | Condition | padj | padjClus | log2FC | orthoGene | orthoDescr | pantherID | pantherDescr | goTerms | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | XLOC_030861 | 0 | Starved | 9.125564e-14 | 3.102692e-12 | -1.641450 | SRSF1 | serine/arginine-rich splicing factor 1 isofor... | [PTHR23147:SF44] | [SERINE/ARGININE-RICH SPLICING FACTOR 1] | [nan] |
| 1 | 2 | XLOC_010635 | 0 | Starved | 5.121409e-13 | 1.741279e-11 | -1.382850 | SRSF1 | serine/arginine-rich splicing factor 1 isofor... | [PTHR23147:SF44] | [SERINE/ARGININE-RICH SPLICING FACTOR 1] | [nan] |
| 2 | 3 | XLOC_040775 | 0 | Starved | 1.881249e-11 | 6.396248e-10 | 1.093848 | PINX1 | PIN2/TERF1-interacting telomerase inhibitor 1... | [PTHR23149:SF27] | [PIN2/TERF1-INTERACTING TELOMERASE INHIBITOR 1] | [GO:0030234,GO:0004857,GO:0005515,GO:0003676,G... |
| 3 | 4 | XLOC_012879 | 0 | Starved | 9.571692e-11 | 3.254375e-09 | -1.921527 | NA | NA | [PTHR43056:SF5] | [ALPHA/BETA-HYDROLASES SUPERFAMILY PROTEIN] | [GO:0016787,GO:0044238,GO:0019538,GO:0006473,G... |
| 4 | 5 | XLOC_028699 | 0 | Starved | 1.657337e-10 | 5.634945e-09 | -1.099936 | NA | NA | NA | NA | NA |
In [ ]:
deseq_df.to_csv('deSeq2_deGenesDF_log2FCof1_singleCellReplicates_noShrinkage_subSample_annotations.csv')
In [ ]: